Some say allowing artificial intelligence (AI) to determine guilt or innocence in a courtroom is a step too far. But for those who are sceptical about the neutrality of human judgment, or have witnessed an unfair justice system in action, AI and legal robots could be the answer to providing a fair and impartial jury. We already automate so much else in society, so why not extend this smart automation to juries? After all, lawyers rely on technology to scan documents for keywords or evaluate collected data. And people can now use legal chatbots to determine if it's worthwhile to pursue their case in court.

The Electronic Privacy Information Center (EPIC) has asked the Federal Trade Commission to investigate HireVue, an AI tool that helps companies figure out which workers to hire. HireVue is one of a growing number of artificial intelligence tools that companies use to assess job applicants. The algorithm analyzes video interviews, using everything from word choice to facial movements to figure out an "employability score" that is compared against that of other applicants. More than 100 companies have already used it on over a million applicants, according to the Washington Post. It's hard to predict which workers will be successful from things like facial expressions. Worse, critics worry that the algorithm is trained on limited data and so will be more likely to mark "traditional" applicants (white, male) as more employable.

Despite the growing use of artificial-intelligence tools on a global basis, there is no universal code of ethics to govern its use. This is a key question the technology industry is beginning to wrestle with as the use of AI generates results both positive and negative. The technology has already been used for positive outcomes in a number of areas, including improving Australia's beaches, delivering reliable weather forecasts, and detecting human disease more accurately. There is also the other side of the coin, where AI has come under fire for injecting racial bias into criminal sentencing decisions and reinforcing gender discrimination. AI-powered facial recognition tools have been subjected to especially harsh criticism by privacy and human rights organizations.

A major new effort is underway to use modern technology to fight human trafficking. It's a tool that may help clamp down on a growing problem that crisscrosses the globe. Human trafficking is an estimated $150 billion business with as many as 40 million victims worldwide. It's a big, evil business with human trafficking victims described as modern-day slaves. "They don't get to make the most basic decisions about their lives," said John Richmond, US ambassador-at-large to monitor and combat human trafficking.

Since 1980, the temperature of the planet has risen by 0.8 degrees Celsius, resulting in unprecedented melting of the Greenland ice sheet and the acidification of oceans. In 2015, 175 million more people were exposed to heat waves compared with the average for 1986 to 2008, and the number of weather-related disasters from 2007 to 2016 was up by 46 percent compared with the average from 1990 to 1999. This is nothing in comparison to the horrors that await us as temperatures continue to rise. According to recent projections, global temperatures are set to increase by 3.2 degrees by the end of century. This will lock in sea level rises that will mean that the cities, towns, and villages currently occupied by 175 million people--including Hong Kong and Miami--will eventually be underwater. There is overwhelming scientific evidence that warming is largely caused by the actions of human beings.

Abstract: As machine learning black boxes are increasingly being deployed in domains such as healthcare and criminal justice, there is growing emphasis on building tools and techniques for explaining these black boxes in an interpretable manner. Such explanations are being leveraged by domain experts to diagnose systematic errors and underlying biases of black boxes. In this paper, we demonstrate that post hoc explanations techniques that rely on input perturbations, such as LIME and SHAP, are not reliable. Specifically, we propose a novel scaffolding technique that effectively hides the biases of any given classifier by allowing an adversarial entity to craft an arbitrary desired explanation. Our approach can be used to scaffold any biased classifier in such a way that its predictions on the input data distribution still remain biased, but the post hoc explanations of the scaffolded classifier look innocuous.

The paper investigates the second-order blameworthiness or duty to warn modality "one coalition knew how another coalition could have prevented an outcome". The main technical result is a sound and complete logical system that describes the interplay between the distributed knowledge and the duty to warn modalities.

The debate on AI ethics largely focuses on technical improve ments and stronger regulation to prevent accidents or misuse of AI, with soluti ons relying on holding individual actors accountable for responsible AI devel opment. While useful and necessary, we argue that this "agency" approach disrega rds more indirect and complex risks resulting from AI's interaction with the soci o-economic and political context. This paper calls for a "structural" approach to assessing AI's effects in order to understand and prevent such systemic risks where no individual can be held accountable for the broader negative impacts. This i s particularly relevant for AI applied to systemic issues such as climate change and f ood security which require political solutions and global cooperation. To pro perly address the wide range of AI risks and ensure'AI for social good', agency-foc used policies must be complemented by policies informed by a structural approa ch.

We have actual people being affected by these algorithms. We see things in the news such as algorithms that predict recidivism -- whether someone will re-commit a particular crime -- and set a bail amount or pass that information on to a judge who decides whether or not to set bail. The algorithms used to make these predictions end up relying on correlations with socioeconomic status, or race, or gender. So someone who might have a very similar background to you but differs across race or gender might have a very different outcome because of what the algorithm predicts. Do you think people are generally aware of the degree to which these algorithms are already part of everyday life?

The unique nature of the Science article is its reference to race. Doing scientific work to test healthcare algorithms is difficult. Many are concealed behind a wall of intellectual property protection. They may be trade secrets, and unlike patents, unavailable to the public. Discussing the methodologies used to do this is beyond the scope of this article.