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The European Commissions Proposal for a Regulation laying down harmonized rules on Artificial Intelligence (Draft AI Regulation) is far from being final and even further from taking effect. Indeed, it was published on April 21, 2021, is open to consultation until August 6, 2021, and is subject to the ordinary legislative procedure, which requires not less than two years to conclude.

We already reviewed some of the aspects of the Draft AI Regulation in our recent article focusing on high-risk AI systems. In addition, we have had a chance to talk and brainstorm about the Draft AI Regulation with good friends and notable clients, sharing our first comments and impressions. In this article, we have shortlisted the top 10 issues that have emerged so far in our conversations: they include five things that were generally liked, and five things that a number of commentators would have wanted more of.

Finally!

After years spent wondering what an artificial intelligence system is, we now have a definition: software that is developed with one or more of the techniques and approaches listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with (AI System).

Compared with older definitions, this one does not make any reference to personal data. Such a lack is only apparent, since the Draft AI Regulation focuses a lot on personal data: both in requiring that high-risk AI Systems are trained using only high-quality sets of data, and in the many similarities with the GDPR that lead one to infer that the GDPR served as a starting point for the Draft AI Regulation.

The fact that the Draft AI Regulation has many similarities with the GDPR should come as no surprise, bearing in mind that (as we all know) artificial intelligence is fed by data.

These similarities include:

Another similarity with the GDPR is the establishment of the European Artificial Intelligence Board, composed of representatives of national supervisory bodies and the European Data Protection Supervisor and chaired by the EU Commission.

The European Artificial Intelligence Board, like the European Data Protection Board, will be in charge of supporting EU member states in the uniform interpretation and application of the new regulatory regime. This task is of paramount importance, taking into account that the EU Commission has opted for a regulation (rather than a directive), thus confirming the need for the most consistent legal regime to be implemented Europe-wide.

Furthermore, the European Artificial Intelligence Board will also help in achieving a unitary approach to the European strategy.

AI regulatory sandboxes make it clear that the purpose of the Draft AI Regulation is not to limit, but to foster innovation.

They will offer a controlled environment, which facilitates the development, testing and validation of innovative AI systems for a limited time before their placement on the market or putting into service, under the direct supervision and guidance from the competent authorities (including data protection authorities, where personal data are processed within the sandbox). In doing so, the EC aims to ensure compliance with the requirements of the Regulation and, where relevant, other Union and member states legislation supervised within the sandbox.

Almost all the new rules will apply to high-risk AI systems only.

The EU Commission pointed out that most AI systems entail a minimal risk and, therefore, fall out of the scope of the Draft AI Regulation. This clearly results in a simplification, since the (rich) new regulatory and compliance regime also considering that certain AI systems are banned will be applied in a limited number of cases. Consequently, it is unlikely that the Draft AI Regulation will disincentivize the use of AI Systems.

As said, the risk-based approach proposed by the Draft AI Regulation is undoubtedly appreciated; however, clearer criteria to determine to which risk category an AI system belongs may be needed.

It is predictable that many AI systems will fall halfway between the high-risk and the limited-risk categories. In these cases, the decision to consider the AI system as high risk (for precautionary reasons) would lead to the application of a very different regulatory/compliance regime, with definitely more onerous compliance requirements. Such requirements would apply not only by the developer of the AI system, but to all people and entities involved in the AI system lifecycle (in light of the extended scope of application proposed by the EU Commission). The risk is clearly that, when in doubt, the decisions will be in favor of the lower level of risk, with a consequent lower protection for individuals.

Furthermore, the Draft AI Regulation does not provide any criteria to distinguish between limited-risk and minimal-risk AI Systems, and therefore, differentiating between the two will not be easy. In such a case, however, adopting a precautionary approach and deciding to consider the AI system as low risk (rather than as minimal risk) will entail only one regulatory requirement: i.e. disclosing to the users that they are interacting with an AI system, thus giving them the opportunity to decide to stop the interaction.

One of the major concerns when speaking about artificial intelligence is: who is liable for defaults and damages attributable to acts and omissions of the AI system?

It is clear that the EU Commission is trying to reinforce trust in AI by adopting an ex ante approach, i.e. trying to ensure that only safe AI systems are used, by reinforcing regulatory and compliance rules to be complied with before placing the AI system on the market, as well during its lifecycle.

However, clear rules on allocation of liability may also help in achieving this purpose. When individuals know (for sure) that someone (who can be clearly and easily identified) is ultimately liable for any default of the AI System, they will undoubtedly be more prone to use (and trust) an AI System.

It has been disclosed that new rules on liability regimes will soon be issued: hopefully, they will be applicable to all AI systems (and not only to high-risk AI systems).

