The Gradient: Perspectives on AI Upol Ehsan on Human-Centered Explainable AI and Social Transparency
Human-Centered XAI
- Human-centered XAI shifts focus to the human's interaction with AI.
- It emphasizes understanding human values and thinking processes to improve AI's understandability.
Cybersecurity Breach
- Upol Ehsan recounts a consulting project where a cybersecurity company's AI system had 90% accuracy.
- Despite this, low employee engagement led to a $2 million breach due to a misinterpretation of the AI's advice.
Social Transparency
- AI systems are actually human-AI assemblages requiring a focus beyond just the algorithm.
- Social transparency addresses this by considering the human element within these systems.
In episode 18 of The Gradient Podcast, we talked to Upol Ehsan, an Explainable AI (XAI) researcher who combines his background in Philosophy and Human-Computer Interaction to address problems in XAI beyond just opening the "black-box" of AI. You can find his Gradient article charting this vision here.
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Papers Discussed:
* Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
* Automated Rationale Generation: A Technique for Explainable AI and its Effects on Human Perceptions
* Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach
* Expanding Explainability: Towards Social Transparency in AI systems
* The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations
* Explainability Pitfalls: Beyond Dark Patterns in Explainable AI
Exciting update!
In addition to listening to the audio recording, you can now experience the interview over at The Gradient’s main site, with live captions and the ability to jump to certain sections.
In addition, you can experience it as follows: Interactive Transcript | Transcript PDF | Interview on YouTube
About Upol:Upol Ehsan cares about people first, technology second. He is a doctoral candidate in the School of Interactive Computing at Georgia Tech and an affiliate at the Data & Society Research Institute. Combining his expertise in AI and background in Philosophy, his work in Explainable AI (XAI) aims to foster a future where anyone, regardless of their background, can use AI-powered technology with dignity.
Actively publishing in top peer-reviewed venues like CHI, his work has received multiple awards and been covered in major media outlets. Bridging industry and academia, he serves on multiple program committees in HCI and AI conferences (e.g., DIS, IUI, NeurIPS) and actively connects these communities (e.g, the widely attended HCXAI workshop at CHI). By promoting equity and ethics in AI, he wants to ensure stakeholders who aren’t at the table do not end up on the menu. Outside research, he is an advisor for Aalor Asha, an educational institute he started for underprivileged children subjected to child labor.Follow him on Twitter: @upolehsan
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