Building intelligent systems applicable in the real world requires more than prediction. Driving decisions requires causal insights. Reliability requires models that are provably robust under clear assumptions. Deploying data-driven technology in society requires accounting for the complex dynamics and feedback loops mediating this interaction. Aligning with social desiderata such as fairness requires a philosophically coherent treatment. ACMI lab studies core machine learning methods , their applications i
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Zachary Lipton | Associate Professor, Machine Learning, CMU
Approximately Correct – Technical and Social Perspectives on Machine Learning
Pratyush Maini
Sachin Goyal
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T-MARS: Improving Visual Representations by Circumventing Text Feature Learning
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