Vera Liao: AI Explainability and Transparency
The Gradient: Perspectives on AI
The Importance of Interactivity in Explanations
The speakers discuss the significance of interactivity in explanations and how interactive explanations enhance goal fulfillment and questioning, closing belief gaps and making the explainability process more effective.
In episode 101 of The Gradient Podcast, Daniel Bashir speaks to Vera Liao.
Vera is a Principal Researcher at Microsoft Research (MSR) Montréal where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics) group. She is trained in human-computer interaction research and works on human-AI interaction, currently focusing on explainable AI and responsible AI. She aims to bridge emerging AI technologies and human-centered design practices, and use both qualitative and quantitative methods to generate recommendations for technology design. Before joining MSR, Vera worked at IBM TJ Watson Research Center, and her work contributed to IBM products such as AI Explainability 360, Uncertainty Quantification 360, and Watson Assistant.
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Outline:
* (00:00) Intro
* (01:41) Vera’s background
* (07:15) The sociotechnical gap
* (09:00) UX design and toolkits for AI explainability
* (10:50) HCI, explainability, etc. as “separate concerns” from core AI reseaarch
* (15:07) Interfaces for explanation and model capabilities
* (16:55) Vera’s earlier studies of online social communities
* (22:10) Technologies and user behavior
* (23:45) Explainability vs. interpretability, transparency
* (26:25) Questioning the AI: Informing Design Practices for Explainable AI User Experiences
* (42:00) Expanding Explainability: Towards Social Transparency in AI Systems
* (50:00) Connecting Algorithmic Research and Usage Contexts
* (59:40) Pitfalls in existing explainability methods
* (1:05:35) Ideal and real users, seamful systems and slow algorithms
* (1:11:08) AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
* (1:11:35) Vera’s earlier experiences with chatbots
* (1:13:00) Need to understand pitfalls and use-cases for LLMs
* (1:13:45) Perspectives informing this paper
* (1:20:30) Transparency informing goals for LLM use
* (1:22:45) Empiricism and explainability
* (1:27:20) LLM faithfulness
* (1:32:15) Future challenges for HCI and AI
* (1:36:28) Outro
Links:
* Research
* Earlier work
* Understanding Experts’ and Novices’ Expertise Judgment of Twitter Users
* Expert Voices in Echo Chambers
* HCI / collaboration
* Exploring AI Values and Ethics through Participatory Design Fictions
* Ways of Knowing for AI: (Chat)bots as Interfaces for ML
* Human-AI Collaboration: Towards Socially-Guided Machine Learning
* Questioning the AI: Informing Design Practices for Explainable AI User Experiences
* Rethinking Model Evaluation as Narrowing the Socio-Technical Gap
* Human-Centered XAI: From Algorithms to User Experiences
* AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
* Fairness and explainability
* Questioning the AI: Informing Design Practices for Explainable AI User Experiences
* Expanding Explainability: Towards Social Transparency in AI Systems
* Connecting Algorithmic Research and Usage Contexts
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