Debate about fairness in machine learning has largely centered around competing definitions of what fairness or nondiscrimination between groups requires. However, little attention has been paid to what precisely a group is. Many recent approaches to "fairness" require one to specify a causal model of the data generating process. These exercises make an implicit ontological assumption that a racial or sex group is simply a collection of individuals who share a given trait. We show this by exploring the formal assumption of modularity in causal models, which holds that the dependencies captured by one causal pathway are invariant to interventions on any other pathways. Causal models of sex propose two substantive claims: 1) There exists a feature, sex-on-its-own, that is an inherent trait of an individual that causally brings about social phenomena external to it in the world; and 2) the relations between sex and its effects can be modified in whichever ways and the former feature would still retain the meaning that sex has in our world. We argue that this ontological picture is false. Many of the "effects" that sex purportedly "causes" are in fact constitutive features of sex as a social status. They give the social meaning of sex features, meanings that are precisely what make sex discrimination a distinctively morally problematic type of action. Correcting this conceptual error has a number of implications for how models can be used to detect discrimination. Formal diagrams of constitutive relations present an entirely different path toward reasoning about discrimination. Whereas causal diagrams guide the construction of sophisticated modular counterfactuals, constitutive diagrams identify a different kind of counterfactual as central to an inquiry on discrimination: one that asks how the social meaning of a group would be changed if its non-modular features were altered.

In the wake of the George Floyd tragedy and so many other appalling cases like it, there is a growing question if a solution lies with robot police powered by artificial intelligence (AI.) In theory, AI cops could reduce biased and discriminatory practices and improve access to justice. Pop culture is filled with heroes like this such as Robocop and CHAPPiE. However, reality maybe a little stranger than fiction in this case as there are already some robots already in action for law enforcement. Let's start with Robo-Guard, which works in the South Korean prison system.

Cindy Bethel was 6 when her babysitter's neighbor started molesting her. Worried what else would happen if she told her parents, she confided in her stuffed panda instead. Sometimes she acted out the abuse with Barbie and Ken dolls. A few years later, the same teen neighbor raped her on a woodpile outside his house. She didn't tell anyone about the assault until long after she moved away from her Ohio hometown.

Google has been sued in the US over claims it illegally invades the privacy of users by tracking people even when they are browsing in "private mode". The class action wants at least $5bn (£4bn) from Google and owner Alphabet. Many internet users assume their search history isn't being tracked when they view in private mode, but Google says this isn't the case. The search engine denies this is illegal and says it is upfront about the data it collects in this mode. The proposed class action likely includes "millions" of Google users who since 1 June 2016 browsed the internet in private mode according to law firm Boies Schiller Flexner who filed the claim on Tuesday in federal court in San Jose, California.

A convicted paedophile who was snared by a vigilante group is to have his case examined at the UK Supreme Court. Judges at the UK's highest court will consider whether prosecutions based on the covert operations of "paedophile hunters" breach the right to privacy. Mark Sutherland, 37, believed he was communicating with a 13-year-old boy on the dating app Grindr. But in reality it was a 48-year-old man who was part of a group called Groom Resisters Scotland. The Supreme Court will hold a virtual hearing to consider the case and will issue its judgement later.

Recent work in cognitive science has uncovered a diversity of explanatory values, or dimensions along which we judge explanations as better or worse. We propose a Bayesian account of how these values fit together to guide explanation. The resulting taxonomy provides a set of predictors for which explanations people prefer and shows how core values from psychology, statistics, and the philosophy of science emerge from a common mathematical framework. In addition to operationalizing the explanatory virtues associated with, for example, scientific argument-making, this framework also enables us to reinterpret the explanatory vices that drive conspiracy theories, delusions, and extremist ideologies. Intuitively, philosophically, and as seen in laboratory experiments, explanations are judged as better or worse on the basis of many different criteria. These explanatory values appear in early childhood [1, 2, 3, 4, 5] and their influence extends to some of the most sophisticated social knowledge formation processes we know [6]. We lack, however, an understanding of the origin of these values or an account of how they fit together to guide belief formation. The multiplicity of values also appears to conflict with Bayesian models of cognition, which speak solely in terms of degrees of beliefs and suggest we judge explanations as better or worse on the basis of a single quantity, the posterior likelihood (see Glossary). In this opinion, we show how to resolve these conflicts by arguing that previously-identified explanatory values capture different components of a full Bayesian calculation and, when considered together and weighed appropriately, implement Bayesian cognition. This framework shows how key explanatory values identified by laboratory experiments and philosophers of science--co-explanation, descriptiveness, precision, unification, power, and simplicity--emerge naturally from the mathematical structure of probabilistic inference, thereby reconciling them with Bayesian models of cognition [7, 8]. Second, it shows how these values combine to produce preferences for one explanation over another.

The morality of algorithms and their potential for bias and discrimination are important concerns. A popular approach to machine learning and artificial intelligence is via the numerical optimization of objective functions, and adapting such an approach to handle ethics could seem natural: with a hammer in hand, everything looks like a nail. The hammer of much artificial intelligence is the optimization of objective values, so some might like to treat morality solely through such objective functions. However, relying solely on the optimization of scalar objective values is fraught with unavoidable flaws when dealing with real people.

At this extraordinary moment in U.S. history, the evils of racism are on full display. It's no secret that technology has played a role in enabling racism to foment and spread. This is an ideal time to read, listen, and learn. Below are many resources -- research, articles, and books -- that speak to the intersection of race and bias in technology, particularly in the field of AI. These are a starting point for the education that all responsible citizens should acquire.

Grindr is removing an "ethnicity filter" from its dating app as part of its support for the Black Lives Matter movement, the company announced on Monday. The controversial feature, limited to those who stump up £12.99 a month for the premium version of the app, allows users to sort search results based on reported ethnicity, height, weight and other characteristics. In a statement posted to Instagram, the company said "We stand in solidarity with the #BlackLivesMatter movement and the hundreds of thousands of queer people of color who log in to our app every day. "We will continue to fight racism on Grindr, both through dialogue with our community and a zero-tolerance policy for racism and hate speech on our platform. As part of this commitment, and based on your feedback, we have decided to remove the ethnicity filter from our next release.

Dating app Grindr has said it will remove its'ethnicity filter' that allows users to search potential matches by race. Singletons prepared to pay £12.99-a-month for the'premium' service are currently able to sort users based on their ethnicity, weight, height, and other characteristics. But less than 24 hours after its tweet supporting'Black Lives Matter' received widespread condemnation over the filter, the company has said it will delete it. Protests have rocked the US for six days following the death of George Floyd, who was filmed gasping'I can't breathe' as an officer knelt on his neck in Logan County, West Virginia. Writing on Twitter, the app said: 'As part of our commitment to (Black Lives Matter), we have decided to remove the ethnicity filter from our next release.