Code of Conduct: Who Is to Blame When a Robot Fails? | NIRMAL NEWS

Here is an article on the topic of assigning blame when a robot fails.

Code of Conduct: Who Is to Blame When a Robot Fails?

A self-driving car misjudges an obstacle and causes a multi-vehicle pile-up. A surgical robot makes an infinitesimal error, leading to a patient’s injury. A financial AI executes a disastrous trade that wipes out a retirement fund. These scenarios, once the stuff of science fiction, are now on the cusp of reality. As autonomous systems become woven into the fabric of our daily lives, a critical and complex question emerges: When a robot fails, who is to blame?

Our legal and ethical frameworks are built around human agency. We understand concepts like negligence, intent, and human error. But when the “actor” is a lines of code, a neural network, and a collection of sensors, our traditional models of accountability begin to crumble. Pinpointing responsibility in the world of AI is like trying to grab smoke—the blame seems to diffuse among a host of potential culprits.

Let’s dissect the chain of responsibility and explore the candidates for culpability.

The Usual Suspects: A Lineup of Liability

1. The Manufacturer/Developer

The most obvious candidate is the company that designed, built, and sold the machine. In traditional product liability, if a new toaster explodes, the manufacturer is held responsible for selling a defective product. The same logic seems to apply here. They designed the hardware, developed the core algorithms, and profited from its sale. If a fundamental design flaw or an oversight in the system’s logic leads to failure, the burden seems to fall squarely on their shoulders.

The Counterargument: AI developers often argue that they cannot possibly foresee every single “edge case” or real-world scenario. A deep learning system, in particular, can be a “black box,” making decisions in ways even its creators don’t fully understand. They might argue that their End-User License Agreement (EULA) and operating manuals clearly state the system’s limitations, shifting some responsibility to the user.

2. The Programmer

What if the failure wasn’t a high-level design flaw, but a simple bug? A single misplaced semicolon or a flawed line of logic written by an individual software engineer. Can we blame the coder who introduced the error?

The Counterargument: This is rarely feasible. Modern software is built by vast teams, with codebases containing millions of lines written and revised over years. Pinpointing a single “guilty” line of code is often impossible. More likely, the failure is an emergent property of complex interactions between different modules. Blaming an individual programmer ignores the systemic pressures of deadlines, budget constraints, and the inherent difficulty of writing perfect code.

3. The Owner/Operator

Perhaps the responsibility lies with the person who owns or operates the robot. Did they use the self-driving car in a prohibited weather condition? Did the hospital fail to properly maintain the surgical robot or provide its staff with adequate training? Did the owner neglect a critical software update that would have patched the vulnerability?

The Counterargument: Owners can only be held responsible for what they can reasonably control and understand. If a user is told a system is “autonomous,” it sets an expectation of reliability. How can we blame someone for not intervening when the entire purpose of the machine is to act without intervention? Expecting an average user to understand the intricate workings of their AI is unrealistic.

4. The Data

Modern AI is not just programmed; it’s trained. It learns from vast datasets. If the data used to train the AI is biased, incomplete, or flawed, the AI’s performance will be flawed. A facial recognition system that fails to identify people of color is not malicious; it’s a reflection of biased training data.

The Counterargument: Who is to blame for bad data? The company that collected it? The society that generated the biased information in the first place? Blaming the data diffuses responsibility to a societal level, making it almost impossible to assign to a single entity. It’s a critical problem to solve, but it offers little recourse for an individual victim.

The Ghost in the Machine: What if No One is to Blame?

The most unsettling possibility is that in some cases, no single party is at fault. The failure could be the result of a “perfect storm”—an unpredictable convergence of environmental factors, subtle data misinterpretations, and unforeseen system interactions. This is known as a systemic failure, where the overall complexity of the system is the cause, not a specific faulty component. While technically plausible, this answer is socially and legally unsatisfying. Victims need recourse, and society needs assurance that someone is accountable.

A New Code of Conduct: Moving from Blame to Responsibility

The solution isn’t to find a single scapegoat, but to build a new framework of shared responsibility and proactive governance. This is the new “Code of Conduct” we must write—not for the robots, but for ourselves.

1. Regulation and Clear Liability: Governments must create clear legal frameworks for AI. This could involve tiered liability models where responsibility is shared between the manufacturer and owner based on the level of autonomy. A “no-fault” insurance system, similar to what exists for auto accidents in some regions, could ensure victims are compensated quickly while the complex question of ultimate blame is sorted out later.

2. Ethics & Safety by Design: Companies must move beyond a “move fast and break things” mentality. Ethical and safety considerations must be integrated into the AI development lifecycle from the very beginning, not bolted on as an afterthought.

3. Transparency and Explainability (XAI): We must demand systems that are less of a “black box.” The field of Explainable AI (XAI) is working to create systems that can articulate why they made a particular decision. This “flight data recorder” for AI is essential for debugging, auditing, and assigning responsibility.

4. Rigorous Auditing and Certification: Just as we have independent bodies that certify the safety of airplanes and bridges, we will need third-party agencies to audit and certify the safety, fairness, and robustness of critical AI systems before they are deployed.

The question of “who is to blame” is ultimately a question of trust. For us to live, work, and coexist with these increasingly powerful autonomous systems, we need to trust that they are safe, fair, and accountable. Building that trust requires a fundamental shift from a reactive search for blame to a proactive culture of shared responsibility. The code of conduct we develop today will determine the safety and stability of our automated future.