Like the GDPR, the Draft AI Regulation provides for maximum fines only (ranging from 0 to 30 million or 6% of the total annual turnover of the preceding financial year, whichever is higher), without clarifying in detail the criteria which will be used to assess the amount of the fine to be concretely issued in each case.

As happened with the GDPR, there will likely be guidelines from the European and national authorities on how to assess fines, but if such guidelines are not sufficiently detailed and specific, the difficulties experienced in the past three years of application of the GDPR will re-occur. Risks in using AI systems will not be concretely assessable, and this may result in a disincentive to employ artificial intelligence.

The Draft AI Regulation requires that high-risk AI systems are explainable, and therefore understandable, by human beings. This is rather utopian, since the functioning of AI systems may not be fully clear to their developers themselves.

To ensure that the Draft AI Regulation remains relevant and is applied in its entirety, a more concrete approach would be appreciated. For example, it may be advisable to provide exceptions to the general rule, taking into account when the control over the AI systems has to be controlled itself.

We all know the problems that data controllers and data processors are (still) experiencing after the so-called Schrems II Judgement to lawfully transfer personal data outside the European Union.

This judgment will undoubtedly affect data transfers for the purposes of constituting the high quality data sets to train the AI systems, as required by the Draft AI Regulation. It is predictable that the more difficulties and burdens in the processing of personal data, the more difficulties and burdens in ensuring the highest quality of data sets.

Perhaps, new and clear rules on data transfers will be issued soon and may also include derogations, exceptions or specific provisions to be applied for AI systems.

What are your top 10 issues with regards to the Draft AI Regulation? Do you agree with the above or do you have more?

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Regulating artificial intelligence in the EU: top 10 issues for businesses to consider - JD Supra

Researchers from De Montfort University Leicester (DMU) who found artificial intelligence (AI) can improve school attendance are now looking at ways to increase pupil engagement and enhance pupil attainment in a follow-up project, notably amongst disadvantaged groups.

Dr Raymond Moodley, member of DMUs Institute of AI (IAI) and of theRiSE(Research in Societal Enhancement) group, has led an initiative that aims to ensure no child goes to school hungry, by providing free breakfast packs for primary-age pupils across Milton Keynes.

This follows on fromRiSEsprevious work which enhanced Milton Keynes-based Willen Primary Schools attendance by using AI models to identify patterns relating to pupils who were frequently absent.

Dr Moodley is leading the initiative as a follow-up to the AI research

Our AI models can describe and predict issues but the solution to these issues lies in social action, explained Dr Moodley. We know from the previous study that AI can be used to improve attendance but how engaged are the pupils when they are at school?

We have found that a number of primary school children go to school without having breakfast and this impacts their behaviour and learning, and ultimately the schools results are affected too.

Funded and supported by a local multifaith organisation, the Milton Keynes Sai Centre, Dr Moodleys initiative has led to the creation of breakfast packs containing a box of cereal and carton of long-life milk for any child who requests one.

A child who is not properly fed can still attendschoolbut they are less likely to be engaged especially with core subjects like Maths and English which are usually taught in the morning, he continued.

"It is hard to imagine children being able to enjoy the adventures of the Famous Five or appreciate the beauty and usefulness of arithmetic and geometry when their tummies are rumbling from not having eaten for 15 hours with their next meal still three hours away.

Dr Suresh Nesaratnam, chair of Milton Keynes Sai Centre, said: This initiative is at the heart of our purpose: love all, serve all.

Apart from alleviating the immediate distress that children are feeling by going without food, we are very mindful that this initiative will also enable children to engage better at school, thus improving their life outcomes and the outcomes for society at large.

The initiative is also being supported by Morrisons Leisure Plaza store in Milton Keynes, with the breakfast packs available to buy in-store for under 2.

This is a social action initiative designed to help resolve engagement issues we are seeing at schools, added Dr Moodley. It is being trialled in Milton Keynes but can easily be adopted anywhere in the country from larger cities like Leicester or Birmingham to smaller villages in Lincolnshire.

Ms Carrie Matthews, headteacher at Willen Primary School, said: We are very fortunate to work alongside DMU's RiSE team in executing our strategy to enhance pupil attainment and progress.

Supported by AI, our attendance has improved and remains high, and we are now addressing pupil engagement by tackling child food poverty which is being led by the breakfast pack scheme.

This holistic, AI-supported approach, will go a long way in enhancing the life outcomes of all our children at Willen Primary School.

RELATED NEWSResearchers use artificial intelligence to improve school attendanceAI research shows 25% of Britain could have avoided lockdownFind out more about studying Artificial Intelligence at DMU

Dr Moodley and his RiSE colleagues, Dr Mario Gongora, Dr Fabio Caraffini, and Prof Francisco Chiclana will be working alongside DMUs Health and Life Science colleagues to provide an integrated approach to improving educational and health outcomes of pupils.

