SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – “AI in Cyber Defense: The Rise of Self-Healing Systems for Threat Mitigation”

As cyber threats become more complex, traditional security controls have real challenges to stay in pace. AI-powered self-healing mechanisms are set to revolutionize cybersecurity with real-time threat detection, automated response, and self-healing by itself without human intervention. These machine-learning-based intelligent systems, behavioral analytics, and big data allow detection of vulnerabilities, disconnection from infected devices, and elimination of attacks while they are occurring. The shift to a proactive defense with AI-enabled cybersecurity solutions will reduce time to detect and respond to attacks and strengthen digital resilience. Forcing businesses and organizations to fight to keep pace with the fast-paced cyber threat landscape, self-healing AI systems have become a cornerstone of next-gen cyber defense mechanisms.

Introduction to Self-Healing Systems

Definition and Functionality of Self-Healing Cybersecurity Systems

In self-healing cybersecurity, an AI-based cyber security system determines, cuts off, and heals a cyber attack or security danger inflicted without the intervention or oversight of a human. Such systems utilize an automated recovery process to fix attacked networks with the least disturbance to restore normalcy. Unlike conventional security measures that require human operations, self-healing systems learn from experiences and detect and respond to dangers reactively and very efficiently.

Role of AI and Machine Learning in Detecting, Containing, and Remediating Cyber Threats

Artificial Intelligence and machine learning facilitate the cyber security-based technologies with self-healing abilities. An AI-enabled threat detection will analyze huge data wealth in real-time to spot anomalies, suspicious behaviors, and possible breaches in security. When a threat gets detected, ML algorithms analyze severity levels, triggering automated containment actions such as quarantining infected devices or blocking bad traffic. In AI-supported repair, self-healing measures are taken, where infected systems are automatically cleaned, healed, or rebuilt, hence shortening the time span of human intervention and damage caused by attacks.

How Big Data Analytics and Threat Intelligence Contribute to Self-Healing Capabilities

Processing of large data sets is a large concern for making autonomous cybersecurity systems more efficient by integrating real-time threat intelligence from multiple sources, including network logs, user behavior patterns, and global cyber threat databases. By processing and analyzing that data, self-healing systems may predict threats as they arise and provide proactive defense against cyberattacks. Continuous updates on emerging vectors of attack by threat intelligence feeds will enable AI models to learn and update security protocols on real time. The convergence of big data, artificial intelligence, and machine learning creates a robust and dynamic security platform, hence amplifying the efficiency of digital resilience.

Key Features of Self-Healing Systems

Self-healing cyber defense systems use artificial intelligence (AI) and automation to isolate and respond to threats as they surface and in real-time. They have the ability to react straight off, identifying and doing away with intruders in less than a millisecond. Autonomous intrusion detection employs machine learning and behavioral analysis to preemptively eradicate the chance of a successful cyber-attack. Self-healing capabilities enable a system to patch vulnerabilities, restore a breached network, and revive the security system without any human aid. These systems learn constantly in real-time and are therefore able to adapt to changing threats and enhance cyber resilience. Self-healing security solutions effectively protect organizations against sophisticated cybercrime and potential business disruption by lessening the load of human intervention and response times.

Advantages Over Traditional Cybersecurity Methods

AI-sustained self-healing systems enable instantaneous threat detection and responses to decrease the Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) to orders of magnitude below conventional cybersecurity practices.

Unlike reactive security, these systems pro-actively do live monitoring, predict, and neutralize threats before they can expand. They preclude reliance on human intervention, hence reducing errors and delays.

Self-healing systems learn and adapt to open-ended cyber threats, creating a long-standing extra-zero-day exploit, ransomware, and advanced persistent threat (APT) resilience. Automated threat mitigation and system recovery raise cybersecurity efficiency, scalability, and cost-effectiveness for the modern organization.

Challenges and Limitations

The self-healing cyber security solutions, despite understanding their benefits, pose serious challenges to integration, making it imperative to deploy and support AI-powered security systems with the specialist skills of professionals. The issue of false positives persists as automated responses can ascribe threats to actions that are though correct, putting business continuity in jeopardy. Compliance with international data protection legislation, such as the General Data Protection Regulation (GDPR) and the Family Educational Rights and Privacy Act (FERPA), is also a big hurdle for AI-assisted security in order to have strong privacy provisions. Compatibility with current legacy systems can be a roadblock to seamless adoption, forcing organizations to renew their superannuated infrastructure. Ethical issues on AI bias in threat detection should also receive due diligence so that fairness and accuracy in decision-making continue to receive encouragement in the field of cybersecurity.

Real-World Applications of Self-Healing Systems

Financial Institutions

AI-based self-healingcybersecurity enables banks and financial institutions to identify and block fraudulent transactions, breaches, and cyberattacks. With constant surveillance over financial transactions, AI detects anomalies to improve fraud detection and automate security controls, thereby decreasing financial losses and maintaining data integrity in the process.

Healthcare Industry

With the threats posed to patient data by cyber warfare on healthcare networks and hospitals, self-healing systems will be used in protecting patient data. These self-healing systems are built for searching for intrusions, isolating the affected parts of a system, and restored by an automated reset process to guarantee compliance with HIPAA and other healthcare regulations.

Government and Defense

National security agencies count on AI-based cybersecurity systems to protect sensitive data, deter cyber war and protect critical infrastructure. Autonomous self-healing AI systems respond to nation-state-sponsored cyberthreats and are able to react failure-point-to-failure-point around an attack’s continual adaptation while providing real-time protection against potential breaches or intrusions in the space around them.

Future Outlook

With someday ever-weaving variation of possible cyber attacks, therefore enhancing most probably probable requirement of AI self-healing cyber security systems. Futuristic advancements such as blockchain for enforcing secure data inter-exchange, quantum computing for championing encryption strength, and AI deception to falsify some attacker’s cognition. It will allow even the SOCs( Security Operation Centers) and add more autonomy, this much will further curtail human intervention and thus make the security proactive, scalable and able to thwart advanced persistent threats.

Conclusion

AI self-healing systems emerge as the next-generation of cyber defense models which will impersonate the real-time threat detection, execute the automated response, and conduct self-correction without human intervention. By utilizing machine learning, big data analytics, and self-adaptive AI, the accomplishment of these systems will be such that no one could dream of lessenedness of their efficacy in providing security and business continuity. As organizations become increasingly more susceptible to advanced cyber threats, self-healing cybersecurity will be key in future-proofing digital infrastructures and establishing cyber resilience.

References

1. https://www.xenonstack.com/blog/soc-systems-future-of-cybersecurity
2. https://fidelissecurity.com/threatgeek/threat-detection-response/future-of-cyber-defense/
3. https://smartdev.com/strategic-cyber-defense-leveraging-ai-to-anticipate-and-neutralize-modern-threats/

About the Authors:

Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University. He is an Adjunct Professor at Universidad del Externado, Colombia, a member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also a member of the Advisory Board at PUCPR, Brazil, Johannesburg Business School, SA, and Milpark Business School, South Africa, along with PetThinQ Inc, Upmore Global and SpaceBasic, Inc. His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum Artificial Intelligence.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

Der Beitrag AI in Cyber Defense: The Rise of Self-Healing Systems for Threat Mitigation erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Major AI Funding Shifts – SwissCognitive AI Investment Radar

The AI investment landscape continues to evolve, with new funding rounds and strategic commitments driving the industry forward. This week, key players across finance, technology, and infrastructure have made major moves to expand AI capabilities, focusing on both software and cloud expansion. Salesforce pledged $1 billion toward AI development in Singapore, while Honor committed $10 billion to integrating AI across its product line.

