In this second episode of the AI Standards Stack Podcast, guest Dr Christine Chow joins hosts Professor Michael Mainelli and Adam Leon Smith to discuss responsible AI governance from an investor’s perspective. Christine, a long-time investment professional and early advocate for responsible AI since 2012, shares insights drawn from her pioneering work, including leading Federated Hermes’ 2019 industry-first investor expectations on responsible AI and data governance.
The conversation centers on why robust data governance forms the foundation of effective AI governance, covering data provenance, bias in raw, model and synthetic data, transparency, explainability and accountability. It explores practical challenges across evolving AI paradigms, from efficiency tools to generative, agentic, multimodal and embodied systems, including use-case identification, prompt engineering, meaningful human-in-the-loop oversight, board-level engagement, and societal risks of over-reliance such as impacts on mental health, confidence and critical thinking. The episode examines the fragmented global standards landscape (EU AI Act risk categories, NIST voluntary frameworks, ISO 42001), investor approaches to company engagement, environmental concerns around AI infrastructure, tensions between free speech and content guardrails, cultural complexities in human rights, and the push for concrete implementation guidance to balance innovation with safety and societal well-being.