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Big Data's Disparate Impact
Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with.
Fairness and Abstraction in Sociotechnical Systems
This paper outlines this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science and suggests ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions.
The hidden assumptions behind counterfactual explanations and principal reasons
It is demonstrated that the utility of feature-highlighting explanations relies on a number of easily overlooked assumptions, including that the recommended change in feature values clearly maps to real-world actions, that features can be made commensurate by looking only at the distribution of the training data, and that features are only relevant to the decision at hand.
The Intuitive Appeal of Explainable Machines
It is shown that machine learning models can be both inscrutable and nonintuitive and that these are related, but distinct, properties.
"Meaningful Information" and the Right to Explanation
There is no single, neat statutory provision labeled the “right to explanation” in Europe’s new General Data Protection Regulation (GDPR). But nor is such a right illusory. Responding to two
Disparate Impact in Big Data Policing
Police departments large and small have begun to use data mining techniques to predict the where, when, and who of crime before it occurs. But data mining systems can have a disproportionately
Contextual Expectations of Privacy
Fourth Amendment search jurisprudence is nominally based on a “reasonable expectation of privacy,” but actual doctrine is disconnected from society’s conception of privacy. Courts rely on various
Negligence and AI's Human Users
Though there might be a way to create systems of regulatory support to allow negligence law to operate as intended, an approach to oversight that it not based in individual fault is likely to be a more fruitful approach.
A Mild Defense of Our New Machine Overlords
We must make policy based on realistic ideas about how machines work. In Plausible Cause, Kiel Brennan-Marquez argues first that "probable cause" is about explanation rather than probability, and
Clock Division as a Power Saving Strategy in a System Constrained by High Transmission Frequency and Low Data Rate
This thesis proposes to slow the internal operating frequency of a cochlear implant receiver in order to reduce the internal power consumption by more than a factor of ten, and creates a new data encoding scheme, called "N-7r Shift Encoding", which makes clock division a viable solution.