Includes bibliographical references (pages 285- 309) and index.
Contents:
When is automated decision making legitimate? -- Classification -- Relative notions of fairness -- Causality -- Understanding United States anti-discrimination law -- Testing discrimination in practice -- A broader view of discrimination -- Datasets.
Summary:
"This book offers a critical view on the current practice of machine learning, as well as proposed technical fixes for achieving fairness in automated decisionmaking"-- Provided by publisher.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.