Teaching AI Agents Ethical Behavior (Rebecca Bultsma)

Can you trust an AI agent to act in line with your values — and who’s responsible when it doesn’t?

In this episode, Andreas Welsch talks with AI ethics consultant Rebecca Bultsma about the pitfalls of rushing AI agents into business workflows and practical steps leaders should take before handing autonomy to software. Rebecca draws on her early ChatGPT experiments and academic work in data & AI ethics to explain why generative AI raises fresh ethical risks and how organizations can reduce harm.

What you’ll learn:

• Why generative AI and agents amplify old AI ethics problems (bias, hidden assumptions, and Western-centric worldviews).
• Why you should build internal understanding first: experiment with low-stakes, traceable use cases before deploying public agents.
• The importance of audit trails, explainability, and oversight to trace decisions and assign accountability when things go wrong.
• Practical red flags: agents that transact autonomously, weak logging, and complacency about vendor claims.
• A legal reality check: new laws (like California’s chatbot rules) are emerging and could increase liability for organizations that deploy chatbots or agents prematurely.

The top takeaways:

• Learn by experimenting personally and internally in your organization to discover where agents fail.
• Start small with low-stakes, narrowly scoped tasks you can monitor and audit.
• Don’t rush; rather, observe others' failures, train your people, and build governance before going public.

If you’re a leader evaluating agents or responsible for AI governance, this episode gives clear, actionable advice for keeping your organization out of the headlines for the wrong reasons. Tune in to hear the whole conversation and learn how to turn AI hype into safer business outcomes.

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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.

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