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Does the value of your insights, analytics, or automated intelligence product sometimes feel invisible to buyers and users? Does your product have impressive analytics and AI technology, but user adoption and sales still are not where you want them to be?
While it has never been easier to build data-driven products, why does it still seem so hard to build indispensable data products that users can't live without—and will gladly pay for?
I’m Brian T. O’Neill, and on Experiencing Data — a Listen Notes top 2% global podcast — I help founders and B2B software product leaders close the Invisible Intelligence Gap through solo episodes and interviews with leaders at the intersection of product management, UX design, analytics, and AI.
If you’re building analytics, BI, or automated intelligence (AI) products, this non-technical show will help you better connect your product to outcomes, value, and the human factors that still matter — even in the age of AI.
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Episodes
Tuesday Sep 05, 2023
Tuesday Sep 05, 2023
Today I’m joined by Vera Liao, Principal Researcher at Microsoft. Vera is a part of the FATE (Fairness, Accountability, Transparency, and Ethics of AI) group, and her research centers around the ethics, explainability, and interpretability of AI products. She is particularly focused on how designers design for explainability. Throughout our conversation, we focus on the importance of taking a human-centered approach to rendering model explainability within a UI, and why incorporating users during the design process informs the data science work and leads to better outcomes. Vera also shares some research on why example-based explanations tend to out-perform [model] feature-based explanations, and why traditional XAI methods LIME and SHAP aren’t the solution to every explainability problem a user may have.
Highlights/ Skip to:
- I introduce Vera, who is Principal Researcher at Microsoft and whose research mainly focuses on the ethics, explainability, and interpretability of AI (00:35)
- Vera expands on her view that explainability should be at the core of ML applications (02:36)
- An example of the non-human approach to explainability that Vera is advocating against (05:35)
- Vera shares where practitioners can start the process of responsible AI (09:32)
- Why Vera advocates for doing qualitative research in tandem with model work in order to improve outcomes (13:51)
- I summarize the slides I saw in Vera’s deck on Human-Centered XAI and Vera expands on my understanding (16:06)
- Vera’s success criteria for explainability (19:45)
- The various applications of AI explainability that Vera has seen evolve over the years (21:52)
- Why Vera is a proponent of example-based explanations over model feature ones (26:15)
- Strategies Vera recommends for getting feedback from users to determine what the right explainability experience might be (32:07)
- The research trends Vera would most like to see technical practitioners apply to their work (36:47)
- Summary of the four-step process Vera outlines for Question-Driven XAI design (39:14)
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