The Accountability Vacuum
For years, the failure of an AI system has often been met with a collective shrug. Accountability is diffuse, spread across vast teams of developers, data providers, and corporate entities. This creates a dangerous accountability vacuum. When nobody is clearly
responsible, everybody can deny fault. The complex and often opaque nature of modern AI, particularly deep learning models, makes it difficult to trace an error back to a specific human decision. This is the 'black box' problem, where even the creators of an AI cannot fully explain its decision-making process. This ambiguity allows organisations to treat AI failures as unavoidable glitches rather than preventable errors, leaving those harmed with little recourse.
Why Named Ownership Is the Answer
The solution is to borrow a principle from older, high-stakes industries: clear, designated ownership. Just as a ship has a captain and a newspaper has an editor-in-chief, every consequential AI system must have a named, accountable owner. This is not about finding a single person to blame, but about creating a structure of responsibility. A designated owner ensures that someone is ultimately answerable for a system's performance, risk management, and ethical alignment from its inception through its entire lifecycle. This simple but powerful shift forces organisations to move from abstract principles to concrete practice. It ensures there is always a human point of contact who can be compelled to provide answers, oversee fixes, and face consequences, whether they are regulatory, legal, or reputational.
The Indian Context: Building Trust in a Digital Future
In India, where the government is pushing a 'people-first' approach to AI, building public trust is paramount. The rapid adoption of AI in critical sectors like finance, healthcare, and public services depends on citizens believing these systems are fair, safe, and accountable. Recent regulatory moves, such as amendments to the IT Rules in early 2026 to govern AI-generated content, show that policymakers are aware of the risks. However, regulating outputs is only part of the solution. For India's AI mission to succeed, corporations must adopt internal governance that mirrors this scrutiny. Clear ownership structures would demonstrate a commitment to responsibility that goes beyond mere compliance, fostering the trust needed for AI to become a true engine of inclusive growth.
From Theory to Practice: Making It Work
Implementing named ownership is not without its challenges. AI systems are complex, involving multiple teams and external vendors. Effective governance requires a tiered structure of accountability. This might involve a board-level AI Risk Committee for high-level oversight and a designated product owner for each specific AI model. This individual or team would be responsible for continuous monitoring, conducting risk assessments, ensuring the data is fair, and maintaining documentation. The role is not purely technical; it requires coordinating with legal, ethical, and operational departments. While complex, this structure is necessary. It moves AI governance from a theoretical exercise documented in binders to a living architecture of responsibility within the organisation.
