At first, you might find the idea of Artificial Intelligence/Machine Learning being associated with Law very unlikely since both the fields appear to be poles apart. Well, the truth is far from it; today, Artificial Intelligence or AI is on the way to transforming the legal profession in various ways, helping Law firms manage their operations as well as augmenting and reducing many of the tasks that were previously relied upon humans to do, saving precious time and manpower that can be otherwise used for more productive tasks.

Commercial and industrial applications of artificial intelligence and machine learning are unlocking economic opportunities, transforming the way we do business, and even helping to solve complex social and environmental problems. In fact, generative applications of this technology have become tools for environmental sustainability. With machine learning's capability to analyze and make predictions using massive pools of data, these applications are now able to accurately model climate change and fluctuations, so that energy infrastructures and energy consumption can be re-engineered accordingly. Ironically, training large-scale models via deep neural networks requires vast computational power. It also produces a great deal of thermal energy from each of the associated graphics processing units (GPUs) or tensor processing units (TPUs) used.

Elite institutions, the self-appointed arbiters of ethics are guilty of racism and unethical behavior but have zero accountability. In July 2020, MIT took a frequently cited and widely used dataset offline when two researchers found that the '80 Million Tiny Images' dataset used racist, misogynistic terms to describe images of Black and Asian people. According to The Register, Vinay Prabhu, a data scientist of Indian origin working at a startup in California, and Abeba Birhane, an Ethiopian PhD candidate at University College Dublin, who made the discovery that thousands of images in the MIT database were "labeled with racist slurs for Black and Asian people, and derogatory terms used to describe women." This problematic dataset was created back in 2008 and if left unchecked, it would have continued to spawn biased algorithms and introduce prejudice into AI models that used it as training dataset. This incident also highlights a pervasive tendency in this space to put the onus of solving ethical problems created by questionable technologies back on the marginalized groups negatively impacted by them. IBM's recent decision to exit the Facial Recognition industry, followed by similar measures by other tech giants, was in no small part due to the foundational work of Timnit Gebru, Joy Buolamwini, and other Black women scholars.

I was sent a copy of Ryan Abbott's "The Reasonable Robot" by the publishers. It is an interesting book that discusses a few critical areas of law as they could interact with artificial intelligence (AI). The book is worth reading, even if it is far from perfect. It is an excellent discussion point, a starting place for people to begin to think about artificial intelligence and the law. Software and law has always been an intersection that has interested me.

The so-called "black-box" aspect of AI, usually referred to as the explainability problem, or X(AI) for short, arose slowly over the past few years. Still, with the rapid development in AI, it is now considered a significant problem. How can you trust a model if you cannot understand how it reaches its conclusions? For commercial benefits, for ethics concerns or regulatory considerations, X)(AI) is essential if users understand, appropriately trust, and effectively manage AI results. In researching this topic, I was surprised to find almost 400 papers on the subject.

Online social networks provide a platform for sharing information and free expression. However, these networks are also used for malicious purposes, such as distributing misinformation and hate speech, selling illegal drugs, and coordinating sex trafficking or child exploitation. This paper surveys the state of the art in keeping online platforms and their users safe from such harm, also known as the problem of preserving integrity. This survey comes from the perspective of having to combat a broad spectrum of integrity violations at Facebook. We highlight the techniques that have been proven useful in practice and that deserve additional attention from the academic community. Instead of discussing the many individual violation types, we identify key aspects of the social-media eco-system, each of which is common to a wide variety violation types. Furthermore, each of these components represents an area for research and development, and the innovations that are found can be applied widely.

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges.

The Los Angeles Police Commission on Tuesday said it would review the city Police Department's use of facial recognition software and how it compared with programs in other major cities. The commission did so after citing reporting by The Times this week that publicly revealed the scope of the LAPD's use of facial recognition for the first time -- including that hundreds of LAPD officers have used it nearly 30,000 times since 2009. Critics say police denials of its use are part of a long pattern of deception and that transparency is essential, given potential privacy and civil rights infringements. Commission President Eileen Decker said a subcommittee of the commission would "do a deeper dive" into the technology's use and "work with the department in terms of analyzing the oversight mechanisms" for the system. "It's a good time to take a global look at this issue," Decker said.

First, the two cumulative criteria proposed by the Commission will inevitably be incomplete, leaving some applications out. That's the tradeoff for simple rules – they miss the mark in a small but significant number of cases. To work properly, simple rules must be supplemented by a general catch-all category for other high-risk applications that would not qualify under the two-criteria test. If you add a catch-all test (which would be necessary in our view), the goal of legal certainty would be largely defeated. Second, the "high risk" criterion will interfere with other legal concepts and thresholds that already apply to AI applications.

Initiating something new, particularly in the midst of change, at the local, national and global levels, takes courage. I would also argue that truly sustainable change happens when we bring multiple perspectives, disciplines and sectors together around a challenge or opportunity. That's why UK Research and Innovation (UKRI) was formed, and Innovate UK is part of it. It invests over £7 billion a year in research and innovation by partnering with academia, industry and government to make the impossible, possible. UKRI will ensure the UK's research and innovation system is fit for the future and able to respond to environmental, social and economic change on a global scale by: This brings together researchers and innovators across disciplines and sectors including engineering and physical sciences, arts, humanities and social sciences, the natural environment, biological sciences, among many others.