Copyright: verdict.co.uk – “The Year Of AI: 2024 Predictions”

Artificial intelligence (AI) became the most talked about technology theme in 2023. Businesses across every sector started to explore how AI, and more specifically generative AI, could drive greater growth and efficiency. The dizzying speed of large language model development has caught some companies by surprise while it has given others a first-mover advantage. With AI now firmly in the line of sight of any board-level executive, Verdict asked a cross section of industry leaders about their AI predictions for 2024.

Chris Sharp, CTO, Digital Realty

“In 2023 AI went mainstream – 2024 will see AI completely transform the way we live and work. AI’s impact will increase exponentially as businesses begin to embed multi-modal generative AI tools. AI workloads will become more demanding, and there will be a greater need for the infrastructure that houses these applications and enables their smooth operation: data centers. They will have to flex to respond to these changing demands – being agile, adaptable, and providing customers with options that allow them to scale or shift direction to make the most of the opportunities AI offers, will be critical.”

Zahra Bahrololoumi, CEO, Salesforce UK & Ireland

“AI innovation is accelerating and unlocking new opportunities for customers and citizens. We need a skilled workforce with the right training and guardrails to ensure this technology is rolled out responsibly, and effectively. Yet, we know there is a digital skills gap that needs to be closed. This is particularly acute in the UK where over a third (38%) of workers are already using or planning to use generative AI at work but most (62%) say they lack skills to do so effectively and safely. This needs to change.

It will take investment and close collaboration between all stakeholders. That’s why we’re urging the UK government to establish a national online digital skills platform. In 2024, organisations all over the world need to commit to upskilling people in AI and digital skills to make sure we don’t create a new digital divide.”

Sridhar Ramaswamy, SVP of AI, Snowflake

“Generative AI’s negative impacts will be hard to manage early on including job loss, deep fakes, and a deepening digital divide. Although generative AI is reimagining how we interact with machines, there are some immediate concerns that will be particularly challenging in the early years of widespread AI and language model adoption. For a lot of people involved in what we loosely call “knowledge work,” quite a few of their jobs are going to vaporise. Rapid change makes it hard to quickly absorb displaced workers elsewhere in the workforce, and as a result both the private sector and governments will need to step up.

“Deep fakes are also another hurdle and we can expect increased attacks on what we humans collectively think of as our reality — resulting in a world where no one can, or should, trust a video of you because it may be AI-generated. Finally, advances in AI will exacerbate the digital divide that has been happening over the past 20-30 years between the haves and have nots, and will further increase inequality across the globe. I can only hope that by making information more accessible, this emerging technology leads to a new generation of young adults who better understand the issues and potential, and can counter that risk.”[…]

Read more: www.verdict.co.uk

Der Beitrag The Year Of AI: 2024 Predictions erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger:  HennyGe Wichers, PhD – “AI Is Huge – And So Is Its Energy Consumption”

Replacing every Google search with an LLM interaction uses as much electricity as Ireland, Alex de Vries writes in a commentary published in the journal Joule on October 10, 2023. The PhD candidate at Vrije Universiteit Amsterdam is raising concerns about the environmental impact of artificial intelligence.

Large models like ChatGPT (OpenAI), Bard (Google), and Claude (Anthropic) consume a lot of energy. Take, for example, ChatGPT. The chatbot is built on a model called GPT-3, which used an estimated 1,287 MWh in electricity during its training phase. According to the chatbot, that’s the equivalent of driving a Tesla Model 3 around the Earth’s equator 21,500 times.

ChatGPT puts 1,287 MWh in perspective

ChatGPT suggested using household energy consumption for scale first. It was a good idea, but its answer was clearly wrong. It doesn’t make sense that 1,287 MWh powers either 1,468 American homes for a year or 122 for a month. The machine politely apologised when I pointed this out.

That’s nice, but I no longer trusted the AI and had to verify its second suggestion – with a Google search. For now, LLMs are probably increasing rather than replacing traditional search traffic. But I digress.

It’s well-known that training an LLM uses a lot of energy, yet that’s only the first step in the process. The second step is inference, a phase few of us have heard of, but many participated in.

For ChatGPT, inference began when it was launched. It continued to learn while interacting with the public and creating live responses to user queries. The chatbot’s estimated energy consumption during this phase was 564 MWh daily. That’s close to half the electricity consumed in training (44%) – but used every single day.

