The algorithm audit: Scoring the algorithms that score us
In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2021-01-01
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| Series: | Big Data & Society |
| Online Access: | https://doi.org/10.1177/2053951720983865 |
| _version_ | 1818652609787985920 |
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| author | Shea Brown Jovana Davidovic Ali Hasan |
| author_facet | Shea Brown Jovana Davidovic Ali Hasan |
| author_sort | Shea Brown |
| collection | DOAJ |
| description | In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. |
| first_indexed | 2024-12-17T02:24:44Z |
| format | Article |
| id | doaj.art-362cdd703dd64e5899539b26d1cea0ff |
| institution | Directory Open Access Journal |
| issn | 2053-9517 |
| language | English |
| last_indexed | 2024-12-17T02:24:44Z |
| publishDate | 2021-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Big Data & Society |
| spelling | doaj.art-362cdd703dd64e5899539b26d1cea0ff2022-12-21T22:07:09ZengSAGE PublishingBig Data & Society2053-95172021-01-01810.1177/2053951720983865The algorithm audit: Scoring the algorithms that score usShea BrownJovana DavidovicAli HasanIn recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed.https://doi.org/10.1177/2053951720983865 |
| spellingShingle | Shea Brown Jovana Davidovic Ali Hasan The algorithm audit: Scoring the algorithms that score us Big Data & Society |
| title | The algorithm audit: Scoring the algorithms that score us |
| title_full | The algorithm audit: Scoring the algorithms that score us |
| title_fullStr | The algorithm audit: Scoring the algorithms that score us |
| title_full_unstemmed | The algorithm audit: Scoring the algorithms that score us |
| title_short | The algorithm audit: Scoring the algorithms that score us |
| title_sort | algorithm audit scoring the algorithms that score us |
| url | https://doi.org/10.1177/2053951720983865 |
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