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Ethics statements have been proposed as a mechanism to increase transparencyand promote reflection on the societal impacts of published research. In 2020,the machine learning (ML) conference NeurIPS broke new ground by requiring thatall papers include a broader impact statement. This requirement was removed in2021, in favour of a checklist approach. The 2020 statements therefore providea unique opportunity to learn from the broader impact experiment: toinvestigate the benefits and challenges of this and similar governancemechanisms, as well as providing an insight into how ML researchers think aboutthe societal impacts of their own work. Such learning is needed as NeurIPS andother venues continue to question and adapt their policies. To enable this, wehave created a dataset containing the impact statements from all NeurIPS 2020papers, along with additional information such as affiliation type, locationand subject area, and a simple visualisation tool for exploration. We alsoprovide an initial quantitative analysis of the dataset, coveringrepresentation, engagement, common themes, and willingness to discuss potentialharms alongside benefits. We investigate how these vary by geography,affiliation type and subject area. Drawing on these findings, we discuss thepotential benefits and negative outcomes of ethics statement requirements, andtheir possible causes and associated challenges. These lead us to severallessons to be learnt from the 2020 requirement: (i) the importance of creatingthe right incentives, (ii) the need for clear expectations and guidance, and(iii) the importance of transparency and constructive deliberation. Weencourage other researchers to use our dataset to provide additional analysis,to further our understanding of how researchers responded to this requirement,and to investigate the benefits and challenges of this and related mechanisms.

AI Ethics Statements -- Analysis and lessons learnt from NeurIPS Broader Impact Statements