Investment priorities are shifting from AI chips to software, with analysts predicting that software firms will capture more value in the coming years. Microsoft is expanding its cloud and AI infrastructure in South Africa with a $298 million investment, reflecting the rising demand for AI-driven services. Meanwhile, Barclays analysts note that AI models are evolving from training-based systems to more advanced reasoning engines, signaling a new phase in AI capabilities.

DeepSeek’s breakthrough continues to drive activity in China’s venture capital sector, attracting fresh funding after years of stagnation. Elsewhere, private equity firms are adjusting their investment strategies to keep pace with AI-driven business transformations.

With AI playing a bigger role in stock markets, investor sentiment is shifting as automation takes on a larger role in financial decision-making. The rise of AI-powered fintech solutions, such as Finnomena’s partnership with Google Cloud, further highlights the increasing role of AI in investment strategies.

Stay tuned as we track these developments and more, bringing you the latest insights from the growing AI investment world.

Previous SwissCognitive AI Radar: $100B for AI Chips, $40B for AI Bets.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag Major AI Funding Shifts – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How Countries Are Using AI to Predict Crime”

A world where police departments can predict when, where and how crimes will occur seems like something from a science fiction novel. Thanks to artificial intelligence, it has become a reality. Already, countries are using this technology to forecast misconduct.

How Do AI-Powered Crime Prediction Systems Work?

Unlike regular prediction systems — which typically use hot spots to determine where and when future misconduct will be committed — AI can analyze information in real time. It may even be able to complete supplementary tasks like summarizing a 911 call, assigning a severity level to a crime in progress or using surveillance systems to tell where wanted criminals will be.

A machine learning model evolves as it processes new information. Initially, it might train to find hidden patterns in arrest records, police reports, criminal complaints or 911 calls. It may analyze the perpetrator’s demographic data or factor in the weather. The goal is to identify any common variable that humans are overlooking.

Whether the algorithm monitors surveillance camera footage or pours through arrest records, it compares historical and current data to make forecasts. For example, it may consider a person suspicious if they cover their face and wear baggy clothes on a warm night in a dark neighborhood because previous arrests match that profile.

Countries Are Developing AI Tools to Predict Crime

While these countries don’t currently have official AI prediction tools, various research groups and private police forces are developing solutions.

• United States

Violent and property crimes are huge issues in the United States. For reference, a burglary occurs every 13 seconds — almost five times per minute — causing an average of $2,200 in losses. Various state and local governments are experimenting with AI to minimize events like these.

One such machine learning model developed by data scientists from the University of Chicago uses publicly available information to produce output. It can forecast crime with approximately 90% accuracy up to one week in advance.

While the data came from eight major U.S. cities, it centered around Chicago. Unlike similar tools, this AI model didn’t depict misdemeanors and felonies as hot spots on a flat map. Instead, it considered cities’ complex layouts and social environments, including bus lines, street lights and walkways. It found hidden patterns using these previously overlooked factors.

• South Africa

Human trafficking is a massive problem in South Africa. For a time, one anti-human trafficking non-governmental organization was operating at one of the country’s busiest airports. After the group uncovered widespread corruption, their security clearance was revoked.

At this point, the group needed to lower its costs from $300 per intercept to $50 to align with funding and continue their efforts. Its members believed adopting AI would allow them to do that. With the right data, they could save more victims while keeping costs down.

Some Are Already Using AI Tools to Predict Crime

Governments have much more power, funding and data than nongovernmental organizations or research groups, so their solutions are more comprehensive.

• Japan

Japan has an AI-powered app called Crime Nabi. The tool — created by the startup Singular Perturbations Inc. — is at least 50% more effective than conventional methods. Local governments will use it for preventive patrols.

Once a police officer enters their destination in the app, it provides an efficient route that takes them through high-crime areas nearby. The system can update if they get directed elsewhere by emergency dispatch. By increasing their presence in dangerous neighborhoods, police officers actively discourage wrongdoing. Each patrol’s data is saved to improve future predictions.

Despite using massive amounts of demographic, location, weather and arrest data — which would normally be expensive and incredibly time-consuming — Crime Nabi processes faster than conventional computers at a lower cost.

• Argentina

Argentina’s Ministry of Security recently announced the Artificial Intelligence Applied to Security Unit, which will use a machine learning model to make forecasts. It will analyze historical data, scan social media, deploy facial recognition technology and process surveillance footage.

This AI-powered unit aims to catch wanted persons and identify suspicious activity. It will help streamline prevention and detection to accelerate investigation and prosecution. The Ministry of Security seeks to enable a faster and more precise police response.

• South Korea

A Korean research team from the Electronics and Telecommunications Research Institute developed an AI they call Dejaview. It analyzes closed-circuit television (CCTV) footage in real time and assesses statistics to detect signs of potential offenses.

Dejaview was designed for surveillance — algorithms can process enormous amounts of data extremely quickly, so this is a common use case. Now, its main job is to measure risk factors to forecast illegal activity.

The researchers will work with Korean police forces and local governments to tailor Dejaview for specific use cases or affected areas. It will mainly be integrated into CCTV systems to detect suspicious activity.

Is Using AI to Stop Crime Before It Occurs a Good Idea?

So-called predictive policing has its challenges. Critics like the National Association for the Advancement of Colored People argue it could increase racial biases in law enforcement, disproportionately affecting Black communities.

That said, using AI to uncover hidden patterns in arrest and police response records could reveal bias. Policy-makers could use these insights to address the root cause of systemic prejudice, ensuring fairness in the future.

Either way, there are still significant, unaddressed concerns about privacy. Various activists and human rights organizations say having a government-funded AI scan social media and monitor security cameras infringes on freedom.

What happens if this technology falls into the wrong hands? Will a corrupt leader use it to go after their political rivals or journalists who write unfavorable articles about them? Could a hacker sell petabytes of confidential crime data on the dark web?

Will More Countries Adopt These Predictive Solutions?

More countries will likely soon develop AI-powered prediction tools. The cat is out of the bag, so to speak. Whether they create apps exclusively for police officers or integrate a machine learning model into surveillance systems, this technology is here to stay and will likely continue to evolve.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How Countries Are Using AI to Predict Crime erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – AI & ML, Woxsen University – “AI As A Tool for Enhancing Wisdom: A Comparative Analysis”

Exploring Artificial Intelligence as a Tool for Enhancing Wisdom: A Comparative Analysis Using Webster’s SAWS Scale and Ardelt Scales

Well-informed decisions are guided by wisdom, which includes in-depth comprehension, emotional control, and critical thinking. AI has the capacity to improve human knowledge because of its capacity to analyze large amounts of data and provide insights. Three evaluation measures are used in this article to examine how AI might augment wisdom: the Ardelt Wisdom Scale, the Three-Dimensional Wisdom Scale (3D-WS) developed by Monika Ardelt, and the Self-Assessed Wisdom Scale (SAWS) developed by Webster. We hope to gain insight into how well AI aligns with the dimensions of wisdom by assessing its performance using these scales, identifying areas of strength and improvement, and providing guidance for future advancements in AI decision-making.

Webster’s Self-Assessed Wisdom Scale (SAWS)

Webster’s Self-Assessed Wisdom Scale (SAWS) measures wisdom across five dimensions: experience, emotional regulation, reminiscence and reflectiveness, openness, and humor [1]. Applying this scale to AI systems offers insights into how AI aligns with these facets. AI excels in the “experience” dimension by analyzing vast datasets to provide valuable insights. Its data-driven strategies support emotional regulation, while its ability to identify patterns in personal data fosters reflective thinking. AI also promotes openness by recommending new experiences and opportunities, encouraging individuals to broaden their horizons. Though limited in generating humor, AI curates humorous content, contributing to well-being and a balanced perspective.

