John Buyers, partner and head of Artificial Intelligence at Osborne Clarke shared his views with Business Leader regarding the future of AI in the UK. The EU Commission is proposing a whole new regulatory framework and infrastructure for developers and providers of Artificial Intelligence (AI) systems. This includes conformity assessments, labelling obligations, a new EU-level body, new national enforcement roles and another batch of extremely heavy penalties for non-compliance, potentially even higher than GDPR fines in some areas. This legislation will have a global impact on the AI sector since it will create new regulatory barriers for supplying or developing AI systems for EU customers. This new proposal follows the Data Governance Act published at the end of 2020 which similarly envisages a new regulatory framework for the EU data ecosystem.

The unprecedented interest, investment, and deployment of machine learning across many aspects of our lives in the past decade has come with a cost. Although there has been some movement towards moderating machine learning where it has been genuinely harmful, it's becoming increasingly clear that existing approaches suffer significant shortcomings. Nevertheless, there still exist new directions that hold potential for meaningfully addressing the harms of machine learning. In particular, new approaches to licensing the code and models that underlie these systems have the potential to create a meaningful impact on how they affect our world. This is Part I of a two-part essay.

A human rights lawyer responds to Catherine Lacey's "Congratulations on Your Loss." A few years ago, I attended a meeting for litigators at a digital rights conference. When entering the room, I saw many familiar faces, and a few that were unfamiliar. When I introduced myself to one of the women I had never seen before, a white woman, she reacted in a most offended manner. "Yes, we met this morning at your office," she snapped at me.

These advances will create a network where almost every device can be simultaneously connected, enabling technologies not possible today. Governments and private entities are just beginning to invest in the technology, and projections suggest commercial availability around 2030. But given 6G's anticipated ubiquity and potential to change the landscape, we would be wise to begin learning about it now. Artificial intelligence ("AI") represents a new frontier in the global economy: Some estimates say it could contribute up to $15.7 trillion worldwide by 2030. Increases in computing power and innovations in computer science have fueled AI innovation.

The study of the social impact of automated decision making has focused largely on issues of fairness at the point of decision, evaluating the fairness (with respect to a population) of a sequence or pipeline of decisions, or examining the dynamics of a game between the decision-maker and the decision subject. What is missing from this study is an examination of precarity: a term coined by Judith Butler to describe an unstable state of existence in which negative decisions can have ripple effects on one's well-being. Such ripple effects are not captured by changes in income or wealth alone or by one decision alone. To study precarity, we must reorient our frame of reference away from the decision-maker and towards the decision subject; away from aggregates of decisions over a population and towards aggregates of decisions (for an individual) over time. An individual who lives with higher precarity is more affected and less able to recover by the same negative decision than another with low precarity. Thus including only the direct impact of a single decision or a few decisions is insufficient to judge if that system was fair. However, precarity is not an attribute of an individual; it is a result of being subject to greater risks and fewer supports, in addition to starting off at a less secure position. Precarity is impacted by racism, sexism, ableism, heterosexism, and other systems of oppression, and an individual's intersectional identity may put one at greater risk in society, subject to a lower income for the same job, less able to build wealth even at the same income level, and less able to recover from harm.

Automatically recommending relevant law articles to a given legal case has attracted much attention as it can greatly release human labor from searching over the large database of laws. However, current researches only support coarse-grained recommendation where all relevant articles are predicted as a whole without explaining which specific fact each article is relevant with. Since one case can be formed of many supporting facts, traversing over them to verify the correctness of recommendation results can be time-consuming. We believe that learning fine-grained correspondence between each single fact and law articles is crucial for an accurate and trustworthy AI system. With this motivation, we perform a pioneering study and create a corpus with manually annotated fact-article correspondences. We treat the learning as a text matching task and propose a multi-level matching network to address it. To help the model better digest the content of law articles, we parse articles in form of premise-conclusion pairs with random forest. Experiments show that the parsed form yielded better performance and the resulting model surpassed other popular text matching baselines. Furthermore, we compare with previous researches and find that establishing the fine-grained fact-article correspondences can improve the recommendation accuracy by a large margin. Our best system reaches an F1 score of 96.3%, making it of great potential for practical use. It can also significantly boost the downstream task of legal decision prediction, increasing the F1 score by up to 12.7%.

But it can step in when companies misrepresent the capabilities of a product they are selling, which means firms that claim their facial recognition systems, predictive policing algorithms or healthcare tools are not biased may now be in the line of fire. "Where they do have power, they have enormous power," says Calo. In the blog post, the FTC warns vendors that claims about AI must be "truthful, non-deceptive, and backed up by evidence." The result may be deception, discrimination--and an FTC law enforcement action." The FTC action has bipartisan support in the Senate, where commissioners were asked yesterday what more they could be doing and what they needed to do it. But it can step in when companies misrepresent the capabilities of a product they are selling, which means firms that claim their facial recognition systems, predictive policing algorithms or healthcare tools are not biased may now be in the line of fire. "Where they do have power, they have enormous power," says Calo. In the blog post, the FTC warns vendors that claims about AI must be "truthful, non-deceptive, and backed up by evidence." The result may be deception, discrimination--and an FTC law enforcement action."

Climate change is a clear and present danger to the world economy. The tech industry bears its share of responsibility, not just for carbons emission but deforestation, plastic, chemical and other waste contamination, resource depletion and other damaging activities. But the tech industry also has the capacity to dramatically change the trajectory of all these problems; to at least slow down, if not reverse, the harm being done to our one and only planet. Artificial Intelligence (AI) in particular is already having a remarkable impact on issues that seemed intractable only a few years ago. Rather than being bad for the climate, AI is proving to help.

TK MILAB AI and Law Online Research Seminar Series' next event covers the following topic: AI systems in different shapes and sizes play an increasing role in our lives – from mobile phones and computers over robot vacuum cleaners and lawnmowers to surgical robots and large industrial installations. Algorithms assist attorneys-at-law in drafting contracts, and they can even create newspaper articles. Which unavoidably leads us to the question, who is going to foot the bill if something goes wrong? In the past couple of years, EU institutions have been working towards comprehensive regulation of these questions on EU level. Several policy papers and a draft regulation have been issued since then.

Currently, the most widespread neural network architecture for training language models is the so called BERT which led to improvements in various NLP tasks. In general, the larger the number of parameters in a BERT model, the better the results obtained in these NLP tasks. Unfortunately, the memory consumption and the training duration drastically increases with the size of these models, though. In this article, we investigate various training techniques of smaller BERT models and evaluate them on five public German NER tasks of which two are introduced by this article. We combine different methods from other BERT variants like ALBERT, RoBERTa, and relative positional encoding. In addition, we propose two new fine-tuning techniques leading to better performance: CSE-tagging and a modified form of LCRF. Furthermore, we introduce a new technique called WWA which reduces BERT memory usage and leads to a small increase in performance.