From the EU AI Act to State regulations in the United States, companies in sectors such as insurance and human resources face new obligations to assess the fairness of AI solutions. And yet the data scientists and AI governance professionals responsible for meeting these requirements often lack the demographic data necessary to undertake accurate and comprehensive bias testing. In this webinar, a group of legal and technical experts will explore how practitioners can overcome this challenge.
The panel discuss the implications of new regulatory proposals like the EU AI Act, which carves out an exception to the EU’s ban on collection of sensitive data for the purpose of bias mitigation; Colorado’s new insurance regulations, which would require insurers to adopt specific data imputation techniques; as well as the potential role of guidance from standards agencies and organizations like NIST, in resolving these tensions.
In this session, our expert panel dives into the complexities and solutions surrounding demographic data gaps in AI fairness assessments. Key highlights include:
- Impact of demographic data gaps on bias assessments in the insurance industry.
- Innovative strategies for imputing demographic data.
- Balancing data imputation vs. data collection amidst legal and regulatory challenges.
- Practical guidance for policymakers and practitioners.
Panelists:
- Miranda Bogen, Founding Director at the Center for Democracy & Technology AI Governance Lab
- Reva Schwartz, Research Scientist/Principal Investigator for AI Bias at the National Institute of Standards and Technology (NIST)
- Myriah Jaworski,Attorney and Member, Data Privacy and Cybersecurity at Clark Hill
- Chris CookseyFCAS, MAAA, CSPA, Senior Director of Advanced Analytics at Guidewire
Watch now on YouTube
