Empirical AI Ethics: Reconfiguring Ethics towards a Situated, Plural, and Transformative Approach

2025-09-22

AI Summary

Dominant AI ethics frameworks rely on top-down, principle-driven approaches that overlook power asymmetries and sociotechnical diversity in practice, often devolving into “ethics washing” that legitimizes corporate interests. Method: Drawing on Science and Technology Studies (STS), this paper critically deconstructs the limitations of vertical (regulatory) and horizontal (consensus-based) models in AI ethics discourse, advocating a threefold reconceptualization: (1) empirical investigation over normative deduction; (2) pluriversal, cross-ontological knowledge practices over monolithic ethical authority; and (3) socially transformative orientation over value-neutral technicism. Contribution/Results: The paper proposes a situated AI ethics framework that acknowledges differential politics, centers social justice, and fosters democratic technology co-governance. Grounded in concrete sociotechnical contexts rather than abstract universals, this framework advances what the authors term “technologies of hope”—practices that prioritize emancipatory outcomes, epistemic pluralism, and collective agency in AI development and deployment.

Technology Category

• Philosophy and Ethics of AI: Societal Impact of AI
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Mainstream AI ethics, with its reliance on top-down, principle-driven frameworks, fails to account for the situated realities of diverse communities affected by AI (Artificial Intelligence). Critics have argued that AI ethics frequently serves corporate interests through practices of 'ethics washing', operating more as a tool for public relations than as a means of preventing harm or advancing the common good. As a result, growing scepticism among critical scholars has cast the field as complicit in sustaining harmful systems rather than challenging or transforming them. In response, this paper adopts a Science and Technology Studies (STS) perspective to critically interrogate the field of AI ethics. It hence applies the same analytic tools STS has long directed at disciplines such as biology, medicine, and statistics to ethics. This perspective reveals a core tension between vertical (top-down, principle-based) and horizontal (risk-mitigating, implementation-oriented) approaches to ethics. By tracing how these models have shaped the discourse, we show how both fall short in addressing the complexities of AI as a socio-technical assemblage, embedded in practice and entangled with power. To move beyond these limitations, we propose a threefold reorientation of AI ethics. First, we call for a shift in foundations: from top-down abstraction to empirical grounding. Second, we advocate for pluralisation: moving beyond Western-centric frameworks toward a multiplicity of onto-epistemic perspectives. Finally, we outline strategies for reconfiguring AI ethics as a transformative force, moving from narrow paradigms of risk mitigation toward co-creating technologies of hope.

Problem

• Mainstream AI ethics fails to address diverse communities' situated realities
• AI ethics often serves corporate interests through ethics washing practices
• Current approaches cannot handle AI's complexities as socio-technical systems

Innovation

• Shift from top-down abstraction to empirical grounding
• Advocate pluralization beyond Western-centric frameworks
• Reconfigure ethics as transformative co-creation force