By evaluating AI systems using the SAWS scale, we can assess how well AI supports these dimensions of wisdom. This analysis highlights AI’s strengths, such as its cognitive capabilities and potential to enhance emotional and reflective aspects of wisdom. It also identifies areas for improvement, guiding the development of AI systems that better align with the multifaceted nature of wisdom. Ultimately, understanding AI’s role in enhancing human wisdom can inform its integration into decision-making processes, promoting wiser and more informed choices.

Monika Ardelt –  Three-Dimensional Wisdom Scale (3D-WS)

The Three-Dimensional Wisdom Scale (3D-WS) breaks down wisdom into three key components: cognitive, reflective, and affective [2]. This multidimensional approach allows for a nuanced understanding of how AI can enhance different aspects of wisdom. In the cognitive domain, AI shines with its ability to process and analyze vast amounts of data, providing insights that help humans make informed decisions. Its analytical prowess complements human cognitive capabilities, enabling more effective problem-solving.

Reflective thinking, another crucial aspect of wisdom, is where AI can also offer significant benefits. AI encourages self-reflection by presenting diverse perspectives and prompting users to reconsider their beliefs and decisions. This helps individuals develop a deeper understanding of themselves and the world around them. On the affective front, while AI does not experience emotions, it supports emotional well-being by offering tools and resources for managing stress and fostering empathy. By addressing these three dimensions, AI has the potential to enrich human wisdom, guiding individuals toward more balanced and thoughtful decision-making.

Ardelt Wisdom Scale

The Ardelt Wisdom Scale measures wisdom through three interconnected dimensions: cognitive, reflective, and affective [2]. This holistic approach provides a comprehensive framework for assessing how AI can enhance wisdom. In the cognitive realm, AI’s ability to process and analyze large amounts of information aligns perfectly with this dimension. AI can offer insights and knowledge that help individuals understand complex issues and make more informed decisions, effectively complementing human intellect.

The reflective dimension of the Ardelt Wisdom Scale focuses on self-awareness and introspection. AI can significantly aid in this area by encouraging individuals to reflect on their past experiences and behaviors. By identifying patterns and providing feedback, AI helps users gain a deeper understanding of themselves, fostering personal growth. In the affective dimension, which involves empathy and emotional regulation, AI can provide support through tools and resources designed to help individuals manage their emotions and develop a more compassionate outlook. While AI itself doesn’t feel emotions, its ability to assist in emotional management can enhance overall well-being and empathy, contributing to a more balanced and wise approach to life.

Comparative Analysis

When we compare AI’s capabilities across the three wisdom scales: Webster’s SAWS, Monika Ardelt’s 3D-WS, and Ardelt’s Wisdom Scale we see a clear picture of how AI aligns with different aspects of wisdom. Each scale highlights AI’s strengths and potential areas for growth. In terms of cognitive abilities, all three scales recognize AI’s exceptional analytical and data-processing skills. This is where AI truly excels, offering comprehensive insights that can enhance human decision-making and problem-solving.

Reflectiveness is another area where AI shows promise. By encouraging individuals to reflect on their experiences and consider multiple perspectives, AI supports the development of deeper self-awareness and understanding. Both the Webster and Ardelt scales emphasize this reflective aspect, which AI can facilitate through data analysis and personalized feedback. However, the affective dimension presents more of a challenge. While AI can provide tools for emotional regulation and suggest strategies for managing emotions, its lack of true emotional experience means it can only indirectly support empathy and emotional intelligence.

From this comparative analysis we can understand that AI can significantly enhance cognitive and reflective aspects of wisdom, with some potential to aid in emotional well-being. This understanding guides the development of more holistic AI systems that better support human wisdom.

Implications for Decision-Making

AI’s integration into decision-making processes can lead to more informed and balanced choices. Its cognitive strengths provide deep insights and data-driven analysis, enhancing our understanding of complex issues. By encouraging reflective thinking, AI helps individuals consider diverse perspectives and learn from past experiences. Additionally, AI’s tools for emotional regulation support better emotional management, contributing to more thoughtful decisions. Overall, leveraging AI in decision-making can foster greater wisdom, leading to more ethical and effective outcomes in both personal and professional contexts.

Conclusion

AI has the potential to significantly enhance human wisdom by aligning with key dimensions of established wisdom scales. It excels in providing cognitive insights, encourages reflective thinking, and supports emotional regulation. While AI cannot fully replicate human emotional experiences, its tools and strategies can still contribute to emotional well-being. By integrating AI into decision-making processes, we can make more informed, balanced, and ethical choices. As AI continues to evolve, its role in augmenting human wisdom will likely grow, offering new opportunities for personal and professional development.

References:

• Webster, J.D. An Exploratory Analysis of a Self-Assessed Wisdom Scale. Journal of Adult Development 10, 13–22 (2003). https://doi.org/10.1023/A:1020782619051
• Ardelt, M. (2003). Empirical assessment of a three-dimensional wisdom scale. Research on Aging, 25(3), 275-324.

About the Authors:

Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University. He is an Adjunct Professor at Universidad del Externado, Colombia, a member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also a member of the Advisory Board at PUCPR, Brazil, Johannesburg Business School, SA, and Milpark Business School, South Africa, along with PetThinQ Inc, Upmore Global and SpaceBasic, Inc. His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum Artificial Intelligence.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

Der Beitrag AI As A Tool for Enhancing Wisdom: A Comparative Analysis erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How AI Will Impact 2024 Elections”

Generative artificial intelligence — models that can create images, videos, audio or text — have become incredibly popular because they’re widely available, easy to use and fast. Unfortunately, their greatest features are also threats. Will this technology permanently improve elections or unfairly sway the polls in one candidate’s favor?

AI’s Impact on Elections Is Global

2024 is a pivotal election year — not just for the United States but the world. Residents of over 50 countries will visit the polls this year alone, including Mexico, South Korea, the United Kingdom, India, South Africa, Taiwan and the European Union.

While most voters have come to expect — and know how to spot — attack ads, online trolling and misinformation around election time, AI has brought the world into uncharted waters. Generative models can create convincing images and videos with only one minute of audio or a few lines of text.

An AI-generated deepfake — real content that has been digitally manipulated with AI — is another massive concern. This technology replaces one person’s likeness or voice with a synthetic alternative.

According to one recent survey, about 78% of people believe bad actors will use AI to influence the U.S. presidential election outcome, with 70% thinking they’ll generate fake information and 62% assuming they’ll convince people not to vote.

AI’s Negative Impacts on Elections

There’s no downplaying AI’s negative impacts on elections.

Disinformation

Most people learn about candidates and current events from social media and internet headlines. In the United States, 82% of adults get their daily news from a digital device. This is an issue in an age where bad actors can create AI-generated disinformation almost instantly.

The Center for Countering Digital Hate, a British nonprofit, recently tested six of the leading AI voice cloning tools. Each produced fake audio snippets of high-profile politicians, with 80% of the tests generating a convincing clip.

Algorithmic Bias

AI systems can learn to make biased decisions if their training data contains skewed or inaccurate information or variables like gender, age, race or sexuality. Algorithms could act with prejudice if governments use this technology to accelerate vote counting or check voter eligibility.

Deepfakes

Divyendra Singh Jadoun is known as the “Indian Deepfaker” for his work on Bollywood clips and TV commercials. Recently, he claimed hundreds of Indian politicians sought his services ahead of the country’s elections, with 50% making unethical requests like defamation or deception. He says he denied them but doesn’t doubt others would accept their offers.

A deepfake can place a politician’s likeness over any body, face and voice to make it seem like they said or did something they never have. Politicians can — and have — used fake videos to make their opponents less likable. They even use AI on themselves to cast doubt on any real wrongdoings that might surface, giving them plausible deniability.

Fake Accounts

AI-powered social media bots spread misinformation and subconsciously influence voters by posting comments, sharing articles, and liking posts about certain politicians or upcoming elections.

