Empirical AI Ethics: Grounded, relational, transformative

Abstract Mainstream artificial intelligence (AI) ethics, with its reliance on top-down, principle-driven frameworks, often fails to account for the situated realities of diverse communities affected by AI. Critics have therefore argued that AI Ethics increasingly serves corporate interests through practices of “ethics washing,” operating as a tool for public relations rather than a means of preventing harm or advancing the common good. In response, this paper adopts a Science and Technology Studies perspective to interrogate AI Ethics itself as a matter of concern. This perspective brings into view a core tension between vertical approaches (external, top-down, principle-based) and horizontal approaches (from-within, risk-mitigating, implementation-oriented). By tracing how these models have shaped the discourse, we show their relevance, as well as their limitations. To move beyond these limitations, we propose a third model that integrates empirical research on local lifeworlds with explicit value commitments. We outline this empirical approach through four shifts: first, a transition from top-down reasoning toward empirical grounding; second, a transition from abstract principles to situated orders of worth; third, a proposal to operate across scales by attending to assemblages of concern; and fourth, a redirection from risk mitigation toward the cultivation of technologies of hope.