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:
Links
142 - Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod)
141 - How They’re Adopting a Producty Approach to Data Products at RBC with Duncan Milne
140 - Why Data Visualization Alone Doesn’t Fix UI/UX Design Problems in Analytical Data Products with T from Data Rocks NZ
139 - Monetizing SAAS Analytics and The Challenges of Designing a Successful Embedded BI Product (Promoted Episode)
138 - VC Spotlight: The Impact of AI on SAAS and Data/Developer Products in 2024 w/ Ellen Chisa of BoldStart Ventures
137 - Immature Data, Immature Clients: When Are Data Products the Right Approach? feat. Data Product Architect, Karen Meppen
136 - Navigating the Politics of UX Research and Data Product Design with Caroline Zimmerman
135 - “No Time for That:” Enabling Effective Data Product UX Research in Product-Immature Organizations
134 - What Sanjeev Mohan Learned Co-Authoring “Data Products for Dummies”
133 - New Experiencing Data Interviews Coming in January 2024
132 - Leveraging Behavioral Science to Increase Data Product Adoption with Klara Lindner
131 - 15 Ways to Increase User Adoption of Data Products (Without Handcuffs, Threats and Mandates) with Brian T. O’Neill
130 - Nick Zervoudis on Data Product Management, UX Design Training and Overcoming Imposter Syndrome
129 - Why We Stopped, Deleted 18 Months of ML Work, and Shifted to a Data Product Mindset at Coolblue
128 - Data Products for Dummies and The Importance of Data Product Management with Vishal Singh of Starburst
127 - On the Road to Adopting a “Producty” Approach to Data Products at the UK’s Care Quality Commission with Jonathan Cairns-Terry
126 - Designing a Product for Making Better Data Products with Anthony Deighton
124 - The PiCAA Framework: My Method to Generate ML/AI Use Cases from a UX Perspective
123 - Learnings From the CDOIQ Symposium and How Data Product Definitions are Evolving with Brian T. O’Neill
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