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

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.

44:42

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Episode guests

Vera Liao

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Quick takeaways

Explainability should be prioritized in AI applications to help users understand the system and make informed decisions.

A human-centered approach is crucial in developing effective explanations that align with user needs and enhance their understanding.

Deep dives

Importance of Explainability in AI Applications

Explainability should be at the core of AI applications to help users understand how the system works, what it can do, and how they can take appropriate actions. HCI researchers have been studying the topic of helping users understand for a long time, and explainability is an important component of that. It is crucial to consider users' mental models and provide explanations that can improve their understanding and decision-making process.

Introduction

2min

Importance of Explainability and Human-Centered Design

25min

Feature-based vs Example-based Explanations in Explainability

5min

Human-Centered Explainable AI

12min

Critical Analysis, Explainability, and the Limitations of Chat GBT

2min

The Challenges of Algorithmic Explanations and Where to Find More Information

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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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

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

Importance of Explainability in AI Applications

Clarifying the Concepts of Model Explainability and Interpretability

The Importance of a Human-Centered Approach to Explainability

Question-Driven XAI Design Process

Remember Everything You Learn from Podcasts