Examples of AI Impacting Elections

AI’s effect on elections isn’t just hypothetical — it’s already happening. Of the 112 national elections in the United Kingdom between 2023 and 2024, 19 show signs of AI interference so far. When considering the evidence of AI-generated disinformation, that figure increases.

In Slovakia, days before the election — which was to determine who would lead the country — an audio clip of one of the leading candidates spread online. In it, he bragged about rigging the election. His opponent ended up defeating him.

In the United States, a former political consultant robocalled New Hampshire voters with an AI-generated voice meant to mimic President Biden, directing them not to vote. It reached thousands of people just ahead of the presidential primary. The man faces criminal and felony charges, along with a steep $6 million fine issued by the Federal Communications Commission.

Although the number of voters influenced in these situations remains unclear, one thing is certain — they were affected by AI interference. Going forward, cases like these aren’t going to be outliers. Instead, they may become as routine as attack ads and fake news posts.

AI’s Positive Impacts on Elections

It turns out AI might not be all bad — it still stands to positively impact the election.

Heightened Awareness

People aware of AI’s capabilities may be more likely to approach social media posts, news articles and viral clips with greater skepticism. Their newfound tendency to fact-check content can protect them from disinformation.

Election Administration

AI-powered systems could help administer elections, accelerating the time it takes to count votes, register voters or remind the general public of upcoming election dates. Considering these processes are typically so time-consuming, streamlining and automating them could be substantially beneficial.

Voter Education

Governments can offer AI tools to help voters stay informed. A machine learning model can pull up the latest news, fact-check social media posts, summarize news articles or identify AI-generated content.

AI-Generated Content Will Influence Elections

While rampant disinformation around election time isn’t new, it was obvious to those who could spot the telltale signs of Photoshop or traditional digital manipulation tactics. Now, generative models have muddied the waters. How can people tell what’s real anymore? What happens when politicians shrug off real scandals as some AI-generated hoax?

The question isn’t whether the AI’s positive impacts outweigh its negatives — it’s how to combat bad actors using this technology. Generative and machine-learning models are here to stay, so voters, governments and politicians should work together to figure out how to handle them. Swift, collaborative action may soon be the only thing ensuring fair elections.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Could Impact The 2024 Elections erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – AI & ML, Woxsen University – “Rethinking the Future of Singularity State with Critical Thinking”

In a time of swift technological progress, education has never had more opportunity to change. Generative and quantum AI present exciting opportunities for improving student learning outcomes and upending educational paradigms as traditional teaching approaches change. First, we explore the possible uses, advantages, and difficulties of incorporating generative and quantum artificial intelligence (AI) into educational environments, and we end up imagining a future in which these advances push education into new frontiers of brilliance and performance.

Understanding Generative AI

A branch of artificial intelligence called “generative AI” is concerned with producing new content—like literature, graphics, and even music—by using patterns discovered in previously collected data. It functions by producing an output that closely resembles the properties of the input data. Generative AI in education makes content generation, assessment automation, and personalized learning possible. For example, platforms like Google’s AutoML allow teachers to create personalized learning resources, while technologies like OpenAI’s GPT models may create educational materials suited to each student’s needs. These instances show how generative AI encourages creativity and adaptability in teaching methods.

Exploring Quantum AI

Using the ideas of quantum mechanics, quantum artificial intelligence (AI) is able to do calculations that are beyond the reach of classical AI. Quantum artificial intelligence (AI) uses quantum bits, or qubits, which are multi-state entities that can exist concurrently, as opposed to classical AI, which uses binary bits. This enables exponential efficiency in solving complicated issues for Quantum AI. Quantum AI has great potential in education for applications such as scheduling algorithm optimization, molecular structure simulation for chemistry lectures, and complex mathematical problem solving that beyond the capabilities of traditional computing. A greater knowledge of quantum principles in education is made possible, for instance, by IBM’s Quantum Experience platform, which provides instructors and students with opportunity to investigate quantum concepts and algorithms firsthand.

Revolutionizing Education: Case Studies and Examples

1. Real-world examples of educational institutions or initiatives leveraging Generative and Quantum AI

At the end of last year, MIT hosted a symposium as part of their “MIT Generative AI Week” to examine state-of-the-art generative AI initiatives being worked on by the academic institution. These projects include a mobile app that employs AI-assisted observational learning to enhance public speaking abilities and individualized educational chat tutors for quantum physics using generative AI. Another such is the University of Cambridge, which has been investigating how deep learning algorithms for educational applications—like more effective and precise language translation models—can be improved by using quantum computing.

1. Success stories of student performance enhancement through the integration of these technologies

The AI Research Center at Woxsen University in India has developed AI chatbots in the Metaverse for Management courses that help students grasp the material clearly and retain it for the rest of their lives. Students who utilized the chatbot to receive texts regarding assignments, academic support, and course content were more likely to receive a B grade or better. Georgia State University’s artificial intelligence-enhanced chatbot, named “Pounce,” has been shown to improve student performance in classes. Similar to this, at California State Polytechnic, Pomona, students are writing and participating better because of the usage of an AI-powered platform called Packback, which encourages critical thinking and deeper engagement with the course materials.

1. Challenges and limitations faced in implementing Generative and Quantum AI in education

Rather than merely creating technology-driven solutions, a major challenge is to match the development of AI tools and solutions with the changing requirements and complexity of the educational system. In addition to pointing out that technologists have historically found it difficult to create tools that properly meet the demands of educators and students, panelists at the MIT symposium emphasized the significance of comprehending the social and technical systems that comprise contemporary education. Furthermore, the search results indicate that in order to fully realize the potential of these cutting-edge technologies in the classroom, a fundamental rethinking of the educational model will be required, shifting away from traditional instructivist techniques and toward more constructionist, hands-on learning.

Future Implications and Possibilities

The future of learning is expected to be significantly impacted by the integration of Generative and Quantum AI in education as they develop further. The combination of these technologies creates new opportunities for tailored instruction, flexible learning environments, and data-driven understanding of students’ development. Furthermore, a paradigm shift in teaching approaches is predicted given the possibilities for complex problem-solving enabled by Quantum AI and immersive virtual environments powered by Generative AI. By adopting these innovations, educators may look forward to a time when education will be more dynamic, inclusive, and engaging, enabling students to succeed in a world that is getting more complicated and dynamic by the day.

Conclusion

The unparalleled opportunity to transform education is presented by the convergence of Quantum AI and Generative AI. Through the utilization of Generative AI for customized learning and content development, and Quantum AI for addressing intricate issues beyond standard computing, educational establishments have the opportunity to improve student learning results and challenge established ideas. The tangible advantages of these technologies are demonstrated by real-world examples, which range from enhanced student performance to personalized chat instructors. But issues like pedagogical changes and alignment with educational needs need to be addressed. Future learning experiences that are adaptable, immersive, and successful are promised by the integration of generative and quantum artificial intelligence (AI), equipping students for success in a world that is always changing.

About the Authors:

Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University. He is an Adjunct Professor at Universidad del Externado, Colombia, a member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also a member of the Advisory Board at PUCPR, Brazil, Johannesburg Business School, SA, and Milpark Business School, South Africa, along with PetThinQ Inc, Upmore Global and SpaceBasic, Inc. His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum Artificial Intelligence.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

Der Beitrag Reforming Education with Generative and Quantum AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Jacques Ludik, Founder & CEO, Cortex Logic & Cortex Group – “The Evolving Swiss AI Ecosystem”

Switzerland stands as a beacon of innovation and a leader in the global AI arena. The country’s educational institutions, such as ETH Zurich and EPFL, are not just academic strongholds but are powerhouses of AI research with international reach and influence (Switzerland Global Enterprise, 2022). They provide a steady flow of highly skilled talent, feeding into a dense and flourishing ecosystem that nurtures both startups (Reinhard, 2023) and tech giants like Google and IBM (Steiger and Fitzer, 2023) and making it an invaluable partner on the global stage. (Switzerland Global Enterprise, 2022).

