In this episode of Human in the Loop, we sit down with Meredith from Applied Information Sciences to explore what it truly takes to bring AI into organizations responsibly. From securing sensitive data to navigating shadow AI, she shares practical guidance shaped by years of working with highly regulated industries. Together, we unpack the evolving role of AI policies, the importance of human‑in‑the‑loop safeguards, and how emerging agentic systems are reshaping expectations for security and governance. It’s a grounded, insightful conversation for anyone looking to innovate with AI while keeping trust and safety at the center.

What You Will Learn:
• Trustworthy AI is crucial for responsible innovation
• Data security and governance are often underestimated risks.
• Zero Trust principles are essential in AI security.
• AI amplifies existing cybersecurity risks.
• Organizations must address shadow AI usage.
• Policies around AI usage are lacking in many organizations.
• Everyone in the organization shares responsibility for AI governance.
• Human oversight is necessary in AI decision-making.
• Experimentation with AI tools can lead to valuable insights.
• Curiosity and willingness to try new AI tools are vital.

In this episode of Human in the Loop, we sit down with Meeri Haataja, CEO and co-founder of Saidot, to explore what responsible AI governance really looks like inside organizations. From navigating regulatory frameworks to embedding transparency and accountability into AI development, Meeri shares practical insights for businesses at every stage of their AI journey. Whether you're scaling a startup or steering enterprise innovation, her advice on aligning technical ambition with ethical clarity is essential listening.

What you will learn:
• AI governance is crucial for building trust in AI systems.
• Responsible AI involves understanding and managing risks.
• The EU AI Act is gaining awareness but still needs more attention
• Organizations must recognize their responsibilities as AI deployers.
• Procurement processes are becoming more strategic in AI governance.
• Regulations provide a solid foundation for responsible AI practices.
• AI governance can differentiate businesses in the market.
• Experimentation with AI agents is essential for learning.
• Scaling AI governance is a significant challenge for organizations.
• Education and awareness are key to effective AI governance.

In this very first episode, hosts Chris Huntingford and Ioana Tanase, talk with Microsoft's Altrese Hawkins-Gopie about what responsible AI really means, why it matters for everyone (not just techies), and how we can all play a part in building Trustworthy AI.

What you will learn:
• What “responsible AI” is (with real-world examples)
• Why community feedback and inclusion are essential
• How users can help shape better AI (even if you’re not a developer)
• The importance of fact-checking and not blindly trusting AI
• Practical steps for organizations to build responsible AI
• How AI is changing the future of work and the skills you’ll need

In this episode of Human in the Loop, Christina Mallon joins us to unpack why disability is best understood as a mismatch between people and systems. We explore inclusive design, responsible AI, and why average users don't exist with a focus on how continuous human oversight matters long after launch.

What You Will Learn:

• Disability is a mismatch, not a deficit.
• There is no such thing as the “average user”.
• Responsible AI means asking hard questions, continuously.
• Inclusive design must involve lived experience, not assumptions.
• Perfect inclusion is impossible, but progress is essential.
• AI can be a powerful force multiplier for accessibility.
• Everyone should define their own Responsible AI principles.

This episode explores what organizations need to get right with their data, the difference between automation and generative AI, and how agentic systems can support real workflows. We break down practical steps for readiness, governance, and using tools like MCP servers and multi-agent models effectively.

What You Will Learn:
• AI is only as good as the data behind it.
• Generative AI does not replace classic data science.
• Unstructured data is a major (and risky) part of the AI landscape.
• AI should be treated as a copilot, not an autonomous decision-maker.
• AI agents require governance and visibility.
• Not every problem needs Generative AI.
• Multi-agent systems introduce new risks.
• Education is the first layer of AI governance.