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The computer science research community and the broader public have becomeincreasingly aware of negative consequences of algorithmic systems. Inresponse, the top-tier Neural Information Processing Systems (NeurIPS)conference for machine learning and artificial intelligence research requiredthat authors include a statement of broader impact to reflect on potentialpositive and negative consequences of their work. We present the results of aqualitative thematic analysis of a sample of statements written for the 2020conference. The themes we identify broadly fall into categories related to howconsequences are expressed (e.g., valence, specificity, uncertainty), areas ofimpacts expressed (e.g., bias, the environment, labor, privacy), andresearchers' recommendations for mitigating negative consequences in thefuture. In light of our results, we offer perspectives on how the broaderimpact statement can be implemented in future iterations to better align withpotential goals.

Unpacking the Expressed Consequences of AI Research in Broader Impact Statements