Bireysel Sorumluluktan Kolektif Failliğe: Yapay Zekâ Etiğinde Dönüşen Etik Paradigmalar

Today, the ethical problems raised by artificial intelligence technologies are predominantly addressed within the framework of individual decisions, intentions, and user choices. However, the current stage of digital transformation increasingly challenges the explanatory power of this individual-centered ethical approach. What we are confronted with today is not a single piece of software or algorithm, but rather a large-scale digital ecosystem sustained and continuously reproduced through the everyday interactions of billions of people. This situation makes it increasingly problematic to evaluate AI ethics solely in terms of individual moral agency. The point of departure of this paper is the observation that dominant individual-centered conceptions of responsibility in AI ethics tend to obscure the structural, collective, and sustainability-related dimensions of digital systems. While data and interactions produced by users, often without their explicit awareness, directly contribute to algorithmic learning processes and dynamics of power accumulation, framing ethical responsibility exclusively in terms of individual good or bad intentions risks concealing these structural problems. From this perspective, the paper argues that the framework of twin transformation, which brings digital transformation and sustainability debates into a common analytical horizon, provides a more comprehensive basis for rethinking AI ethics. The aim of this study is to move AI ethics beyond the axis of individual moral obligations and to reconceptualize it within a framework of collective responsibility. To this end, Rainer Mühlhoff’s concept of “Human-Aided Artificial Intelligence” is adopted as the central theoretical framework. According to Mühlhoff, contemporary AI systems become operational through active yet often unrecognized user contributions; humans are not merely passive users of these systems, but subjects who continuously feed, sustain, and reproduce them. This perspective enables a critical examination of individualizing conceptions of responsibility that reduce ethical accountability to the level of individual users. Through a philosophical and conceptual analysis, the paper highlights structural parallels between AI ethics and debates on the climate crisis and sustainability. These parallels make visible the gap between individual actions and the cumulative, destructive effects they produce in both domains. The analysis demonstrates that ethical challenges in AI cannot be adequately addressed within a framework limited to individual intentions and preferences; rather, they constitute issues that require collective action, ethical governance, and structural transformation. Accordingly, the paper argues that a genuinely transformative and sustainable approach to AI ethics depends on rethinking users not merely as data providers, but as moral agents capable of assuming collective responsibility.