In the realm of AI, Zurich epitomizes this Swiss excellence. Home to ETH Zurich, one of the world’s leading universities, Zurich is a hotbed for AI research and development. The precision and accuracy inherent in Swiss culture resonate deeply with the needs of AI, drawing professionals to a city that promises not only professional success but also personal well-being. The forthcoming establishment of the AI Institute in Zurich is a testament to Switzerland’s appeal as a hub for cutting-edge technology and research (Boston Dynamics AI Institute, 2023). This expansion promises to further Switzerland’s capabilities in robotics and AI, integrating the intellectual rigor of academia with the dynamism of corporate R&D. The Institute aims to attract the best talent and contribute to the robust European ecosystem of innovation and technology.

Swiss innovation is globally recognized, with the nation consistently securing top positions in various innovation rankings. This innovative strength is bolstered by a business-friendly landscape with pragmatic regulations that encourage tech advancements while maintaining economic and political stability (European Commission, 2021). The synergistic relationship between business and academia in Switzerland is practically unique in Europe, creating a pragmatic cooperation that accelerates the translation of research into practical solutions (ZHAW, 2023).

Geneva stands as a testament to Switzerland’s commitment to global ethical standards in AI. As home to numerous international organizations, it is a pivotal city for establishing global standards that guide the ethical development and deployment of AI technologies (European Commission, 2021). International Geneva, hosting significant global AI activities, adds to Switzerland’s status as a leader in AI governance and the ethical implementation of AI technologies (U.S. News, 2023). This is a testament to Switzerland’s conducive environment for international organizations and their efforts in cognitive technologies.

Switzerland has emerged as a premier destination for AI innovation, distinguished by its combination of a solid educational foundation, economic stability, and quality of life. The country’s innovative prowess, evidenced by its top position as the most innovative country worldwide, is a result of its world-class educational institutions and a high quality of life that attracts global talent. Switzerland’s commitment to excellence is reflected in its thriving economy and its role as a benchmark of global innovation and sustainable growth. (WEF, 2023).

Switzerland’s political and economic stability, coupled with its business-friendly and pragmatic regulations, creates an environment conducive to growth and innovation. This is evidenced by successful AI applications across various sectors, including medical imaging and expense management, as demonstrated by companies like Incepto Medical and Jenji. (Switzerland Global Enterprise, d.u.).

Switzerland and Africa

Looking at potential collaborations with South Africa and the broader African continent, Switzerland’s strengths align well with the growing technological landscape of Africa. Some recent dynamics in the Africa AI Ecosystem along for a call to collaborate to democratize Human-centric AI. For example, the Machine Intelligence Institute of Africa (MIIA) aims to develop a collaborative impactful African AI Ecosystem in collaboration with a global partner network of excellence that helps to transform Africa and shape a better future for all in the Smart Technology Era. By engaging with Switzerland’s AI centers valuable insights and support can be provided to African AI ventures and organizations. The cross-pollination of ideas and resources between Switzerland and Africa can lead to mutual growth and the realization of a human-centric AI future.

Switzerland’s approach to innovation can inspire and align with Africa’s goals, from fostering talent and pioneering research to nurturing a dense and flourishing ecosystem that supports startups and established businesses alike. This alignment offers a promising foundation for business and governmental collaborations, further strengthening the AI bridge between Switzerland and Africa.

Switzerland’s Start-up Ecosystem

Celebrating this spirit of innovation, the TOP 100 Swiss Start-up Award exemplifies the dynamism of Switzerland’s start-up ecosystem. The event spotlights startups that contribute significantly to the job market and the economy, underscoring Switzerland’s role as a cradle of entrepreneurial success and a magnet for international investment. (Reinhards, 2023). The country’s start-up ecosystem has been a fertile ground for innovation, creating thousands of jobs and significant revenue. HAYA Therapeutics, Planted Foods, and Yokoy Group are among the success stories emerging from this vibrant landscape.

Western Switzerland, meanwhile, stands as a beacon of AI research, hosting esteemed institutions like the Idiap Research Institute and platforms like CAIM and TORCH, which contribute to the country’s stature in the global AI landscape. Switzerland’s agility in adapting to regulatory changes while fostering innovation makes it a prime location for AI companies and thought leaders.

In the vibrant landscape of European AI, Switzerland commands attention with its 67 AI startups. (Smart, 2022). Remarkably, the nation’s AI sector is amplified by the contributions of ETH Zurich, which stands out as a premier institution fostering AI founders. This educational powerhouse underpins the nation’s AI prowess, distinguishing it even among larger countries with more startups. Such educational and research institutions are pivotal in advancing Switzerland’s capabilities in the AI domain.

Switzerland’s commitment to world-class research and innovation

Switzerland’s commitment to research and innovation is deeply entrenched in its national ethos, with substantial investments in R&D that eclipse the spending percentages of many other developed countries. The nation’s research institutions, such as CERN, the Paul Scherrer Institute, and the Friedrich Miescher Institute, are renowned for their significant contributions to scientific knowledge, particularly in the fields of engineering and biotechnology. Such investments bolster the Swiss economy and cement its reputation as a global leader in innovation. (Swiss Confederation)

Switzerland’s accolade as the best country in the world by U.S. News & World Report for the sixth time speaks volumes about its global standing. With assessments based on power, cultural influence, and heritage among 87 countries, Switzerland’s ranking reflects its economic stability, safety, and low corruption levels. The nation’s reputation is further solidified by its educational quality and innovative spirit, as noted by Ambassador Jacques Pitteloud. The recognition of its climate initiatives and economic prudence, alongside its top rankings, positions Switzerland at the forefront of global leadership. Moreover, Switzerland’s consistent ranking in the World Competitiveness Ranking, particularly in infrastructure and government efficiency, underscores its formidable position in the global economy and its conducive environment for business and innovation.

Switzerland’s commitment to research and development has cemented its status as a world leader in innovation. The active promotion of innovation by government and academic institutions has fostered a dynamic ecosystem that continually drives progress. (U.S. News, 2023)

Lastly, Switzerland’s dedication to the Sustainable Development Goals (SDGs) showcases its commitment to global sustainability. Its collaborative efforts, such as those by the Swiss chocolate industry, and its economic achievements, underscore the nation’s proactive role in international efforts to address global challenges. (Wharton University of Pennsylvania, 2022).

In summary, Switzerland’s prowess in research and development is not only a pillar of its own economy but also contributes significantly to global technological advancements. Swiss universities and research institutions serve as fertile ground for the world’s best minds to tackle the foremost challenges in AI and robotics.

“The Evolving Swiss AI Ecosystem” by Jacques Ludik has been published originally on Digital First Magazine.

Der Beitrag The Evolving Swiss AI Ecosystem erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – “Exploring the Cognitive Psychology of Consumer Behavior in the Age of Artificial Intelligence”

The idea of the metaverse has arisen as a dynamic area where virtual and real-world experiences meet at a time of fast technology progress. The potential to revolutionize education is becoming more and more clear as the lines between the physical and digital worlds converge. A core component of contemporary education, personalized learning, takes on new meaning in the metaverse. In this article, the revolutionary potential of AI-powered virtual tutors is explored, along with how these tutors are changing the face of education by personalizing instruction for each student. These instructors provide a look into a future where education transcends conventional boundaries, encouraging greater engagement and knowledge acquisition. They do this by utilizing the powers of artificial intelligence.

