Organizations often release and receive medical data with all explicit identifiers, such as name, address, telephone number, and Social Security number, removed on the assumption that patient confidentiality is maintained because the resulting data look anonymous. However, in most of these cases, the remaining data can be used to reidenafy individuals by linking or matching the data to other data bases or by looking at unique characteristics found in the fields and records of the data base itself. When these less (...) apparent aspects are taken into account, each released record can map to many possible people, providing a level of anonymity that the recordholder determines. The greater the number of candidates per record, the more anonymous the data.I examine three general-purpose computer programs for maintaining patient confidentiality when disclosing electronic medical records: the Scrub System, which locates and suppresses or replaces personally identifying information in letters between doctors and in notes written by clinicians; the Datafly System, which generalizes values based on a profile of the data recipient at the time of disclosure; and the μ-Argus System, a somewhat similar system which is becoming a European standard for disclosing public use data. (shrink)

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...) secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable (...) and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)