“Looking at the growing demand for AI service, it’s very likely that energy consumption related to AI will significantly increase in the coming years,” de Vries commented in an interview.

ChatGPT exploded as soon as it launched, registering an incredible 100 million users in just 2 months and igniting a chain reaction of artificial intelligence products. Unfortunately, we can’t skip the inference phase for new AIs because, without it, the machine would not have the ability to learn.

From 2019 to 2021, a whopping 60% of Google’s AI-related energy bill was for inference. But progress is being made. Hugging Face, a relative newcomer founded in 2016, developed the open-source alternative BLOOM using significantly less energy in the inference phase relative to the training phase.

Let’s take a look at the energy consumption for a single user request. Comparing the different methods, the graph looks as follows.

Fig 1: Energy consumption per user request (replicated from Joule)

The last two bars show estimations for AI-powered Google Search by two independent research firms, New Street Research and SemiAnalysis. It’s very costly at 20 to 30 times the usage of regular Google Search. That’s not an immediate problem, however, because NVIDIA can’t supply the hardware required.

HARDWARE CONSTRAINTS

Google would need 512,821 of NVIDIA’s AI servers to make every search an LLM interaction. That’s more than 5 times the company’s production for 2023, when it’s expected to deliver around 100,000 servers. The gap is enormous. Moreover, NVIDIA has around 95% market share, so no alternative supplier exists today.

Chips are an issue, too. NVIDIA’s chip supplier, TSCM, is struggling to expand its chip-on-wafer-on-substrate (CoWoS) packaging technology, which is essential for the chips NVIDIA needs. TSCM is investing in a new plant, but it will only begin to produce volumes in 2027. By then, NVIDIA could have demand for 1.5 million of its AI servers.

Hardware will remain a bottleneck for several more years. Still, without hardware constraints, we’d encounter problems further upstream. Building permissions for data centres take time, and construction does, too. Not to mention, the energy grid needs to expand to deliver the electricity required to run and cool them.

INNOVATION

But these constraints will drive innovation. We will find more efficient models and ways to operationalise them. A breakthrough in quantum computing could change everything, both for the supply and demand for AI.

De Vries points out, “The result of making these tools more efficient and accessible can be that we just allow more applications of it and more people to use it.”

He’s referring to Jevons’ Paradox, which occurs when an increase in efficiency causes costs to fall and demand to increase to the point where we use the tools more than we would have without the improvement.

The paradox was formulated in 1865 and has been observed many times since. LED lighting is a nice example: running LEDs is so cheap that we’ve covered the planet with them and now use more electricity for lights than ever before. If that is hard to imagine, just take a look at Sphere in Las Vegas.

Link to the YouTube video.

How amazing is that? But we wouldn’t have built it if innovation hadn’t graduated to LEDs. AI might follow a similar path. Nevertheless, without game-changing innovation to reduce energy use and ensure sustainable supply, we must think twice.

“The potential growth highlights that we need to be very mindful about what we use AI for. It’s energy-intensive, so we don’t want to put it in all kinds of things where we don’t actually need it,” de Vries offers as a parting thought.

Still, the very AI that poses the risk may also help us solve climate change and sustainability challenges.

Source: Joule via EurekAlert!

About the Author:

HennyGe Wichers is a technology science writer and reporter. For her PhD, she researched misinformation in social networks. She now writes more broadly about artificial intelligence and its social impacts.

Der Beitrag AI Is Huge – And So Is Its Energy Consumption erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: siliconrepublic.com – “How to Use Big Data to Create Sustainable Cities of the Future” Image: © SeanPavonePhoto/Stock.adobe.com

Currently, about 56pc of the global population live in cities. This is expected to rise to more than 70pc by 2050.

According to the World Economic Forum, cities cover just 2pc of the world’s land surface yet 75pc of the earth’s material resources are consumed within their boundaries. With the future expansion of urban areas, material consumption is expected to grow also.

In light of this rapidly changing demographic, Dr Marguerite Nyhan’s research is especially timely. Nyhan, who is an associate professor in environmental engineering at University College Cork (UCC), uses new and emerging tech “to study and predict interactions between human populations, urban systems and the environment” with the goal of advancing the science of “sustainable, net-zero, healthy and liveable future cities”.

‘Sensors and data from sensors are transforming urban life’

Speaking to SiliconRepublic.com, Nyhan explains that her Environmental Intelligence project, which is funded by the Science Foundation Ireland, “harness[es] state-of-the-art technologies, including sensors, largescale digital datasets, artificial intelligence and digital twins” to understand “the dynamics of urban populations and transport systems, the changing patterns of emissions and air pollution, and the varying distributions of green space” in several cities in Ireland and the US.