The Evolution of Education in the Metaverse

The boundaries of education are being redefined by the metaverse, an immersive digital realm. Interactive virtual learning environments replace traditional classrooms, allowing students to interact actively with their studies. Education crosses geographic boundaries as students explore lifelike simulations and collaborative settings. The metaverse supports current pedagogical trends by promoting active engagement and hands-on learning. Educators take use of its ability to engage students and impart knowledge by using virtual lectures, interactive experiments, and historical reconstructions. The metaverse’s growth of education represents an exhilarating move toward flexible, interesting, and learner-centered strategies that equip students for a world that is changing quickly.

The Role of AI in Personalized Learning

In the metaverse, customized learning is changing thanks in large part to artificial intelligence (AI). Artificial intelligence (AI) adapts instructional materials to each student’s particular speed, preferences, and learning style by utilizing machine learning and natural language processing. AI virtual tutors change the curriculum, provide real-time feedback, and pinpoint areas for development by evaluating data from individual encounters. With this proactive approach, comprehension and retention are maximized, resulting in a greater knowledge of the material. Personalized learning in the metaverse is becoming an increasingly effective tool for developing knowledge and critical thinking as AI develops and improves its capacity to offer nuanced, tailored advice.

Building the Ideal AI-Powered Virtual Tutor

It takes careful blending of technology innovation and pedagogical ideas to build the optimal AI-powered virtual teacher. User interface usability is a design factor that ensures easy navigation across the immersive environment of the metaverse. Customizability, which takes into account various learning preferences and styles, emerges as a major feature.

AI analytics-driven real-time assessment systems make it possible to continuously assess students’ development. This dynamic feedback loop improves understanding and reveals areas that need more investigation. The versatility of the virtual tutor enables it to readjust educational strategies, enhancing the learning process.

Empathetic AI is a key component of this design since it assesses emotional states and modifies interactions accordingly. The tutor’s programming incorporates ethical principles to prevent prejudices and advance diversity. Privacy protections also guarantee data security and foster trust.

A new age in education is about to begin when the immersive potential of the metaverse and AI’s cognitive brilliance come together. By creating the classic AI-powered virtual tutor, we revolutionize individualized learning in the metaverse by balancing technical innovation with educational efficacy.

Challenges and Considerations

There are several difficulties in integrating AI-powered virtual teachers into the metaverse. Data privacy, algorithmic prejudice, and the possible deterioration of the duties of human teachers all present ethical problems. Careful consideration is required to provide fair access across socioeconomic strata. It is crucial to take precautions against technical errors that disrupt continuous learning. A complex issue to be considered is how to balance AI’s effectiveness with individualized human contact. To overcome these obstacles, educators, technologists, and legislators must work together to develop a metaverse that supports inclusive, moral, and efficient tailored learning experiences.

Case Studies: Transformative Impact of AI-Powered Virtual Tutors

• The Georgia Institute of Technology unveiled Jill Watson, a virtual teaching assistant with AI capabilities, in 2016.
• AI-powered simulations are used at the INTERACTIVE building at Wharton University of Pennsylvania to create cutting-edge educational opportunities.
• The AI Research Centre at Woxsen University, Hyderabad, India implementing courses in metaverse platform to give interactive learning experience to the students and develops simulations for the Metaverse that are advantageous to management and engineering students.

The firm offers the following educational solutions:

• Palitt: Making it easier for teachers to create unique lecture series, syllabi, and textbooks.
• Cram101: Using artificial intelligence (AI) technology, every textbook can be turned into a smart study guide with chapter summaries, limitless practice exams, and targeted flashcards that are personalized to certain volumes, ISBN numbers, authors, and chapters.
• JustTheFacts101: Serving as the AI counterpart of a conventional yellow marker, it produces accurate book and chapter summaries rapidly while underlining key information.

These examples demonstrate how AI technology improves accessibility, comprehension, and engagement, making education more effective and inclusive. They highlight how the metaverse can democratize education and encourage educational institutions all across the world to use AI-powered virtual tutors to improve the quality of education in the future.

Future Prospects and Potential

A future overflowing with opportunities is revealed by the merger of AI-powered virtual teachers and the metaverse. Immersing students in experiencing worlds via the use of virtual reality (VR) and augmented reality (AR) technology might improve comprehension and retention. Global schools that cross boundaries and promote different cultural interactions could be made possible via collaborative metaverse environments. The development of AI may result in even more specialized personalization, with information that is tailored not just to learning preferences but also to emotional moods and cognitive requirements.

Additionally, the dynamic structure of the metaverse could make it possible for people to continue learning outside of the confines of traditional academic institutions, equipping them for a lifetime of discovery and development. AI-powered instructors may transform professional development as they advance in sophistication, guaranteeing the most current skills for a work market that is always evolving.

Conclusion

AI-powered virtual tutors serve as beacons of educational reform inside the metaverse in a society driven by innovation and connection. These instructors provide a preview of a day when education will be individualized, open to all, and unrestricted by geographical boundaries by customizing learning experiences to meet individual requirements. The ethical and fair integration of AI in education will be guided by cooperation between educators, technologists, and policymakers, despite challenges.

The metaverse becomes a canvas on which the art of learning is recreated as we set off on our educational voyage. The potential for a genuinely lifelong, learner-centric journey is unlocked by the symbiotic link between AI and the metaverse, which is set to transform conventional teaching. Students enter a world where learning is an exciting journey that develops brains and hearts in ways that go beyond the limits of time and space as they travel through immersive landscapes with the help of sympathetic AI partners.

About the Authors:

Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University. He is an Adjunct Professor at Universidad del Externado, Colombia, a member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also a member of the Advisory Board at PUCPR, Brazil, Johannesburg Business School, SA, and Milpark Business School, South Africa, along with PetThinQ Inc, Upmore Global and SpaceBasic, Inc. His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum AI.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

Der Beitrag AI-Powered Virtual Tutors: Personalized Learning in the Metaverse erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Gregg Barrett, Chief Executive Officer, Cirrus – “A Sound of Hope for South Africa in the Fight Against Trafficking with AI”

The release of the movie, the Sound of Freedom, while not representative of all the facets of current day human trafficking, has helped initiate broader conversations about modern exploitation. For many who watch the movie the feeling will be that doing something is better than doing nothing. But what can be done that will actually help? I was presented with this question in 2021 by an anti-human trafficking (AHT) NGO operating in South Africa and internationally. Prior, the organisation had been operating at one of South Africa’s busiest airports, and in the course of their work had uncovered systemic corruption and organised crime involved in trafficking at the airport. As a result, their security clearance had been revoked preventing them from operating in the airport. Their cost per intercept was around 300 USD which needed to be reduced to around 50 USD to align with funding. Astutely they believed that turning to the adoption of artificial intelligence (AI) methods would allow them to increase the efficiency and effectiveness of their work, and that this could be scaled without a resulting scaling in cost and headcount. Could there be a greater application of AI than that which results in the saving of lives? [1]

From an institutional governance standpoint, South Africa’s law enforcement institutions are not resourced to the same extent as many of those in the developed western world. [2] Under such conditions trafficking thrives, elevating the role of civil society organisations. However, as with law enforcement, these civil society organisations are not equipped with the optimal tools to enable the application of AI in the fight against trafficking. In understanding the scope and nature of human trafficking in South Africa, the United States Agency for International Development released a first report from a larger authoritative study. Five key points from the report are instructive:

• Human trafficking is indeed a serious, pervasive, and systemic problem in South Africa, that seamlessly intersperses with other crimes and social phenomena — including gender-based violence, prostitution, organised crime, missing persons, irregular migration, child abuse and labour disputes, to name a few.
• South Africa is not nearly equipped or co-ordinated enough to deal with this crime as effectively as it should or could, and enabling factors such as corruption, complicity, and compromise of officials and other AHT role players is a constant stark background to AHT efforts.
• There is poor record keeping, inaccessibility of official trafficking data, and the absence of an integrated information system required to collate and analyse specific information.
• A lack of proactive investigations and intelligence sharing, and a largely inactive national AHT task team, means that evidence of South Africa as a transit country is not proactively pursued with international counterparts.
• Citing a 2019 multi-country report by the UN, the report says South Africa is a “main destination” for smuggled and trafficked persons on the African continent. According to that 2019 report: “Most Africans see South Africa as the easiest country of transit to reach Europe or the Americas”, and it is “an origin and transit country for trafficking towards Europe and North America, and for trafficking and smuggling to and from Latin America and Asia”.

