XAI Tutorials
If you have less than 3 hours to spare & want to learn (almost) everything about state-of-the-art explainable ML, please read on! Below, I am sharing info about 4 of our recent tutorials on explainability presented at NeurIPS, #AAAI, #FAccT, and #CHIL conferences. NeurIPS 2020: Our longest tutorial (2 hours 46 mins) discusses various types of explanation methods, their limitations, evaluation frameworks, applications to domains such as decision making/nlp/vision, and open problems. Slides and Video: https://t.co/iOkixtoRyi AAAI 2021: Can't spend 2 hours 46 mins on this topic? No problem! Our tutorial at AAAI 2021 is right here (1 hour 32 mins). This one discusses different explanation methods, their limitations, evaluation, and open problems. Slides and video: https://t.co/5tusl3WBzQ FAccT 2021: Want to know more about the ethical/practical implications of explainability along with a gentle intro to the topic? Our tutorial on "Explainable ML in the Wild" (1 hr 31 mins) might be helpful. Slides: https://t.co/L21ZumDhLA Video: https://t.co/Qwy8LflY9V CHIL 2021: Alright, you can't even spare 1 hr 30 mins you say, no worries! Our shortest tutorial (just 1 hour) on this topic gives a quick overview of various state-of-the-art methods, their limitations, open problems. Slides: https://t.co/pZYQLxJZEs Video: https://t.co/n0g4M2olnX Special thanks to all the collaborators Sameer Singh Julius Adebayo Chirag Agarwal Shalmali Joshi Lastly, if you have more than 3 hours to spare, then this might be of interest. I recently taught a full fledged seminar course on explainability in ML at Harvard University. All the readings and slides will be posted very soon. Stay tuned! Meanwhile, last year's version of the course is at https://t.co/PAmv6xSuli
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