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.

Thirty years ago, the idea of a machine learning on its own would have stoked the worst kind of sci-fi nightmares about robots taking over the planet. These days, machine learning is so commonplace, we barely notice it. Computers routinely learn what we watch on TV, what we buy, how we talk, and even how we feel--and use that to make predictions about how we'll act next. As the field of machine learning (ML) has become increasingly mainstream, says Harvard Business School doctoral student Mike Teodorescu, it has evolved into the reach of everyday companies, who are increasingly using ML to manage many aspects of their business operations. "There's been an explosion," Teodorescu says.