Gender bias perpetuation and mitigation in AI technologies

Challenges and opportunities

Datos Bibliográficos

ID20399151
AutoresSinéad O’connor (0000-0001-7480-0260, National Taiwan University), Helen K Liu (0000-0003-1968-2171, National Taiwan University, autor de correspondencia)
Año2024
Volumen39
Número4
Páginas2045-2057
Fecha de publicación2024-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaAI & Society (JOURNAL)
Identificadores de la revistaISSN: 0951-5666 • E-ISSN: 1435-5655
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s00146-023-01675-4
OpenAlexW4382293074
IdiomaEN
Citas recibidas34
Referencias citadas33

Across the world, artificial intelligence (AI) technologies are being more widely employed in public sector decision-making and processes as a supposedly neutral and an efficient method for optimizing delivery of services. However, the deployment of these technologies has also prompted investigation into the potentially unanticipated consequences of their introduction, to both positive and negative ends. This paper chooses to focus specifically on the relationship between gender bias and AI, exploring claims of the neutrality of such technologies and how its understanding of bias could influence policy and outcomes. Building on a rich seam of literature from both technological and sociological fields, this article constructs an original framework through which to analyse both the perpetuation and mitigation of gender biases, choosing to categorize AI technologies based on whether their input is text or images. Through the close analysis and pairing of four case studies, the paper thus unites two often disparate approaches to the investigation of bias in technology, revealing the large and varied potential for AI to echo and even amplify existing human bias, while acknowledging the important role AI itself can play in reducing or reversing these effects. The conclusion calls for further collaboration between scholars from the worlds of technology, gender studies and public policy in fully exploring algorithmic accountability as well as in accurately and transparently exploring the potential consequences of the introduction of AI technologies

Accountability · Categorization · Data science · Emerging technologies · Neutrality · Political science · Sociology · Software deployment · Computer Science · Ethics and Social Impacts of AI · Law · Sex and Gender in Healthcare · Artificial Intelligence