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