Nyhan describes this research as “unprecedented” in its “spatiotemporal resolution and scale” as it encompasses “entire cities or megacities”.

‘Analysable and predictable’

In a TED Talk she gave a few years ago, Nyhan said that “sensors and data from sensors are transforming urban life”. She used the example of the famous scramble crossing in Shibuya, Tokyo as seeming to be a chaotic mass but in fact “much of this movement is analysable and predictable”.

Most of the people in this scene have at least one, if not many, mobile, connected devices which allows their movements to be tracked and analysed. By using this data to develop mathematical models which describe the principles underlying human mobility, among many other data points, Nyhan aims to understand cities, how we use them and how we can improve them.[…]

Read more: www.siliconrepublic.com

Der Beitrag How to Use Big Data to Create Sustainable Cities of the Future erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Marco Lardelli, MSc. ETH

Everybody knows that artificial intelligence is currently developing at unprecedented speed. But it is still worth looking at some numbers to get an intuition for what’s going on. OpenAI published an analysis in 2018, showing that the amount of compute used in the largest AI training runs has been increasing exponentially with a doubling time of just 3.4 months (!). We all know that the famous Moore’s Law, which described the incredible development speed of electronics and computer science very well in the past, assumes a doubling period of about two years. Let’s look at some concrete numbers to understand the mathematical implications of this better: Between 2012 and 2018, the computing power used in leading A.I. projects has increased by a factor of more than 300’000! Moore’s law would have predicted „only“ a 7 fold increase in the same time period. But this is not all! We should not only look at the number of calculations executed but also at the efficiency of these calculations. Another publication from OpenAI from 2020 shows that the algorithmic efficiency doubles about every 16 months. This means that the amount of calculations required to achieve a certain result is decreasing very fast as well.

The two improvements add up and we therefore have to be prepared for simply mind-boggling A.I. advances in the coming years. We can safely say that we are experiencing an AI explosion right now!

It is clear that such an extremely fast technological progress will bring enormous economic, social and cultural challenges for society. AI might be the last and most important invention of humankind. Of course, such a development needs to be guided. We have to make sure that the huge potential of this invention is used to improve the living conditions of humans and not, for instance, to create intelligent autonomous weapons. In a democracy, the people are ultimately holding the steering wheel. To be able to make smart decisions they must understand what we are dealing with. Unfortunately, we are far away from such conditions right now. If I tell people about my AI projects, they often ask me questions like „interesting, but for what do we need this AI?“. Most people don’t even know that they are already dealing with powerful AI systems every day on social media platforms and e-commerce websites. But we urgently need to start a productive public dialogue about how this technology can be used for the benefit of all.

Therefore, the AI explosion also creates an unprecedented challenge for our school system. The challenge is multifaceted: apart from the necessary speed, which has to keep pace with the development of the technology, teaching AI requires an interdisciplinary approach. Mathematics is required to understand the engineering part and inspiration came from biological systems. But AI has to be discussed also outside STEM subjects: students must learn to discuss complex economic, social, ethical and philosophical questions. AI even questions our understanding of language. In the coming world, things cannot be studied in isolation. Everything is ultimately connected to everything. Kids must learn to think in networks and systems instead of isolated problems. They also need a deeper understanding of the technical concepts used in AI. Only then they will be competent enough to discuss the implications of this technology for society productively.

To bring our school system up to speed for these challenges, it will have to adapt faster than ever before. To be able to be successful in this transformation process, schools need strong support. This means a concentrated effort of society is necessary. First of all, we need quick and competent decisions from politics: our schools need substantial additional funding to finance the education of teachers. Relevant research at pedagogical universities must be promoted and funded. We need to train a large number of instructors who can train teachers.

For all this to happen, we will need the support of the media: society as a whole must become aware of the enormous challenges we are facing today. Only then the topic will be given the attention in politics it deserves.

About the Author:

Marco Lardelli (born 1969) is a Swiss entrepreneur, data scientist and trainer. After studying physics at ETH Zurich and the University College Dublin he founded several startups in the web and mobile business. He is the author of a free A.I. and robotics course for teenagers: ki-kit.ch.

