Two employees of controversial facial recognition startup Clearview AI have been found to have ties to white nationalism, according to an exhaustive report by HuffPost published on Tuesday. The report found that one investigator for the company belonged to a white nationalist group based in Washington, DC who continued to work for the company as recently as last month. Another employee had enthusiastically endorsed "Islamophobia, Eurocentrism and anti-Semitism" in online writings in 2015. Reached by The Verge, Clearview CEO Hoan Ton-That said he was unaware of the online writings and that neither employee remains with the company. "I am not a white supremacist or an anti-semite, nor am I sympathetic to any of those views," Ton-That said in a statement.

There is no denying that in the court of law, any mistake in communication can end up turning a case over its head. When there are inaccuracies with the recording of statements and audio files, it can be quite easy for a case to become more confusing than it should be. The worst part is that a case could end up punishing the wrong people. In an industry that often deals with life and death, it is no wonder why legal transcriptionists are highly valued. There are even advancements being made where AI is utilized to take over in courtroom transcription.

We propose learning discrete structured representations from unlabeled data by maximizing the mutual information between a structured latent variable and a target variable. Calculating mutual information is intractable in this setting. Our key technical contribution is an adversarial objective that can be used to tractably estimate mutual information assuming only the feasibility of cross entropy calculation. We develop a concrete realization of this general formulation with Markov distributions over binary encodings. We report critical and unexpected findings on practical aspects of the objective such as the choice of variational priors. We apply our model on document hashing and show that it outperforms current best baselines based on discrete and vector quantized variational autoencoders. It also yields highly compressed interpretable representations.

There exist several inherent trade-offs in designing a fair model, such as those between the model's predictive performance and fairness, or even among different notions of fairness. In practice, exploring these trade-offs requires significant human and computational resources. We propose a diagnostic that enables practitioners to explore these trade-offs without training a single model. Our work hinges on the observation that many widely-used fairness definitions can be expressed via the fairness-confusion tensor, an object obtained by splitting the traditional confusion matrix according to protected data attributes. Optimizing accuracy and fairness objectives directly over the elements in this tensor yields a data-dependent yet model-agnostic way of understanding several types of trade-offs. We further leverage this tensor-based perspective to generalize existing theoretical impossibility results to a wider range of fairness definitions. Finally, we demonstrate the usefulness of the proposed diagnostic on synthetic and real datasets.

A trio of Princeton social scientists recently conducted a mass experiment with 160 research teams to see if any of them could predict how children's lives would turn out. The participants were given fifteen years of data and were allowed to use any technique they wanted, from good old fashioned statistical analysis to modern-day artificial intelligence. That's because artificial intelligence – much like psychics and headless chickens – cannot predict the future. Sure, it can predict trends and in some cases provide valuable insights that can help industries make the best decisions, but determining whether or not a child will become successful requires a level of prescience that brute-force mathematics can't provide. We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study.

Companies, organizations, and governments around the world are implementing or endorsing principles for artificial intelligence. During the Berkman Klein Center's first fully-virtual Tuesday luncheon, Jessica Fjeld, Assistant Director of BKC's Cyberlaw Clinic and lead author of the Principled AI report, and Ryan Budish, Assistant Research Director at BKC and a member of the Organisation for Economic Co-operation and Development (OECD)'s AI Governance Expert Group that came up with principles for OECD's AI Principles, teamed up to share their experiences. Their discussion was moderated by BKC Executive Director Urs Gasser and also featured commentary from members of the BKC community. Fjeld explained the method of creating the Principled AI report and visualization, which analyzed AI principles from around the world and ultimately identified eight trends among them. "We believe [these themes] are the signs of the earliest emerging consensus for societal norms around how AI can be -- should be -- used," she said.

The publication titled as "ARTIFICIAL INTELLIGENCE: AN INDUCEMENT OF TECHNOLOGY IN HUMAN AFFAIRS" is an attempt to explore into the study of Artificial Intelligence and its numerous facets. The book contains scholarly articles from students, research scholars, academicians and experts from different fields medical, engineering, management etc. who have endeavoured to sightsee the various aspects of Artificial Intelligence, its use and effectiveness in the present times. This book aims to give its readers an insightful study of the contribution and development of Artificial Intelligence in other multidisciplinary disciplines like law, medical health care, management, history, economics, social sciences etc. The introductory and several other chapters give the historical perspective, definition, scope, aim and objective of Artificial Intelligence. Then there are few chapters which relate intelligence with medical science, physiology, medical robotics, etc.

Many organisations have established an AI policy. Some companies, such as IBM1, Google2 and others have made these available online. Here, we propose ten - largely risk-based - considerations that synthesise the various societal, legal, ethical and engineering challenges that organisations need to consider in developing an AI system. In a recent report, Accenture reported that 63% of AI adopters had an ethics committee3. Establishing an AI ethics committee to oversee the use of AI will ensure adherence to the law, promote best practice, oversee risk and provide authority for periodic audit.

A future driven by artificial intelligence (AI) is often depicted as one paved with improvements across every aspect of life--from health, to jobs, to how we connect. But cracks in this utopia are starting to appear, particularly as we glimpse how AI can also be used to surveil, discriminate, and cause other harms. What existing legal frameworks can protect us from the dark side of this brave new world of technology? Facial recognition is a good example of an AI-driven technology that is starting to have a dramatic human impact. When facial recognition is used to unlock a smartphone, the risk of harm is low, but the stakes are much higher when it is used for policing.

Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world. A critical question then recently arised among the population: Do algorithmic decisions convey any type of discrimination against specific groups of population or minorities? In this paper, we show the importance of understanding how a bias can be introduced into automatic decisions. We first present a mathematical framework for the fair learning problem, specifically in the binary classification setting. We then propose to quantify the presence of bias by using the standard Disparate Impact index on the real and well-known Adult income data set. Finally, we check the performance of different approaches aiming to reduce the bias in binary classification outcomes. Importantly, we show that some intuitive methods are ineffective. This sheds light on the fact trying to make fair machine learning models may be a particularly challenging task, in particular when the training observations contain a bias.