The Dual Challenge of AI Accountability: Regulation and User Responsibility
Artificial Intelligence (AI) presents both immense opportunities and challenges, as seen in the recent case of ChatGPT’s mistranslation involving Malaysian MP Teresa Kok. This incident raises an important question: who is accountable when AI goes wrong? While regulation is often proposed as a solution, it is not a one-size-fits-all answer. Users also share responsibility, particularly when AI is used as a tool to assist in decision-making.
Pic credit - The Vibes file pic, September 10, 2024.
The Case for Regulation
On one hand, regulation provides a necessary framework to manage the risks associated with AI. The European Union’s AI Act is one such example, seeking to enforce transparency, human oversight, and accountability in high-risk AI applications. Regulation ensures that companies developing AI systems take precautions to minimize harm, through rigorous testing, bias detection, and risk management. Without such oversight, AI could run amok, leading to significant legal, ethical, and social harm.
For instance, when AI systems are used in high-stakes fields like policing, healthcare, or education, the margin for error is small. The consequences of an AI error, such as wrongful arrests or biased grading algorithms, can be devastating. Regulation can provide safeguards to prevent such outcomes and establish clear legal frameworks for when AI systems do cause harm.
The Role of User Accountability
However, AI is a tool—like a hammer or a calculator—and users cannot simply absolve themselves of responsibility when things go wrong. The Teresa Kok incident is a perfect example: while ChatGPT made an error, should the burden not also lie with the users who failed to verify the AI’s output before releasing it to the public?
If we treat AI as an assistive technology, users must take responsibility for overseeing its output, especially in sensitive areas. A doctor using an AI to assist in diagnosis cannot blame the tool for a wrong decision if they fail to cross-check the information. Similarly, a journalist using AI for translation should verify the text before publishing. In many cases, AI is not the final decision-maker—it is an aid. Thus, users hold ultimate responsibility.
The Grey Area of Product Disclaimers
But where do we draw the line between user responsibility and corporate accountability? Many AI companies issue disclaimers, stating that their products are not always accurate and that users should not rely on them for critical decisions. Does such a disclaimer absolve the company of liability?
The answer is complicated. A product disclaimer can alert users to potential risks, but it may not be enough to escape liability altogether. Courts may still find companies responsible if their AI system causes harm, particularly if the product was marketed as reliable in certain contexts. Disclaimers may reduce liability, but they don’t eliminate it. Moreover, if an AI system operates in a way that its developers should reasonably foresee could cause harm—such as bias in facial recognition—the company could still be held accountable despite a disclaimer.
Striking a Balance
The future of AI accountability likely lies in a hybrid approach. Regulation must play a role in setting safety standards for AI systems, particularly in high-risk applications. However, users must also be vigilant and take responsibility for how they deploy AI. A balance is needed where both AI developers and users share the burden of accountability.
Ultimately, AI is here to stay, and its role in society will only expand. As we navigate this evolving landscape, we must ensure that both regulation and personal responsibility evolve alongside it. Only then can we harness the full potential of AI while minimizing its risks.