Responsibility 2.0

In the age of AI, we need to update the definition, utility and function of responsibility.

The mainstream perspective on AI and responsibility, while superficially appealing, rests on several problematic assumptions that deserve scrutiny.

The argument creates a false dichotomy: AI will become extraordinarily capable BUT can never be responsible, therefore humans must always bear responsibility. But this is conceptually confused. Responsibility isn't some metaphysical property that only biological entities possess—it's a social and legal construct we assign based on agency, predictability, and accountability mechanisms. We already distribute responsibility across non-human entities like corporations and governments through legal frameworks and insurance structures.

The deeper problem, though, is that our entire concept of responsibility is trapped in feudal thinking. Think about it: our inherited notion that someone must "answer for" and “pay for” failures in a hierarchical chain of command is an artifact of power structures where authority and capability were concentrated in human agents. In today's distributed, algorithmic systems, this medieval framework doesn't just fail to fit—it actively obscures how these systems actually work, forcing us into feudal narratives that make no sense.

What's particularly telling is how those who insist "AI cannot be held responsible" reveal what really bothers them: AI cannot suffer consequences. It can't be fired, imprisoned, or shamed. But this betrays such a primitive view of accountability, one rooted in retribution rather than systemic improvement. Sure, an AI system can't feel guilt—but neither can a corporation or a government, and we've managed to develop sophisticated mechanisms for corporate liability that shape behavior through incentive structures, not threats of suffering. The inability to punish AI is only a problem if you believe accountability is fundamentally about making someone pay rather than making systems better.

This punishment fixation creates perverse incentives everywhere. When we preemptively assign human responsibility regardless of context, we're just engaging in regret avoidance—a defensive posture that prioritizes having someone to blame over actually preventing problems. If humans must accept responsibility for outcomes they can neither predict nor control as AI systems become more autonomous, then responsibility becomes meaningless. It's just accountability theater, not a useful concept for improving outcomes.

Even worse, this blame-focused culture actively prevents us from learning. Complex sociotechnical systems fail in complex ways. Look at aviation—it became safer not by ensuring there was always a human to blame, but by developing just culture principles, system safety approaches, and blameless post-mortems. When we obsess over having a punishable party, we make systems less safe by encouraging cover-ups and discouraging the transparency needed for genuine improvement.

So the productive question isn't "Who takes responsibility when AI fails?" but rather "How do we design AI systems, governance structures, and liability frameworks that align incentives, distribute risks appropriately, and promote beneficial outcomes?" This means developing a post-feudal concept of responsibility suited to networks, not hierarchies—one focused on system design, feedback loops, and outcome optimization rather than identifying which person must answer for which "domain." We need insurance requirements, mandatory audit trails, regulatory sandboxes, and novel forms of algorithmic accountability that treat responsibility as an emergent property of well-designed systems rather than a burden to be carried by designated humans.

The goal isn't to find someone to hold responsible but to create responsibility mechanisms that drive improvement rather than blame and retribution. In fact, this obsession with finding a responsible party already fails in human organizations. Almost all organizational outcomes are emergent—they arise from complex interactions between people, processes, and circumstances that no single person truly controls. When a project fails or a crisis emerges, it's rarely because one person made one bad decision; it's because multiple decisions, constraints, and systemic factors combined in unexpected ways. Yet we persist in the theatrical exercise of identifying the "responsible party." Even when we successfully trace responsibility to a person or group—the executive who "owned" the strategy, the team that "dropped the ball"—what then? They resign, get reassigned, or receive their reprimand, and the system that produced the failure remains fundamentally unchanged. We've satisfied our need for accountability theater but done nothing to prevent the next failure. The system improvement aspect, the only part that actually matters, is almost always missing because we've exhausted our organizational energy on the blame game.

AI doesn't create this responsibility problem; it just makes it impossible to ignore. We can no longer pretend that complex outcomes have simple authors. Instead of forcing humans to serve as designated scapegoats for increasingly autonomous systems, we need to build accountability mechanisms that actually improve outcomes—whether the agents in question are human, artificial, or both.

© Saip Eren Yilmaz, 2024