AI algorithms employed in everything from hiring to lending to criminal justice have a persistent and often invisible problem with bias. The big picture: One solution could be audits that aim to determine whether an algorithm is working as intended, whether it's disproportionately affecting different groups of people and, if there are problems, how they can be fixed. How it works: Algorithmic audits -- usually conducted by outside companies -- involve examining an algorithm's code and the data used to train it, and assessing its potential impact on populations through interviews with stakeholders and those who might be affected by it. Between the lines: Financial audits exist in part to open up the black box of a company's internal operations to outside investors, and ensure that a company remains in compliance with financial laws and regulations. Details: Algorithmic audits can help companies screen their AI products for flaws that may not be apparent at first glance.

Machine learning algorithms have become prevalent tools for decision-making in socially sensitive situations, such as determining credit-score ratings or predicting recidivism during parole. It has been recognized that algorithms are capable of learning societal biases, for example with respect to race (Larson, Mattu, Kirchner, and Angwin 2016) or gender (Lambrecht and Tucker 2019; Blau and Kahn 2003), and this realization seeded an important debate in the machine learning community about fairness of algorithms and their impact on decision-making. In order to define and measure discrimination, existing intuitive notions have been statistically formalized, thereby providing fairness metrics. For example, demographic parity (Darlington 1971) requires the protected attribute A (gender/race/religion etc.) to be independent of a constructed classifier or regressor Ŷ, written as Ŷ A. Another notion, termed equality of odds (Hardt, Price, Srebro et al. 2016), requires equal false positive and false negative rates of classifier Ŷ between different groups (females and males for example), written as Ŷ A Y. To this day, various different notions of fairness exist, which are sometimes incompatible (Corbett-Davies and Goel 2018), meaning not of all of them can be achieved for a predictor Ŷ simultaneously. There is still no consensus on which notion of fairness is the correct one. The discussion on algorithmic fairness is, however, not restricted to the machine learning domain. There are many legal and philosophical aspects that have arisen. For example, the legal distinction between disparate impact and disparate treatment (McGinley 2011) is important for assessing fairness from a judicial point of view.

Policymakers around the world are developing guidelines for use of artificial intelligence in health care. Baymax, the robotic health aide and unlikely hero from the movie Big Hero 6, is an adorable cartoon character, an outlandish vision of a high-tech future. But underlying Baymax's character is the very realistic concept of an artificial intelligence (AI) system that can be applied to health care. As AI technology advances, how will regulators encourage innovation while protecting patient safety? AI does not have a precise definition, but the term generally describes machines that have the capacity to process and respond to stimulation in a manner similar to human thought processes.

What happens when a machine learning dataset is deprecated for legal, ethical, or technical reasons, but continues to be widely used? In this paper, we examine the public afterlives of several prominent deprecated or redacted datasets, including ImageNet, 80 Million Tiny Images, MS-Celeb-1M, Duke MTMC, Brainwash, and HRT Transgender, in order to inform a framework for more consistent, ethical, and accountable dataset deprecation. Building on prior research, we find that there is a lack of consistency, transparency, and centralized sourcing of information on the deprecation of datasets, and as such, these datasets and their derivatives continue to be cited in papers and circulate online. These datasets that never die -- which we term "zombie datasets" -- continue to inform the design of production-level systems, causing technical, legal, and ethical challenges; in so doing, they risk perpetuating the harms that prompted their supposed withdrawal, including concerns around bias, discrimination, and privacy. Based on this analysis, we propose a Dataset Deprecation Framework that includes considerations of risk, mitigation of impact, appeal mechanisms, timeline, post-deprecation protocol, and publication checks that can be adapted and implemented by the machine learning community. Drawing on work on datasheets and checklists, we further offer two sample dataset deprecation sheets and propose a centralized repository that tracks which datasets have been deprecated and could be incorporated into the publication protocols of venues like NeurIPS.

Slanted news coverage strongly affects public opinion. This is especially true for coverage on politics and related issues, where studies have shown that bias in the news may influence elections and other collective decisions. Due to its viable importance, news coverage has long been studied in the social sciences, resulting in comprehensive models to describe it and effective yet costly methods to analyze it, such as content analysis. We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues. In a large-scale user study, we find very promising results regarding this interdisciplinary research direction. Our recommender detects and reveals substantial frames that are actually present in individual news articles. In contrast, prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets. Further, our study shows that recommending news articles that differently frame an event significantly improves respondents' awareness of bias.

We usually think of surveillance cameras as digital eyes, watching over us or watching out for us, depending on your view. But really, they're more like portholes: useful only when someone is looking through them. Sometimes that means a human watching live footage, usually from multiple video feeds. Most surveillance cameras are passive, however. They're there as a deterrence, or to provide evidence if something goes wrong. But this is changing -- and fast.

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A combination of situational complexity, intractable positions of opposing sides and escalating costs is driving the search for AI-based approaches that could replace humans in resolving legal cases, international disputes and military conflicts. Master of the Rolls and head of civil litigation in England and Wales, Sir Geoffrey Vos, has talked for some time about AI's potential to propose resolutions for humans to ratify. The goal of AI is to develop computer algorithms that replicate the way humans think when processing language, solving problems and analyzing large amounts of data to extract relevant information. The nation is reportedly investing over $400 billion to develop leadership in AI across all domains, and the legal sector is seen as an area where massive efficiencies and financial savings could be achieved by automating significant parts of the judicial process. The third suggested contribution of AI lies in "creating greater inclusivity of mediation processes" -- pulling in the views of a wider cross-section of the affected populations, geographic neighbors of the opposing factions and independent institutions that may have previously played peacekeeping and monitoring roles.

In brief Authorities in the United Arab Emirates have requested the US Department of Justice's help in probing a case involving a bank manager who was swindled into transferring $35m to criminals by someone using a fake AI-generated voice. The employee received a call to move the company-owned funds by someone purporting to be a director from the business. He also previously saw emails that showed the company was planning to use the money for an acquisition, and had hired a lawyer to coordinate the process. When the sham director instructed him to transfer the money, he did so thinking it was a legitimate request. But it was all a scam, according to US court documents reported by Forbes.

Key Takeaways: - New Artificial Intelligence (AI) technology is being integrated into all industries. I have written a few articles regarding the liability of autonomous systems under the United Arab Emirates' (UAE) law, regarding the liability of autonomous systems under the UAE's Civil Code, available remedies, comparing to other regimes, and recommendations for law, policy and ethics. I focused mainly on the liability and regulation of autonomous or Artificial Intelligence (AI) systems under the laws of the UAE, but I also compared the UAE's legal system to other regimes, including the United Kingdom (UK) and the European Union (EU). I concluded that generally speaking, when it comes to AI, the issues are similar across the globe. In the near future, every single one of us will be dealing in some shape or form with an autonomous system or an AI-powered system.