How Do You Build a Moral AI? with Jana Schaich Borg

In this episode of the Behavioral Design Podcast, hosts Aline and Samuel are joined by Jana Schaich Borg, Associate Research Professor at Duke University and co-author of the book “Moral AI and How We Get There”. Together they explore one of the thorniest and most important questions in the AI age: How do you encode human morality into machines—and should you even try?

Drawing from neuroscience, philosophy, and machine learning, Jana walks us through bottom-up and top-down approaches to moral alignment, why current models fall short, and how her team’s hybrid framework may offer a better path. Along the way, they dive into the messy nature of human values, the challenges of AI ethics in organizations, and how AI could help us become more moral—not just more efficient.

This conversation blends practical tools with philosophical inquiry and leaves us with a cautiously hopeful perspective: that we can, and should, teach machines to care.

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 Topics Covered:

• What AI alignment really means (and why it’s so hard)

• Bottom-up vs. top-down moral AI systems

• How organizations get ethical AI wrong—and what to do instead

• The messy reality of human values and decision making

• Translational ethics and the need for AI KPIs

• Personalizing AI to match your values

• When moral self-reflection becomes a design feature

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Timestamps:

00:00  Intro: AI Alignment — Mission Impossible?
04:00  Why Moral AI Is So Hard (and Necessary)
07:00  The “Spec” Story & Reinforcement Gone Wrong
10:00  Anthropomorphizing AI — Helpful or Misleading?
12:00  Introducing Jana & the Moral AI Project
15:00  What “Moral AI” Really Means
18:00  Interdisciplinary Collaboration (and Friction)
21:00  Bottom-Up vs. Top-Down Approaches
27:00  Why Human Morality Is Messy
31:00  Building a Hybrid Moral AI System
41:00  Case Study: Kidney Donation Decisions
47:00  From Models to Moral Reflection
52:00  Embedding Ethics Inside Organizations
56:00  Moral Growth Mindset & Training the Workforce
01:03:00  Why Trust & Culture Matter Most
01:06:00  Comparing AI Labs: OpenAI vs. Anthropic vs. Meta
01:10:00  What We Still Don’t Know
01:11:00  Quickfire: To AI or Not To AI
01:16:00  Jana’s Most Controversial Take
01:19:00  Can AI Make Us Better Humans?

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Understanding AI Risks with Peter Slattery

In this episode of the Behavioral Design Podcast, hosts Aline and Samuel are joined by Peter Slattery, behavioral scientist and lead researcher at MIT’s FutureTech lab, where he spearheads the groundbreaking AI Risk Repository project. Together, they dive into the complex and often overlooked risks of artificial intelligence—ranging from misinformation and malicious use to systemic failures and existential threats.

Peter shares the intellectual and emotional journey behind categorizing over 1,000 documented AI risks, how his team built a risk taxonomy from 17,000+ sources, and why shared understanding and behavioral science are critical for navigating the future of AI.

This one is a must-listen for anyone curious about AI safety, behavioral science, and the future of technology that’s moving faster than most of us can track.

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LINKS:

• Peter's LinkedIn Profile
• MIT FutureTech Lab: futuretech.mit.edu
• AI Risk Repository

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The song used is ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Murgatroyd by David Pizarro⁠

Enter the AI Lab: Insights from LinkedIn Polls and AI Literature Reviews

In this episode of the Behavioral Design Podcast, hosts Samuel Salzer and Aline Holzwarth explore how AI is shaping behavioral design processes—from discovery to testing. They revisit insights from past LinkedIn polls, analyzing audience perspectives on which phases of behavioral design are best suited for AI augmentation and where human expertise remains crucial.

The discussion then shifts to AI-driven literature reviews, comparing the effectiveness of various AI tools for synthesizing research. Samuel and Aline assess the strengths and weaknesses of different platforms, diving into key performance metrics like quality, speed, and cost, and debating the risks of over-reliance on AI-generated research without human oversight.

The episode also introduces Nuance’s AI Lab, highlighting upcoming projects focused on AI-driven behavioral science innovations. The conversation concludes with a Behavioral Redesign series case study on Peloton, offering a fresh take on how AI and behavioral insights can reshape product experiences.

If you're interested in the intersection of AI, behavioral science, and research methodologies, this episode is packed with insights on where AI is excelling—and where caution is needed.

LINKS:

• Nuance AI Lab: Website

TIMESTAMPS:
00:00 Introduction and Recap of Last Year's AI Polls
06:27 AI's Strengths in Literature Review
15:12 Emerging AI Tools for Research
19:31 Evaluating AI Tools for Literature Reviews
23:57 Comparing Chinese and American AI Tools
26:01 Evaluating Literature Review Outputs
28:12 Critical Analysis and Human Oversight
35:19 The Worst Performing Model
37:21 Introducing Nuance's AI Lab
38:51 Behavioral Redesign Series: Peloton Example
45:21 Podcast Highlights and Future Guests

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Interesting in collaborating with Nuance? If you’d like to become one of our special projects, email us at hello@nuancebehavior.com or book a call directly on our website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠nuancebehavior.com.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Support the podcast by joining ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Habit Weekly Pro⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 🚀. Members get access to extensive content databases, calls with field leaders, exclusive offers and discounts, and so much more.

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The song used is ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Murgatroyd by David Pizarro⁠