Returning to 2021 in my engagements with the NGO, the first step became obvious. We needed to focus on the data. Data is the necessary input for the training of AI models and their deployment (inference). In North America for example, Polaris operates the U.S. National Human Trafficking Hotline. Through that work, they have built the largest known dataset on human trafficking in North America, with the data informing real time strategies. Underpinning Polaris is what is known as a data management platform. It is this nontrivial piece of technology that effectively enables the application of AI to AHT.

At a high level a data management platform provides the capability needed to store, manage, share, find and use data for AI. A database is not a data management platform. Rather a data management platform is required for a single point of data ingestion. As the name implies this is to ingest data in all its forms (structured, unstructured, and semi-structured), and with all key data transfer approaches (batch, micro-batch, and streaming). To do this the data management platform provides a highly configurable set of data integration tools that extend far beyond typical extract-transform-load (ETL) or extract-load-transform (ELT) solutions.

In the context of AHT the data management platform provides the operational fabric that enables the access control framework to restrict access to sensitive information at a granular level, ensuring that analysts see only the specific data points that are necessary to complete their work. This ensures that data is being used effectively to have a positive impact, while protecting the privacy of individuals. The platform also enables detailed privacy impact assessments to codify the risks of the data used and the development of mitigation plans.

Beyond this the existence of a data management platform more fully enables the utilisation of mobility data, a financial intelligence unit, a research hub, and philanthropic engineering support. Mobility data is needed to generate heatmaps to identify choke points for placing new stations with human monitors to intercept trafficked persons. The financial intelligence units’ purpose is to train financial services and anti-money laundering staff on the data management platform itself, and allows these professionals to share knowledge, information, and best practices in real-time. In addition, this unit is intended to help survivors of human trafficking get access to banking services that they would not otherwise qualify for because of poor credit and other issues related to their trafficking experience. The research hub is there to bolster the data science / AI capabilities of the AHT organisations by establishing collaborations with academia and industry. This includes a data facility to provide key data to researchers, academics, law enforcement officers and others seeking to deepen knowledge and understanding in the fight against trafficking. Lastly, philanthropic engineering support is intimately connected to a commercial operation, and develops deep, hands-on, and often long-term relationships with NGOs and social sector organisations. This includes on-site engineering to develop foundational understanding of the respective fields enabling the establishment of broader collaborations of players to work on solving social problems. In the context of AHT, philanthropic engineering support provides the capability to drive on-the-ground action with partners that: have the capacity to utilise data management and AI for maximum impact; with organisations that have data and develop data sources; will benefit from AI analysis of the data; and have staff in place to act on the insights to drive action. Importantly, while philanthropic engineering support directly improves and save lives, the engagements also have a powerful impact internally, helping to attract, retain, and engage employees. The challenges being tackled require world class engineers and in South Africa there is a dire need to create opportunities to cultivate this talent – to offer meaningful work and the chance to make a difference.

Yet, here in South Africa a Polaris like data management platform has not been in sight. Enter anti-money laundering (AML). As with human trafficking, South Africa is plagued by money laundering. In February the country was greylisted by global financial crime watchdog the Financial Action Task Force (FATF) for not fully complying with international standards around the prevention of money laundering, terrorist financing and proliferation financing. Presently, each bank in South Africa undertakes AML mostly in isolation. A recent Bank for International Settlements project confirmed that collaborative analysis and learning approaches were more effective in detecting money laundering networks than the current siloed approach in which financial institutions carry out analysis in isolation. By the numbers, a LexisNexis report found South Africa’s largest banks spent on average 12.3 million USD in financial crime compliance operations in 2021. Further, the total projected cost of financial crime compliance in South Africa increased by 65% between 2019 and 2021, from $2.3 billion USD to $3.8 billion USD.

Contrasting deficiencies in human trafficking and money laundering show significant overlaps. This is unsurprising given that both fundamentally involve intelligence gathering, requiring the very same technological toolsets. Unquestionably, AI is the future for AML and AHT, and South Africa requires an industry wide approach – an industry wide data management platform. Just such an intervention has been initiated, where the intention is that the data management platform and its related operations for AML will be placed into a shared entity co-owned by industry. There is precedent for such. The country’s banks collaborate and co-own Bankserv, the automated clearing house. For South Africa’s largest banks an industry wide AML platform will result in significantly enhanced AML capabilities and cost reductions to around one third of their current financial crime compliance spend. For the country, eventual removal from the FATF greylisting. And for South Africa’s AHT organisations, the provision of a world class platform enabling the application of AI to fight trafficking.

In summary, out of South Africa’s struggles arises an opportunity for the country to be a global leader in the fight against trafficking and money laundering. Technological interventions like this do not come to pass by chance but through leadership. For what is it if we have these technological capabilities but fail to implement? Do we not have an ethical obligation? We must be pragmatic and assume that based on experience Government leadership is unlikely to materialise, and be optimistic that there are leaders in industry with the vision and fortitude to pursue this endeavour that will ultimately save lives. That there are those who are motivated in their hearts and minds to actually do something that will make a difference.

[1]

Ways in which long-range research in AI could be applied to the fight against human trafficking, see:

Bliss, N. et al.  (2021).  CCC/Code 8.7: Applying AI in the Fight Against Modern Slavery.  https://arxiv.org/abs/2106.13186

An algorithm to identify similarities across escort ads, making it easier for law enforcement to identify human traffickers, see: Carnegie Mellon University.  (2021).  Algorithm Uses Online Ads To Identify Human Traffickers.  https://www.ml.cmu.edu/news/news-archive/2021-2025/2021/april/machine-learning-ai-algorithm-uses-online-ads-identify-human-traffickers.html

Algorithms to extract signatures in images, such as specific tattoo designs linked to human trafficking networks, see: MIT News.  (2021).  Turning technology against human traffickers.  https://news.mit.edu/2021/turning-technology-against-human-traffickers-0506

Using AI to reveal trends in payments and help identify victims of modern slavery, see: World Economic Forum.  (2020).  How AI can help combat slavery and free 40 million victims.  https://www.weforum.org/agenda/2020/11/how-ai-can-help-combat-modern-slavery/

Examples of the work done by the Stanford Human Trafficking Data Lab to combat human trafficking, see: Stanford University.  (2021).  Melding Artificial Intelligence and Algorithms with Health Care and Policy to Combat Human Trafficking.  https://fsi.stanford.edu/news/melding-ai-and-algorithms-health-care-and-policy-combat-human-trafficking

[2]

For an index of governance in Africa see: Mo Ibrahim Foundation.  (2020).  2020 Ibrahim Index of African Governance: Key Findings.  https://mo.ibrahim.foundation/news/2020/2020-ibrahim-index-african-governance-key-findings

For a global comparison across various governance indicators see: The Worldwide Governance Indicators project.  (2020).  Worldwide Governance Indicators.  https://info.worldbank.org/governance/wgi/

With an average score of 32, Sub-Saharan Africa is the lowest performing region on the Transparency International Corruption Perceptions Index, see: Transparency International.  (2021).  CPI 2020: Sub-Saharan Africa.  https://www.transparency.org/en/news/cpi-2020-sub-saharan-africa

About the Author:

Gregg Barrett is a seasoned executive with extensive and diverse experience in strategy, building and managing relationships, deal-making, communication, developing high-performance teams, organisational leadership, and problem-solving across a range of areas. Over the last decade, Gregg has led work in data science, machine learning, corporate research, and corporate venture capital. This includes the establishment and management of data science, machine learning, corporate research, and corporate venture capital operations, working across people, processes, and technology, integrating structured and unstructured data to direct research, business, and investment strategy.

