The Human in the Loop | Ethical AI with Di Le
ServicveNow Insights Podcast - hosted By Bobby Brill

What does it actually mean to build AI responsibly? Not the buzzword version. The real version. In our latest episode, I sat down with Di Le — AI Ethicist and Human-Centered AI Strategist at ServiceNow — and she broke it down in a way I hadn't heard before.

Most people use Ethical AI, Responsible AI, and Human-Centered AI interchangeably, and Di breaks down exactly where each one lives and how they apply to building AI that aligns with our societal values. Fairness. Transparency. Bias. Beyond evaluation and technical talking points, these are also design decisions with real consequences for real people — and operationalizing them is harder than most organizations want to admit.

One line from Di that stopped me: "People have crossed oceans and built monuments in honor of our capability to think. And I just want people to preserve that and not surrender it so freely." That's the episode in one sentence.

To learn more about Ethical AI and reseatch from Di Le and more - https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1020&context=sighci2025

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1025&context=sighci2024

https://www.youtube.com/watch?v=QhVY-85A-Wk&t=5s

ServiceNow Insights Podcast 🎧 The Human in the Loop — now available wherever you listen to podcasts.

0:00 – Introduction & Di Le's Background
1:35 – Is Ethical AI Just About Preventing Skynet?
2:22 – Responsible AI vs. Ethical AI vs. Human-Centered AI
6:34 – Why All Three Are Deeply Interconnected
8:39 – When "Amazing" Tech Goes Wrong: The Adobe Example
10:07 – Fairness, Transparency & Accountability Explained
12:06 – The Mug Factory: Understanding Bias in AI
14:52 – Transparency & Interpretability in AI Design
16:48 – Keeping Humans Genuinely in the Loop
18:06 – Accountability & Human Oversight
20:39 – Why Bias Isn't a Bug You Can Just Fix
22:30 – How ServiceNow Approaches Responsible AI
23:11 – Why Di Chose ServiceNow (The Origin Story)
27:09 – One Thing Everyone Should Do With AI 28:38 – Closing

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