AI Ethics, Ethics Washing, and the Need to Politicize Data Ethics

Area of Science:

• Tech Ethics
• Data Ethics
• Sociology of Technology

Background:

• Commercial tech actors often use ethics guidelines to deflect criticism.
• Concerns exist about the effectiveness of traditional ethical theories in regulating big tech.
• Critiques of 'ethics washing' risk oversimplifying ethical considerations.

Purpose of the Study:

• To analyze the limitations of current ethical approaches in the tech sector.
• To propose an alternative, question-based ethical data practice.
• To integrate historical and narrative 'exemplars' into data ethics.

Main Methods:

• Critique of principle- and theory-based ethical frameworks.
• Application of casuistry and insights from political philosophy (Raymond Geuss).
• Emphasis on in-depth understanding of socio-political phenomena in tech development.

Main Results:

• Dominant ethical theories lack resistance to exploitation by powerful tech actors.
• A question-based approach, focusing on practical understanding, is more effective.
• Learning from 'exemplars' (stories) enhances ethical analysis and practice.

Conclusions:

• Re-evaluating ethical practices in big tech is crucial.
• Shifting from abstract principles to practical, question-driven inquiry is necessary.
• Integrating narrative and historical context strengthens data ethics and its repoliticization.