"mathwashing" meaning in All languages combined

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  "lang_code": "en",
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        {
          "ref": "2016 June 24, Nanette Byrnes, “Why We Should Expect Algorithms to Be Biased”, in MIT Technology Review, Cambridge, M.A.: Massachusetts Institute of Technology, →ISSN, →OCLC, archived from the original on 2023-10-22",
          "text": "This is just one result, however, of a broader trend that Fred Benenson, Kickstarter's former data chief, calls \"mathwashing\": our tendency to idolize programs like Facebook's as entirely objective because they have mathematics at their core.",
          "type": "quotation"
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        {
          "ref": "2017 December 5, Frida Polli, “The Dark Side Of Artificial Intelligence”, in Forbes, New York, N.Y.: Forbes Media, →ISSN, →OCLC, archived from the original on 2022-11-17",
          "text": "Many of the people and companies employing algorithms hope that their use of technology in replacement of humans results in reduced unconscious bias. While it would be great if it were that simple, this mindset is often a case of \"mathwashing:\" our tendency to attribute objectivity to technology.",
          "type": "quotation"
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          "ref": "2018 January 29, Sophie Kleber, “As AI Meets the Reputation Economy, We're All Being Silently Judged”, in Harvard Business Review, Brighton, M.A.: Harvard Business Publishing, →ISSN, →OCLC, archived from the original on 2023-05-28",
          "text": "Unintended mathwashing occurs when the algorithm is left unchecked, and, learning from historical data, amplifies social bias. The U.S. justice system uses an algorithm called COMPAS to determine a criminal's likelihood to re-offend. COMPAS has been proven by Pro Publica to predict that black defendants will have higher rates of recidivism than they actually do, while white defendants are predicted to have lower rates than they actually do.",
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        "The usage of data and algorithms to create the false impression that a subjective decision or policy was made objectively."
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        [
          "data",
          "data#Noun"
        ],
        [
          "algorithms",
          "algorithm#Noun"
        ],
        [
          "subjective",
          "subjective#Adjective"
        ],
        [
          "decision",
          "decision#Noun"
        ],
        [
          "policy",
          "policy#Noun"
        ],
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          "objectively",
          "objectively#Adjective"
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        "(neologism) The usage of data and algorithms to create the false impression that a subjective decision or policy was made objectively."
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        "present participle and gerund of mathwash"
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  "lang_code": "en",
  "pos": "noun",
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        {
          "ref": "2016 June 24, Nanette Byrnes, “Why We Should Expect Algorithms to Be Biased”, in MIT Technology Review, Cambridge, M.A.: Massachusetts Institute of Technology, →ISSN, →OCLC, archived from the original on 2023-10-22",
          "text": "This is just one result, however, of a broader trend that Fred Benenson, Kickstarter's former data chief, calls \"mathwashing\": our tendency to idolize programs like Facebook's as entirely objective because they have mathematics at their core.",
          "type": "quotation"
        },
        {
          "ref": "2017 December 5, Frida Polli, “The Dark Side Of Artificial Intelligence”, in Forbes, New York, N.Y.: Forbes Media, →ISSN, →OCLC, archived from the original on 2022-11-17",
          "text": "Many of the people and companies employing algorithms hope that their use of technology in replacement of humans results in reduced unconscious bias. While it would be great if it were that simple, this mindset is often a case of \"mathwashing:\" our tendency to attribute objectivity to technology.",
          "type": "quotation"
        },
        {
          "ref": "2018 January 29, Sophie Kleber, “As AI Meets the Reputation Economy, We're All Being Silently Judged”, in Harvard Business Review, Brighton, M.A.: Harvard Business Publishing, →ISSN, →OCLC, archived from the original on 2023-05-28",
          "text": "Unintended mathwashing occurs when the algorithm is left unchecked, and, learning from historical data, amplifies social bias. The U.S. justice system uses an algorithm called COMPAS to determine a criminal's likelihood to re-offend. COMPAS has been proven by Pro Publica to predict that black defendants will have higher rates of recidivism than they actually do, while white defendants are predicted to have lower rates than they actually do.",
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        "The usage of data and algorithms to create the false impression that a subjective decision or policy was made objectively."
      ],
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          "algorithms",
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        ],
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          "decision",
          "decision#Noun"
        ],
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          "policy",
          "policy#Noun"
        ],
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          "objectively",
          "objectively#Adjective"
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        "(neologism) The usage of data and algorithms to create the false impression that a subjective decision or policy was made objectively."
      ],
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  "word": "mathwashing"
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