Der Beitrag We must teach our kids AI now! The why, the what and the how erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

copyright by news.bloombergtax.com

Robots and artificial intelligence are not just sci-fi movie stars. Payroll is moving into the cyberworld as a way to improve efficiency, two executives said June 3.

The use of robotic process automation (RPA), artificial intelligence, and data analytics can help employers streamline payroll processing, analyze information, and reduce costs by having computer software perform tasks that often are conducted manually, said Joe Ziska, manager of compensation and organizational management at the automaker BMW Manufacturing Co.

Employers’ needs are rapidly evolving, especially during the novel coronavirus pandemic, Ziska said. The number of teleworkers in the U.S. rose to 64% from 29% since mid-March, and gig workers make up 36% of the workforce, either as contractors or as members of a temporary workforce, he said.

“Workers suddenly needed to receive payments while working remotely, in many cases for the first time,” Ziska said. Companies are struggling to keep up, and with 30% of businesses using payroll systems that are at least 10 years old, the move toward automation to improve efficiency has employers open to new technology, he said at the annual American Payroll Association Congress, which was held online because of social-distancing requirements from the coronavirus crisis.

Automated Robot: Not a Machine

The use of RPAs is one solution to improve efficiency, said Fidelma McGuirk founder and CEO of Payslip, a company in Ireland that provides technology to help companies automate payroll processes and standardize data. Robotics can be used to “take over standard and repetitive activities that are currently carried out by humans,” she said. “It increases the value of the work done by the human by removing work than can be repetitive.”

“An RPA is not a physical machine,” McGuirk said, unlike the sci-fi machines from movies and novels. In payroll, an RPA would not replace existing payroll software, but could be used in support of data-driven tasks, such as payroll validation and reconciliation. If errors or inconsistencies are found, the RPA can return the file for a manual review, she said.

Additionally, with so many employees teleworking, distractions at home would be negated with the use of RPAs because automation would reduce the chance for errors, McGuirk said.

While RPAs can take over manual tasks, the software cannot learn.

Artificial Intelligence

“RPA is a technology that follows predetermined rules to help automate tasks. It’s elegant but it’s also static, and it can’t learn,” Ziska said. “AI commonly describes technology that has the capacity to get smarter over time. The term for that is machine learning.” As machines get smarter, their capabilities increase, he said.

Common uses of AI in payroll include self-service support, reporting requests, and technical troubleshooting, Ziska said. An added benefit: Bots can run for 24 hours.

In choosing to adopt AI in the payroll process, Ziska said employers need to be flexible and open to change, prepare for a long-term commitment, react quickly to unexpected developments, and perform due diligence with the AI service provider.

“AI always gets billed as not needing to take lunch breaks or being able to work overtime for free,” Ziska said. “In the same light, you also invest as much time up front as you would a person.”

Analytics as an Alternative

If an organization is not ready for RPA and AI, data analytics is an alternative.

Analytics can help employers sort out data, such as how much overtime was incurred in a month during the pandemic or the difference between increase payroll costs vs. the number of employees. In the process, large amounts of information from various sources can be analyzed to uncover hidden patterns and unknown correlations, McGuirk said. […]

read more news.bloombergtax.com

Der Beitrag Robots, Artificial Intelligence Are in Payroll’s Future erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright by www.computerweekly.com

Sure, it might be fun for a few days, but after completing your sixth, seventh or eighth Millennium Falcon, eventually you’re going to get bored out of your skull. Infographic: 5 ways to achieve a risk-based security strategy

Learn five steps for implementing a risk-based security strategy that naturally delivers compliance as a consequence of an improved security posture.

As we snatch 20 minutes to chat during a break at a recent summit on all things digital and cyber, hosted by the Irish government in Dublin , Microsoft’s vice-president of cyber security, Ann Johnson, draws just such an analogy.

“If you think about an SOC [ security operations centre ] admin – they have a brain that needs to be challenged,” she says. “If I keep handing them a Lego kit that always builds the Millennium Falcon, and that’s the only thing they ever get to do, they’re going to get bored eventually.”

So what does this have to do with cyber security, and artificial intelligence (AI) in particular? Don’t worry, this wasn’t just an excuse to shoehorn in a Star Wars reference. This is going somewhere.

“We see so much signal, right?” says Johnson. “Too much. And so you need machine learning to rationalise all that signal that we see. But when AI becomes more than machine learning, something that reasons and thinks – it will help our analysts work better.

