59,910 research outputs found
The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
The nascent field of fair machine learning aims to ensure that decisions
guided by algorithms are equitable. Over the last several years, three formal
definitions of fairness have gained prominence: (1) anti-classification,
meaning that protected attributes---like race, gender, and their proxies---are
not explicitly used to make decisions; (2) classification parity, meaning that
common measures of predictive performance (e.g., false positive and false
negative rates) are equal across groups defined by the protected attributes;
and (3) calibration, meaning that conditional on risk estimates, outcomes are
independent of protected attributes. Here we show that all three of these
fairness definitions suffer from significant statistical limitations. Requiring
anti-classification or classification parity can, perversely, harm the very
groups they were designed to protect; and calibration, though generally
desirable, provides little guarantee that decisions are equitable. In contrast
to these formal fairness criteria, we argue that it is often preferable to
treat similarly risky people similarly, based on the most statistically
accurate estimates of risk that one can produce. Such a strategy, while not
universally applicable, often aligns well with policy objectives; notably, this
strategy will typically violate both anti-classification and classification
parity. In practice, it requires significant effort to construct suitable risk
estimates. One must carefully define and measure the targets of prediction to
avoid retrenching biases in the data. But, importantly, one cannot generally
address these difficulties by requiring that algorithms satisfy popular
mathematical formalizations of fairness. By highlighting these challenges in
the foundation of fair machine learning, we hope to help researchers and
practitioners productively advance the area
Good Faith Discrimination
The Supreme Court\u27s current doctrinal rules governing racial discrimination and affirmative action are unsatisfying. They often seem artificial, internally inconsistent, and even conceptually incoherent. Despite a long and continuing history of racial discrimination in the United States, many of the problems with the Supreme Court\u27s racial jurisprudence stem from the Court\u27s willingness to view the current distribution of societal resources as establishing a colorblind, race-neutral baseline that can be used to make equality determinations. As a result, the current rules are as likely to facilitate racial discrimination as to prevent it, or to remedy the lingering effects of past discrimination
European Union regulations on algorithmic decision-making and a "right to explanation"
We summarize the potential impact that the European Union's new General Data
Protection Regulation will have on the routine use of machine learning
algorithms. Slated to take effect as law across the EU in 2018, it will
restrict automated individual decision-making (that is, algorithms that make
decisions based on user-level predictors) which "significantly affect" users.
The law will also effectively create a "right to explanation," whereby a user
can ask for an explanation of an algorithmic decision that was made about them.
We argue that while this law will pose large challenges for industry, it
highlights opportunities for computer scientists to take the lead in designing
algorithms and evaluation frameworks which avoid discrimination and enable
explanation.Comment: presented at 2016 ICML Workshop on Human Interpretability in Machine
Learning (WHI 2016), New York, N
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