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      A snapshot of the frontiers of fairness in machine learning

      Author(s): 1 , 2
      Publication date Created:
      Publication date (Print):
      Journal: Communications of the ACM
      Publisher: Association for Computing Machinery (ACM)

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          Abstract

          A group of industry, academic, and government experts convene in Philadelphia to explore the roots of algorithmic bias.

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          Most cited references65

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          Calibrating Noise to Sensitivity in Private Data Analysis

          Cynthia Dwork, Frank McSherry, Kobbi Nissim … (2006)
          Article 0 comments Cited 585 times – based on 0 reviews
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            The Algorithmic Foundations of Differential Privacy

            Cynthia Dwork, Aaron Roth (2014)
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              Semantics derived automatically from language corpora contain human-like biases

              Aylin Caliskan, Joanna Bryson, Arvind Narayanan (2017)
              Machine learning is a means to derive artificial intelligence by discovering patterns in existing data. Here, we show that applying machine learning to ordinary human language results in human-like semantic biases. We replicated a spectrum of known biases, as measured by the Implicit Association Test, using a widely used, purely statistical machine-learning model trained on a standard corpus of text from the World Wide Web. Our results indicate that text corpora contain recoverable and accurate imprints of our historic biases, whether morally neutral as toward insects or flowers, problematic as toward race or gender, or even simply veridical, reflecting the status quo distribution of gender with respect to careers or first names. Our methods hold promise for identifying and addressing sources of bias in culture, including technology.
              Article 0 comments Cited 249 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: Communications of the ACM
                Abbreviated Title: Commun. ACM
                Publisher: Association for Computing Machinery (ACM)
                ISSN (Print): 0001-0782
                ISSN (Electronic): 1557-7317
                Publication date Created: April 20 2020
                Publication date (Print): April 20 2020
                Volume: 63
                Issue: 5
                Pages: 82-89
                Affiliations
                [1 ]Carnegie Mellon University, Pittsburgh, PA
                [2 ]University of Pennsylvania, Philadelphia, PA
                Article
                DOI: 10.1145/3376898
                SO-VID: c01f18aa-b6b0-4c6c-be58-1b64caa3a3cb
                Copyright © © 2020
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