Experiencing Data w/ Brian T. O’Neill (UX for AI Data Products, SAAS Analytics, Data Product Management)

125 - Human-Centered XAI: Moving from Algorithms to Explainable ML UX with Microsoft Researcher Vera Liao

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Sep 5, 2023

Vera Liao, Principal Researcher at Microsoft, discusses the importance of human-centered approach in rendering model explainability within a UI. She shares insights on why example-based explanations tend to out-perform feature-based ones and why traditional XAI methods may not be the solution for every explainability problem. Vera advocates for qualitative research in tandem with model work to improve outcomes and highlights the challenges of responsible AI.

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Introduction

00:00 • 2min

Importance of Explainability and Human-Centered Design

01:46 • 25min

Feature-based vs Example-based Explanations in Explainability

26:46 • 5min

Human-Centered Explainable AI

32:00 • 12min

Critical Analysis, Explainability, and the Limitations of Chat GBT

43:58 • 2min

The Challenges of Algorithmic Explanations and Where to Find More Information

45:59 • 2min

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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