Bob Pulver speaks with Jeff Pole, co-founder and CEO of Warden AI, about the critical issues surrounding trust in AI technology, particularly in the context of HR and recruitment. They discuss the importance of third-party assurance in AI systems, the fluidity of AI terminology, and the need for continuous monitoring to ensure compliance and fairness. Jeff shares insights on how AI can potentially enhance fairness in hiring practices and the implications of emerging AI legislation across the globe. Bob and Jeff discuss the widespread issue of age discrimination in hiring, and Warden AI’s newly announced capabilities to check for age bias in AI-powered hiring solutions. They explore future opportunities and challenges in Responsible AI governance, including the implications of existing discrimination laws, the need for comprehensive data to assess bias, and the evolving landscape of AI adoption in various sectors, particularly in the public domain. Bob and Jeff conclude by emphasizing the importance of balance between innovation and ethical considerations in AI to maintain trust across stakeholder communities. Keywords AI, trust, assurance, governance, HR technology, bias, compliance, monitoring, legislation, fairness, AI, age bias, discrimination, responsible AI, governance, technology, workforce, regulations, innovation, public sector Takeaways AI technology can be a force for good if used correctly. Warden AI focuses on third-party assurance for AI systems. Continuous monitoring of AI is crucial for trustworthiness. The terminology around AI governance is fluid and evolving. Legislation is pushing for more transparency in AI processes. AI can help identify and correct bias in recruitment. The potential for AI to improve fairness in hiring is significant. Emerging laws will likely increase scrutiny on AI systems. AI can help unlock hidden talent pools in the workforce. The future of AI in HR is about enhancing diversity and inclusion.  Age discrimination is a significant issue in hiring. AI systems must comply with existing discrimination laws. The first AI bias lawsuit was related to age discrimination. Employers can unintentionally lead to discriminatory outcomes. Five generations will soon be part of the workforce. Data collection is crucial for assessing AI bias. Counterfactual analysis is a technique to test AI systems. Responsible AI practices can coexist with innovation. AI literacy is essential for effective adoption. AI adoption is a gradual process, not an immediate change. Sound Bites "How can we trust and safely adopt AI?" "Technology can be a force for good in society." "We're working on age bias detection capability." "The first AI bias lawsuit was for age bias." "We have to keep in mind existing legislation." "Five generations will be in the workforce soon." "We bring our own data to test AI systems." "Counterfactual analysis helps assess AI bias." "AI can augment human processes effectively." "It's a marathon, not a sprint with AI adoption." Chapters 00:00 Introduction to AI and Trust Issues 03:00 Warden AI's Mission and Assurance Role 05:50 Understanding AI Terminology and Governance 08:51 The Importance of Continuous Monitoring 12:13 AI in HR: Opportunities and Challenges 14:45 The Role of Legislation in AI Assurance 18:10 AI's Potential for Fairness in Hiring 21:05 The Future of AI and Workforce Diversity 28:59 Addressing Age Bias in AI Systems 41:24 Navigating Responsible AI and Governance 50:34 The Future of AI: Opportunities and Cautions Jeff Pole: https://www.linkedin.com/in/jeffrey-pole-91887a44 Warden AI: https://www.warden-ai.com/ Addressing Age Discrimination: https://www.warden-ai.com/blog/age-bias-ai-hiring-age-discrimination-fairer-recruitment For advisory work and marketing inquiries: Bob Pulver: https://linkedin.com/in/bobpulver Elevate Your AIQ: https://elevateyouraiq.com