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hello everyone and thanks for joining us the conversation about explainable ai today almost always includes references to wyman chap methods that fall under the classification of removal based explanations removal-based explanations identify the important features by removing and measuring the impact on prediction interestingly were going to talk about the work that analyze 25 different methods that follow that approach and were going to be discussing questions as to what are the common patterns between these methods what are the choices and assumptions made by many of these explanatory methods my name is aliya sharif and im a steering committee member at aggregate intellect today we are very pleased to be hosting ian colbert ian is a phd student in computer science at the university of washington where he works on machine learning research with his advisor sue and lee his research focuses on developing rigorous foundations for explainable ai particularly as it relates to game theor