In an era where algorithms increasingly shape critical decisions across healthcare, finance, hiring, and governance, robust legal frameworks are essential to ensure fairness, accountability, and transparency. I will go into the complexities of regulatory responses to automated decision-making, exploring emerging policies that govern algorithmic development and deployment. I will explain the efficacy of existing regulations, identify critical gaps, and discuss best practices for ensuring stakeholders-from developers to end users-are adequately protected.
Key issues include liability for harms caused by algorithmic biases, the scope of transparency requirements, and the continually evolving international data protection standards. I will examine how legislation, such as the EU’s AI Act, can serve as both a template and a challenge in establishing global norms. The session also explores cross-disciplinary collaborations that leverage legal expertise, data science proficiency, and stakeholder engagement for robust compliance strategies. Attendees will gain a nuanced understanding of legal tools and enforcement mechanisms aimed at mitigating risk while enabling innovation.
