Today, AI is getting adopted in everyday life and now it is more important to ensure that decisions that have been taken using AI are not reflecting discriminatory behavior towards a set of populations. It is important to take fairness into consideration while consuming the output from AI. A quote from "The Guardian" has summarized it very well – "Although neural networks might be said to write their own programs, they do so towards goals set by humans, using data collected for human purposes. If the data is skewed, even by accident, the computers will amplify injustice." Discrimination towards a sub-population can be created unintentionally and unknowingly but while the deployment of any AI solution, a check on bias is imperative.

Gary M. Shiffman, Ph.D. is the Founder and CEO of Giant Oak and Co-Founder and CEO of Consilient. He is the creator of GOST and Dozer. According to my company's research, a full 25% of PPP fraud cases brought by the Department of Justice could have been easily prevented. The fraud is so obviously clumsy that it is embarrassing to whomever approved the loans. Decision-makers consume a lot of data.

Real-world applications of machine learning tools in high-stakes domains are often regulated to be fair, in the sense that the predicted target should satisfy some quantitative notion of parity with respect to a protected attribute. However, the exact tradeoff between fairness and accuracy with a real-valued target is not clear. In this paper, we characterize the inherent tradeoff between statistical parity and accuracy in the regression setting by providing a lower bound on the error of any fair regressor. Our lower bound is sharp, algorithm-independent, and admits a simple interpretation: when the moments of the target differ between groups, any fair algorithm has to make a large error on at least one of the groups. We further extend this result to give a lower bound on the joint error of any (approximately) fair algorithm, using the Wasserstein distance to measure the quality of the approximation. On the upside, we establish the first connection between individual fairness, accuracy parity, and the Wasserstein distance by showing that if a regressor is individually fair, it also approximately verifies the accuracy parity, where the gap is given by the Wasserstein distance between the two groups. Inspired by our theoretical results, we develop a practical algorithm for fair regression through the lens of representation learning, and conduct experiments on a real-world dataset to corroborate our findings.

Facial recognition is facing a showdown in Congress. U.S. Sens. Edward Markey and Elizabeth Warren are joined by U.S. Rep. Ayanna Pressley in reintroducing legislation to tamp down on the government's use of biometric technology, which includes facial recognition. Executive Director of ACLU Massachusetts Kate Ruane said people shouldn't worry "that government agencies are keeping tabs on their every movement." The bill would, under almost any circumstance, make it illegal for any federal agent or official to "acquire, possess, access or use" any biometric surveillance system. This includes facial recognition, voice recognition, gait recognition and "other immutable characteristic(s)," according to the bill.

UserWay, the leading web accessibility company, announced today the release of its AI-Powered Accessibility Widget in the Shopify app store. UserWay's widget automatically solves accessibility issues on websites to create a more inclusive and accessible user experience. By embedding the UserWay widget in their sites, Shopify store owners ensure that their shops are inclusive of the more than one billion people living with disabilities. Creating websites that are fully accessible is challenging, requiring a high level of effort and expertise to cover hundreds of accessibility guidelines. A recent analysis of the top one million websites detected failures to meet the Web Content Accessibility Guidelines (WCAG) in more than 97% of home pages.

To answer today's problems, our research centre is dedicated to anticipating the challenges that European businesses face. To help doctors detect tumours in a patient, we built a model combining 2D and 3D convolutional neural networks in collaboration with the Institute Carnot CALYM. Computer vision is one of the most active research areas and applies to many use cases in different sectors. In the field of healthcare, AI tools can assist doctors in their decision-making. Our alumna Cécile Pereira worked at Eura Nova Marseille on a pipeline that builds navigable pathways aligned with the researcher's needs.

In April, the European Commission released a wide-ranging proposed regulation to govern the design, development, and deployment of A.I. systems. The regulation stipulates that "high-risk A.I. systems" (such as facial recognition and algorithms that determine eligibility for public benefits) should be designed to allow for oversight by humans who will be tasked with preventing or minimizing risks. Often expressed as the "human-in-the-loop" solution, this approach of human oversight over A.I. is rapidly becoming a staple in A.I. policy proposals globally. And although placing humans back in the "loop" of A.I. seems reassuring, this approach is instead "loopy" in a different sense: It rests on circular logic that offers false comfort and distracts from inherently harmful uses of automated systems. A.I. is celebrated for its superior accuracy, efficiency, and objectivity in comparison to humans.

Chief Technology Officer at Integrity Management Services, Inc., where she is leading cutting-edge technology solutions (AI) for clients. Artificial intelligence is ubiquitous today. Most of us do not know where AI is being used and are unaware of the biased decisions that some of these algorithms produce. There are AI tools that claim to infer "criminality" from face images, race from facial expressions and emotion recognition through eye movements. Many of these technologies are increasingly used in applications that impact credit card checks, fraud detection, criminal justice decisions, hiring practices, healthcare outcomes, spreading misinformation, education, lifestyle decisions and more.

Indeed, there has been a slew of research projects making a wide variety of predictions about automation-caused job loss, but those predictions differ by tens of millions of jobs, even when comparing similar time frames. This is irresponsible as new legislation might use any one of these predictions as a base for new laws, and ought to be using accurate calculations. In fact, most workers should not be in full panic territory just yet: automation will come in three distinct waves, and we're only riding the first one. Data analysis and theoretically simple digital tasks are already becoming obsolete thanks to the creation of "basic" A.Is trained through machine learning, but this is unlikely to go much further in the next couple of years.

Artificial Intelligence (AI) started out with an ambition to reproduce the human mind, but, as the sheer scale of that ambition became manifest, it quickly retreated into either studying specialized intelligent behaviours, or proposing over-arching architectural concepts for interfacing specialized intelligent behaviour components, conceived of as agents in a kind of organization. This agent-based modeling paradigm, in turn, proves to have interesting applications in understanding, simulating, and predicting the behaviour of social and legal structures on an aggregate level. For these reasons, this chapter examines a number of relevant cross-cutting concerns, conceptualizations, modeling problems and design challenges in large-scale distributed Artificial Intelligence, as well as in institutional systems, and identifies potential grounds for novel advances.