Understanding Harm Generated by Analytic Technique Bias from the Afro-communitarian Perspective

Area of Science:

• Ethics of Artificial Intelligence
• Health Informatics
• Social Philosophy

Background:

• Analytic techniques, driven by training data and human actions, risk introducing bias.
• This bias can manifest as distinct harms, including isolation, minimization, and essentialism.
• The healthcare sector is poised to be a major adopter of these analytic techniques.

Purpose of the Study:

• To identify and define three forms of harm stemming from analytic technique bias: isolation/alienation, minimization, and essentialism.
• To ground these harms within Afro-communitarian thinking, utilizing concepts of exclusivism, (transformative) inclusivism, and incompleteness/conviviality.
• To propose a framework for understanding and mitigating bias in analytic techniques within healthcare.

Main Methods:

• Normative ethical analysis.
• Conceptual framework development drawing on Afro-communitarian philosophy.
• Identification of potential harms linked to algorithmic bias.

Main Results:

• Three distinct harms—isolation/alienation, minimization, and essentialism—are identified as potential consequences of analytic technique bias.
• Afro-communitarian concepts provide a lens to understand the nature and origins of these harms.
• The study highlights the critical need for ethical considerations in the development and deployment of analytic techniques in healthcare.

Conclusions:

• Analytic technique bias poses significant ethical challenges, particularly in healthcare.
• Further research is needed to explore how diverse moral frameworks can illuminate the specific harms of analytic techniques and guide prevention strategies.
• Integrating ethical analysis with technical development is crucial for responsible innovation in AI-driven healthcare.