Der Beitrag A Sound of Hope for South Africa in the Fight Against Trafficking with AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – “Exploring the Cognitive Psychology of Consumer Behavior in the Age of Artificial Intelligence”

Consumer behavior is undergoing significant transformations due to the rapid advancement of technology, particularly artificial intelligence (AI). The integration of AI into various aspects of consumers’ lives has revolutionized their interactions with products, services, and brands. Understanding the cognitive psychology behind consumer behavior in the age of AI is imperative. This article explores the theoretical foundations of cognitive psychology and its relevance to consumer behavior. It analyses how AI influences decision-making, personalization, trust, and ethical considerations and aims to contribute to ethical guidelines and interdisciplinary collaborations, ensuring a responsible integration of AI that enhances the consumer experience and respects individual autonomy.

The Rise of Artificial Intelligence in Consumer Decision Making

Artificial intelligence (AI) has transformed consumer decision-making through personalized recommendations, chatbots, voice recognition, and smart devices. AI-powered recommendation systems analyze consumer data, generating personalized suggestions and enhancing engagement. Chatbots automate customer service, improving convenience and responsiveness. Voice-enabled AI assistants streamline interactions through natural language processing. Smart devices collect and analyze data for personalized experiences and automation. AI’s rise in consumer decision making has positive aspects, such as convenience and personalization, but raises concerns about privacy, security, and biases. Understanding AI’s impact on decision making helps businesses tailor strategies and design ethical AI systems aligned with consumer needs. By recognizing the cognitive processes involved, companies can create meaningful interactions that enhance the consumer experience while addressing ethical considerations.

Cognitive Biases and AI’s Impact on Decision Making

Cognitive biases significantly shape consumer decision making, and the integration of artificial intelligence (AI) introduces new dynamics that can amplify or mitigate these biases. AI’s impact on cognitive biases includes personalized recommendations reinforcing confirmation bias, pricing algorithms exploiting anchoring biases, and social proof amplification through AI-driven platforms. Mitigating cognitive biases with AI involves providing a wider range of information to counteract availability heuristic, implementing transparent algorithms to address biases, and educating consumers about cognitive biases. Responsible AI implementation promotes transparency, fairness, and consumer welfare. Understanding the interplay between cognitive biases and AI enables marketers to design systems that minimize biases and enhance decision-making, providing a more balanced consumer experience. Consumer education empowers individuals to make rational choices in the AI age.

Personalization and Emotional Engagement

AI has transformed personalization in consumer behavior, using cognitive psychology principles to create emotionally engaging experiences. AI enables data-driven, contextual, and predictive personalization. It analyzes consumer emotions, optimizes design elements, and personalizes storytelling. Emotional engagement through personalization enhances the consumer experience, improves memory and recall, and fosters word-of-mouth and brand advocacy. However, ethical considerations arise, including privacy, manipulation, and emotional well-being. Marketers must balance personalization, emotional engagement, and ethics by leveraging AI responsibly, ensuring transparency, consumer control, and informed consent. This approach creates personalized experiences that resonate with consumers, foster emotional connections, and build lasting relationships while prioritizing consumer well-being.

The Role of Trust and Explainability in AI-Driven Consumer Behavior

Trust and explainability are crucial for the adoption of AI technologies in consumer behavior. Trust in AI systems is influenced by factors such as reliability, accuracy, and security. Algorithmic transparency enhances trust by reducing uncertainty and increasing fairness. Trust can be built through proactive communication, accountability, and user control. Consumers desire explanations for AI-generated outcomes to make informed choices and maintain a sense of control. They expect explanations regarding biases and fairness in AI algorithms. Methods like interpretable machine learning algorithms can provide transparent AI outputs. Businesses should prioritize user-centric design, transparency, and ethical guidelines. Consumer education about AI empowers informed decision-making and fosters trust. Building trust and ensuring explainability in AI-driven consumer behavior improves the consumer experience and fosters long-term relationships. Businesses should develop transparent AI systems, provide understandable explanations, and adhere to ethical guidelines. By doing so, they can enhance consumer trust, address biases and fairness concerns, and create an environment where AI is seen as a reliable tool that enhances consumer well-being and satisfaction.

Ethical Considerations and Consumer Perceptions

The integration of AI in consumer behavior raises ethical concerns impacting consumer perceptions. Personal data collection and use raise privacy concerns, necessitating informed consent and data security. Manipulative techniques, such as dark patterns and persuasive personalization, raise questions about autonomy and biases. Lack of transparency in AI algorithms erodes trust, while explainable AI and accountability address ethical concerns. Consumer perceptions are shaped by trust, privacy, and empowerment. Businesses must address ethical considerations by embedding transparency, accountability, and data protection into AI systems. Open dialogues and consumer feedback help shape ethical AI practices. Public awareness and education promote informed choices. Ethical considerations are pivotal in shaping consumer perceptions and attitudes toward AI-driven consumer behavior. Prioritizing ethics, transparency, and consumer empowerment builds trust and ensures responsible AI integration.

The Future of AI and Consumer Behavior

The future of AI continues to reshape consumer behavior. Advancements in AI technology will provide enhanced personalization, improved natural language processing, and immersive AR/VR experiences. Ethical guidelines and regulatory frameworks will ensure responsible AI deployment, empowering consumers through awareness and education. Balancing automation and the human touch will lead to hybrid models and emotionally intelligent AI systems. Collaboration between psychology and AI, as well as integration with ethics and social sciences, will inform AI development. The future holds potential for enhanced consumer experiences, innovation, and ethical integration of AI. Ethical considerations, transparency, and consumer trust are vital. Collaboration among stakeholders is key in navigating challenges and opportunities. By prioritizing ethics, transparency, and consumer empowerment, businesses can utilize AI to create engaging, personalized, and ethical experiences that enhance consumer well-being and drive sustainable growth.

Conclusion

The integration of AI into consumer behavior has ushered in a new era of personalized experiences, decision-making processes, and brand interactions. Understanding the cognitive processes underlying consumer behavior allows businesses to create emotionally resonant experiences. Ethical considerations and consumer perceptions are crucial for responsible AI integration, addressing privacy, transparency, biases, and manipulation. The future of AI and consumer behavior holds immense potential through advancements in technology, ethical guidelines, and interdisciplinary collaborations. Balancing automation, recognizing emotions, and empowering consumers are key. Prioritizing consumer well-being, privacy, and fairness in AI design, deployment, and regulation is vital. By incorporating cognitive psychology principles, ethical practices, and meaningful collaborations, AI can enhance consumer experiences while respecting autonomy and societal values. Understanding cognitive psychology in the age of AI is essential for businesses, researchers, and policymakers. Embracing opportunities and fostering ethical integration shapes a future where AI-driven consumer behavior enriches lives, fosters connections, and empowers individuals in the digital age.

About the Authors:

Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University. He is an Adjunct Professor at Universidad del Externado, Colombia, a member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also a member of the Advisory Board at PUCPR, Brazil, Johannesburg Business School, SA, and Milpark Business School, South Africa, along with PetThinQ Inc, Upmore Global and SpaceBasic, Inc. His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum Artificial Intelligence.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

Der Beitrag Exploring the Cognitive Psychology of Consumer Behavior in the Age of Artificial Intelligence erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.