In his 2009 book The Second-Person Standpoint: Morality, Respect, and Accountability, philosopher Stephen Darwall redefines moral obligation as inherently relational and interactive, rooted deeply in mutual accountability and respect among free and rational agents. Central to Darwall’s argument is the concept that morality is not simply a personal or detached observation of right and wrong; rather, it emerges distinctly from second-person interactions. These interactions provide a communicative space where individuals address, demand, and recognize obligations toward each other. Moral obligations are thus not just norms but demands we legitimately place upon one another, supported by what Darwall identifies as "second-personal reasons."

These second-personal reasons derive their authority not from their practical outcomes but from the mutual acknowledgment of each participant’s standing within the moral community. Darwall emphasizes the critical role of "reactive attitudes" such as resentment, gratitude, and indignation, which presuppose that others have the competence and authority to understand and respond to moral demands. The validity of these demands depends inherently on this mutual recognition of agency and authority. Moral accountability, in Darwall’s framework, necessarily involves addressing others (or oneself) with a presumption of shared moral competence.

Although Darwall could not have imagined applying his ideas to artificial intelligence fifteen years ago, I bet he would agree with me that it would be worth doing so now. Much like the reactive attitudes central to Darwall’s theory, interactions with A.I. raise profound questions about accountability, moral recognition, and mutual respect. Contemporary discourse, including Amanda Askell’s critique of AI bias, tocuhes on the practical and epistemic locality challenges of aligning A.I. systems with ethical norms. Askell's framework underscores that A.I. systems inherit ethical limitations from the very human narratives and social structures that train them. But perhaps even more foundational than Askell's concern is Darwall’s core insight: Morality demands genuine second-personal engagement, which is precisely the kind of reciprocal recognition that current A.I. fundamentally lacks.

When humans interact ethically, they engage in mutual second-personal exchanges. These exchanges allow for demands and recognitions of responsibility that affirm each other's dignity. A.I. systems, however sophisticated, are limited in their ability to genuinely participate in these reciprocal moral relationships because they lack authentic second-personal standing. An A.I. can simulate responses, exhibiting linguistic fluency and apparent moral reasoning, but it does not genuinely "recognize" or "respect" authority and obligation. As I suggested in a previous post, linguistic fluency in narratives can obscure deeper ambiguities, substituting specious stories for genuine moral complexity; and A.I. systems, trained exclusively on language and narratives marked by retrospective coherence, exacerbate this issue by amplifying the illusion of decisiveness and ethical clarity.

Applying Darwall’s second-personal standpoint ethics to A.I. development reveals a few key insights. Reactive attitudes like resentment and indignation rely fundamentally on authentic second-personal engagement; they address someone with the expectation that he or she can respond meaningfully. When directed towards A.I., however, such attitudes become incoherent or, at best, metaphorical. The illusion of moral agency in A.I. thus risks not only confusion but also ethical complacency. Users might erroneously assume that fluent, confident A.I. responses reflect genuine ethical authority, obscuring the deeper moral uncertainties and accountability that only authentic second-personal interactions can establish.

Given this ethical predicament, practical solutions to promoting A.I. safety must recognize the inherently limited second-personal capacity of artificial systems. One solution involves explicitly foregrounding the provisional and instrumental nature of A.I.-generated responses. Rather than positioning A.I. as moral authorities or definitive ethical guides, we must frame them as tools for facilitating, not replacing, second-personal human moral deliberation.

Further, organizations developing A.I. should institutionalize mechanisms for continuous ethical oversight and accountability grounded in authentic human relationships. Regular, structured interactions involving ethicists, philosophers, and affected communities can ensure A.I. outputs remain critically assessed rather than passively accepted. Transparency about A.I. training processes, explicit disclaimers about the provisional nature of A.I. responses, and the implementation of red-teaming exercises can help maintain vigilance against the seductive fluency of A.I. narratives.

Moreover, fostering educational initiatives that enhance public understanding of A.I.'s limitations is essential. Users must learn to distinguish between the apparent clarity provided by A.I. and genuine ethical reasoning grounded in mutual accountability. Education programs can draw explicitly from Darwall's insights, emphasizing the critical distinction between mere narrative coherence and authentic moral engagement.

Ultimately, Darwall’s ethics demand that we maintain human judgment, with its inherent second-personal responsiveness, at the center of moral discourse around A.I. Only through continuous, critically engaged human oversight, anchored in genuine relational accountability, can we hope to navigate ethically sound paths in the yellow wood of advanced A.I. development, consciously resisting the comforting myths of moral certainty that language and A.I. so readily and convincingly offer.