A Bias Network Approach (BNA) to Encourage Ethical Reflection Among AI Developers: A Bias Network Approach (BNA) to Encourage Ethical Reflection...: G. Arriagada et al.

We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are s...

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Publicado en:Science & Engineering Ethics Vol. 31; no. 1; pp. 1 - 30
Autores principales: Arriagada-Bruneau, Gabriela, López, Claudia, Davidoff, Alexandra
Formato: Artículo
Publicado: Springer Nature Feb2025
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: A Bias Network Approach (BNA) to Encourage Ethical Reflection Among AI Developers: A Bias Network Approach (BNA) to Encourage Ethical Reflection...: G. Arriagada et al.
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          Arriagada-Bruneau, Gabriela
          López, Claudia
          Davidoff, Alexandra
        affil:
          https://ror.org/04teye511 Instituto de Éticas Aplicadas, Instituto de Ingeniería Matemática y Computacional, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna, 4860, Santiago, Chile
          Centro Nacional de Inteligencia Artificial (CENIA), Santiago, Chile
          https://ror.org/05510vn56 Departamento de Informática, Universidad Técnica Federico Santa María, Avenida España, 1680, Valparaíso, Chile
          Sociology of Childhood and Children's Rights, Social Research Institute, UCL. 20 Bedford Way, London, UK
          Nucleo Futures of Artificial Intelligence Research (FAIR), Santiago, Chile
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        Professional ethics
        Artificial intelligence
        Pilot projects
        Decision making
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          Professional ethics
          Artificial intelligence
          Pilot projects
          Decision making
      keyword:
        AI bias
        AI ethics
        Decision-making
        Professional bias
        Sociotechnical
      ab: We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are seen as separate occurrences linked to specific stages in an AI pipeline. Dealing with these multiple biases can trigger a sense of excessive overload in managing each potential bias individually or promote the adoption of an uncritical approach to understanding the influence of biases in developers' decision-making. The BNA fosters dialogue and a critical stance among developers, guided by external experts, using graphical representations to depict biased connections. To test the BNA, we conducted a pilot case study on the "waiting list" project, involving a small AI developer team creating a healthcare waiting list NPL model in Chile. The analysis showed promising findings: (i) the BNA aids in visualizing interconnected biases and their impacts, facilitating ethical reflection in a more accessible way; (ii) it promotes transparency in decision-making throughout AI development; and (iii) more focus is necessary on professional biases and material limitations as sources of bias in AI development.
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      doctype: Article
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    language: English
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