“We have now learned that analysis plus AI is a much better outcome. It will help them work better, smarter and faster. I talk about the automation of low-level tasks a lot – that’s because I want our humans working on complex stuff for two reasons: number one, they’re better at it; number two, they get bored.”

Let’s pivot back to Lego for a moment. If you give the same Millennium Falcon kits to the AI to build, the human master builders – or security analysts – can go away and challenge themselves with something new. Perhaps the Tower of Orthanc from Lord of the Rings, the Ghostbusters’ headquarters, or the Shield helicarrier from the Marvel Cinematic Universe.

Or as Johnson puts it: “AI actually helps us discern what is a really complex task and what is a really simple task and then, using automated remediation on those simple tasks, we can let the humans work with the AI on the really complex tasks, and that’s the place we want to be, because then you start getting much better outcomes, you get faster outcomes. […]

Read more – www.computerweekly.com

Der Beitrag The Security Interviews: Applying AI to Lego, and security erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright by www.forbes.com

Last year, one group of students decided to work with a hospital in Dublin that treats people with mental and physical disabilities. After one meeting with staff and patients, the students realized they needed a different technical environment in order to make progress on their project.

Tierney, who is a lecturer at TU Dublin, worked closely with them and saw a way to assist. “I said, ‘You need a database environment and a couple of other things,’” he recalls. “And within five minutes I had everyone working in Oracle Autonomous Database.”

What’s an autonomous database? It’s Oracle’s cloud-based database that uses machine learning designed to reduce, or in some cases eliminate, the human labor associated with tasks such as database tuning, security patching, software updates, data backups, and other routine efforts. Introduced in 2018, this family of cloud services frees database administrators and other users to do more valuable work.

Free for Educators

To help remove as many barriers as possible for professors and students to realize the benefits of the cloud, Oracle has launched Oracle Cloud Free Tier, including new Always Free services. This means that anyone—from large and small organizations, developers, students, educators, and IT professionals—can try services and build applications on Oracle Cloud. The Always Free services are a set of core resources that include two Autonomous Database instances, two compute virtual machine instances, 100 gigabytes of block storage, 10 gigabytes each of object and archive storage, network and load balancing, monitoring, and notifications, all available at no charge with no expiration date, as long as it’s still in use.

Tierney applauds this move, noting that it provides assurances to professors that the cloud database they start using in their classrooms will be with them for the long haul. For students, he adds, it means that they’re learning on the best database in the marketplace—and students, whether or not they’re using Oracle Cloud Database for a classroom project, will be able to spin up a separate cloud instance for any side project they’re engaged with—be it pro bono work or an entrepreneurial plan.

In addition, Oracle Academy institutional member educators and their students will be able to access these cloud services and free credits through a new Oracle Academy Cloud Program that includes new training for teachers and curriculum for Oracle Autonomous Database to help students expand their skillset and gain hands-on experience. […]

Read more – www.forbes.com

Der Beitrag 5 Ways The Cloud Changes The Game For College Developers erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

copyright by www.irishexaminer.com

According to Enterprise Ireland’s high-potential startup manager Joe Healy, these are the key enabling technologies that will drive the scaling of businesses of the future.

“We are seeing artificial intelligence/machine learning being more widely used in the development of platforms and software solutions across all verticals. These technologies are being adapted for things as complex as big data solutions, but also in many practical enterprise software solutions,” he said.

This follows a global trend which has seen the emergence of deep tech companies, using cutting edge and disruptive technologies based on scientific research, setting to work on solving some of the world’s major problems.

Irish companies operating in the deep tech space include AidTech in Dublin, the overall winner of the 2018 innovation award, which used blockchain to develop a secure way to deliver aid.

Its technology has already been used to deliver international aid to Syrian refugees in Lebanon.

Another Enterprise Ireland high-potential startup client this year included ServisBot, based at ArcLabs in Waterford, which uses AI conversational to enhance the customer experience.

At the Rubicon Centre in Cork, start-up FourTheorem describes itself as a new breed of software consultancy focused on cloud architecture, DevOps, and artificial intelligence.

According to its website, it provides solutions which enable customers to be leaders in the fourth wave of industrialisation.

“Companies such as ServiceBot, RecommenderX, which transforms AI to business intelligence, and AidTech not only have compelling business models but are numerous enough to attract international venture capital to Ireland,” said Mr Healy.

In addition to providing support for deep tech companies at high-potential startup level, Enterprise Ireland has also encouraged early stage startups in this space.

In July, it put out a call offering €750,000 in competitive start funding for fintech and deep tech companies, the first call to offer specific support for deep tech.

