Research Summary: Validity, Reliability, and Transparency in Artificial Intelligence Regulation

Existing data protection frameworks are insufficient for addressing the unique risks posed by Artificial Intelligence (AI) systems. While current regulations address privacy concerns related to data leakage, re-identification, and profiling, they do not adequately cover the fundamental issue of unreliable or unjustified inferences made by AI, even when data collection and processing are legitimate. New regulatory approaches are required to address specific AI concerns such as construct validity, confounding, representativeness, distribution shift, and fairness trade-offs. The concepts of transparency and explainability in AI also present distinct and more complex challenges.

Why it matters

The expanding role of AI in decision-making necessitates a re-evaluation of regulatory frameworks to mitigate novel risks beyond traditional data privacy. Failure to address these emerging concerns can lead to significant societal and individual harms, impacting trust, fairness, and the legitimate application of AI technologies across various sectors.

Key insights

• Current data protection frameworks are inadequate for AI, particularly concerning unreliable or unjustified inferences.
• AI systems introduce distinct risks such as construct validity, confounding, representativeness, distribution shift, and fairness trade-offs.
• The challenges of transparency and explainability in AI are significantly more complex than in traditional data processing.
• Specialised regulatory attention is required to address the unique concerns of AI systems.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.05800