Rather than broadly anticipating concerns, AI regulation should focus on mitigating specific, demonstrable harms. This targeted approach allows for a more adaptable and effective governance framework in the rapidly evolving landscape of artificial intelligence. Regulation should address tangible risks, such as AI's potential to reinforce societal biases or cause psychological harm.
Legislators can categorise AI systems based on risk levels, applying stricter rules to applications with potential for severe harm, like those contributing to mental health issues or undermining fairness. Preventative measures and clear guidelines are crucial, shifting the focus from defining harm after it occurs to proactively protecting mental health and well-being. Algorithmic Impact Assessments can evaluate potential social harms before implementation, ensuring accountability and informing policy development.
By focusing on harm mitigation, regulators can foster responsible AI innovation while safeguarding public interests. This involves establishing technical advisory bodies, maintaining incident databases, and encouraging industry collaboration to develop transparency and real-time monitoring of AI's negative outcomes.