The call was one of nine during the year which provided a total of €5.75m, offering €50,000 competitive start funding to 115 companies. This was at the same level as in 2017 when 91 companies availed of it.

Other such funding calls made during the year were for graduate entrepreneurs, international entrepreneurs, and experienced business professionals, an initiative started last year.[…]

read more – copyright by www.irishexaminer.com

Der Beitrag Deep tech to make deep impact erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

copyright by home.kpmg.com

“Ireland stands out in the field of AI on a number of fronts, including the innovative ecosystem that has developed over the past few years that supports and fosters disruptive thinking, the access we have to global tech giants, the breadth and depth of talent that exists in our workplace and the spirit of entrepreneurialism,” says Lewis. “Coupling this with some global heavyweights in AI, both in academic and professional circles, then I can only see growth in this capability.”

Artificial intelligence has had a much more significant role in day-to-day life in recent years. The ability of a computer to observe and learn from patterns has been the driving force behind new technology – from alarms that can monitor our sleep cycles and tell us the best time to wake up, to the GPS systems that can redirect us on alternative routes in rush hour, the everyday uses of AI are increasing. But the real potential for AI lies in how it is used behind the scenes, analysing big data, and that is also the area where Ireland has the ability to lead.

The real challenge, according to Lewis, is retaining talent: “Our challenge remains how we retain talent when such amazing opportunities exist for home-grown talent to be snapped up abroad. This is where Government policies need to continue to support local innovation and investment, and our success on the global stage for home-grown start-ups needs to be supported. Ireland is clearly capable to achieve this and requires us all to keep our eyes on the future as we consider priorities in Government investments, education and regulatory agility.”

Ireland may not be leading the way in the consumer branch of AI, but it has a rich history in advanced research and development of AI through universities and research institutes, which are often collaborations between academic institutions and the many large multinational tech companies that are based here.[…]

read more – copyright by home.kpmg.com

Der Beitrag Artificial Intelligence erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

copyright by www.lexology.com

The term is defined in popular culture, and in the eyes of employees the world over, as an ever-approaching threat. The World Economic Forum has discussed AI as a major element of the fourth industrial revolution (4IR) and something which will rapidly change our world and workplaces. Regardless of the definition, AI is coming into our workplaces and coming quickly.

As with any change to workplaces, employment law will follow. Employment lawyers and the Irish legislature need to consider how current employment law in Ireland may need to adapt to harness the full potential of AI for Irish businesses and, also, to protect employees. William Fry will investigate these issues in a series of articles in 2018, exploring how modern Irish workforces will be changed by AI and will analyse the employment law issues connected with these changes. This introductory article to the series focuses on the potential positives of AI in helping to solve some of Ireland’s current employment law issues.

Recruitment

AI has already changed the recruitment process making it more effective. Such change will most likely increase rapidly. As this continues to develop, and perhaps reach the stage where the first stages in a recruitment process have little human interaction, it will be crucially important to ensure that Irish employment equality legislation is still respected. This includes programming the technology early on to respect the nine equality grounds and fixing any latent bias in technology. Of course, AI might also help employers to be more diverse in their recruitment process allowing candidates who fit criteria but who failed to reach interview stage because of a previous latent bias to overcome those hurdles.

Gender Equality

The gender pay gap and gender equality more generally, can stand to improve from the rise of robots too. This can come from recruitment as discussed above to bonuses, benefits and even pay being tracked by technology to actual output by employees. This has the potential to defeat any latent bias in an organisation and can stand to also defeat the sort of issues identified as a result of the first deadline of UK gender pay gap reporting. We identified in a recent article that the Irish legislature and Irish employers need to learn from the lessons of the UK before final gender pay gap reporting legislation is adopted in Ireland. Making use of technology to accurately track pay in companies and then accurately report would provide better and more real data and help companies identify problems in their companies and seek to fix those problems more effectively.

Disabilities

The use of technology in harmony with human work can open new doors to helping employees with disabilities remain in or return to the workforce more easily than before. This would also help employers to honour their legislative responsibilities more easily than before and perhaps assist with reasonably accommodating more cost-effectively. Existing legislation could be reviewed to allow for greater connection between employees and technology including perhaps the need for employers to offer education as part of accommodation following serious illness or accident. […]

read more – copyright by www.lexology.com

Der Beitrag Artificial Intelligence in the Workplace – An Employment Law Perspective erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.