"The damaging effects of child poverty can have lifelong negative impacts on well-being and health outcomes. Initiatives like the breakfast pack scheme are simple, effective, and will positively impact childrens lives both immediately and later in life," said Professor Bertha Ochieng, Professor of Integrated Health and Social Care at DMU.

Posted on Friday 2nd July 2021

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Artificial intelligence researchers tackle pupil engagement in new project with primary schools - De Montfort University

Dutch artist Rembrandt van Rijn is famous for his extensive oeuvre of art, which includes sketches, prints, and paintings. Among the many masterpieces within his portfolio is a particularly ambitious piece that has impressed audiences with its scale and detail centuries after the painter's deathThe Night Watch (1642). This lifesize group portrait is one of the most influential pieces from the Dutch Golden Age and, since 2019, has been the subject of an extensive restoration by the Rijksmuseum called Project Night Watch.

Last year, Project Night Watch released an extremely detailedand completely free-to-download 44.8 gigapixel image of Rembrandt's masterpiece. Recently, the museum unveiled another incredible feat. By combining art with artificial intelligence, the team was able to add missing pieces to the original painting. In 1715, the monumental canvas was cut down on all four sides to fit onto a wall between two doors in Amsterdams Town Hall, writes Nina Siegal. The snipped pieces were lost. Since the 19th century, the trimmed painting has been housed in theRijksmuseum, where it is displayed as the museums centerpiece, at the focal point of its Gallery of Honor.

This success of completing The Night Watch to its original form depended on a 17th-century copy of the painting that was made by Dutch artist Gerrit Lundens prior to the trimming. With this record of what The Night Watch used to look like, the museum's scientists were able to train artificial intelligence to recreate the missing portions in Rembrandt's unique style of painting. This process required a long period of tinkering with the neural network's precision. Eventually, the Rijsmuseum's senior scientist, Rob Erdmann, was able to successfully calibrate the AI technology to print a believable replica of Rembrandt's handiwork onto canvas.

The AI's printed panels will be displayed beside Rembrandt's painting until the end of September 2021, and then they will be removed to honor the original work.

17th-century copy of Rembrandts The Night Watch' by Dutch painter Gerrit Lundens with lines added indicating the areas cut down from the original painting in 1715. (Photo: Public domain, via Wikimedia Commons.)

Rijksmuseum: Website | Facebook | Instagramh/t: [Open Culture, Art News]

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Rembrandt's 'The Night Watch' is Restored by Artificial Intelligence - My Modern Met

At present, global AI adoption and investment are soaring. Reportedly, 37 percent of organizations have deployed AI solutions. Analysts have forecasted that global AI spending will more than double over the next three years, topping US$79 billion by 2022.

Almost two-thirds of global reports state that AI technologies are enabling their organizations to move ahead of the competition. Sixty-three percent of the leaders surveyed already view AI as very or critically important to their business success, and that number is expected to grow to 81 percent within two years.

However, analysts are somewhat uncertain about how much effect AI would have on human jobs. On one hand, AI-driven automation has made most jobs obsolete and on the other hand, new designations of jobs and new territories are being discovered concerning AI operations. While AI adopters express concern about automation as an ethical risk, they emphatically believe that human workers and AI will augment each other, changing the nature of work for the better. Changing how work gets done within the organization by making operations more efficient, supporting better decision-making, and freeing up workers from repetitive tasks- is the core to what companies want to achieve with AI.

To meet their AI aspirations, companies will likely need the right mix of talent to translate business needs into solution requirements, build and deploy AI systems. They would require to integrate AI into processes and interpret results. However, most early adopters face an AI skills gap and are looking for expertise to boost their capabilities. Reportedly, 68 percent of executives confess to a moderate-to-extreme skills gap, and more than a quarter rate their skills gap as major or extreme. The gap is evident across all countries surveyed, ranging from 51 percent reporting moderate-to-extreme gaps in China to 73 percent reporting the same in the United Kingdom. The new designations of this field are AI builders and AI translators. These AI builders refer to researchers to invent new kinds of AI algorithms and systems, software developers to architect and code AI systems, data scientists to analyze and extract meaningful insights from data, and project managers to ensure that AI projects are executed according to plan. AI translators bridge the divide between the business and technical staffthe front and back ends of building AI solutions.

The desire to replace workers with new, AI-ready talent is clear, but it is not the end of human jobs at all. Reports reveal a scarcity of AI talent around the world. Canadian firm Element AI recently analysed LinkedIn profiles to gauge the size of the worldwide top-tier AI talent pool and counted 36,524 self-reported PhD-level AI experts (including data scientists and machine learning researchers and engineers). So, it is very clear that when a door is being closed in the job sector, a new, more advanced section of jobs are opening doors on the other side.

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Are We Losing Jobs? Effect of Artificial Intelligence in Workforce - Analytics Insight