An Analysis of the New EU AI Act and A Proposed Standardization Framework for Machine Learning Fairness
This addresses regulatory gaps in AI fairness for policymakers and industry stakeholders, but is incremental as it builds on existing work.
The paper identifies a lack of quantifiable fairness metrics and ambiguous terminology in the EU AI Act, arguing this creates liability risks that deter investment, and proposes a standardization framework for fairness and transparency in AI systems, exemplified with speech technology cases.
The European Union's AI Act represents a crucial step towards regulating ethical and responsible AI systems. However, we find an absence of quantifiable fairness metrics and the ambiguity in terminology, particularly the interchangeable use of the keywords transparency, explainability, and interpretability in the new EU AI Act and no reference of transparency of ethical compliance. We argue that this ambiguity creates substantial liability risk that would deter investment. Fairness transparency is strategically important. We recommend a more tailored regulatory framework to enhance the new EU AI regulation. Further-more, we propose a public system framework to assess the fairness and transparency of AI systems. Drawing from past work, we advocate for the standardization of industry best practices as a necessary addition to broad regulations to achieve the level of details required in industry, while preventing stifling innovation and investment in the AI sector. The proposals are exemplified with the case of ASR and speech synthesizers.