It's Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Process

By Brent J. Hecht, Lauren Wilcox, Jeffrey P. Bigham, Johannes Schöning, Ehsan Hoque, J. Ernst, Yonatan Bisk, Luigi De Russis, L. Yarosh, Bushra Anjum and 2 more. 2021

113 citations recorded by the source corpus.

The computing research community needs to work much harder to address the downsides of our innovations. Between the erosion of privacy, threats to democracy, and automation's effect on employment (among many other issues), we can no longer simply assume that our research will have a net positive impact on the world. While bending the arc of computing innovation towards societal benefit may at first seem intractable, we believe we can achieve substantial progress with a straightforward step: making a small change to the peer review process. As we explain below, we hypothesize that our recommended change will force computing researchers to more deeply consider the negative impacts of their work. We also expect that this change will incentivize research and policy that alleviates computing's negative impacts.

Subfields: Computer Science Applications, AI Ethics, Fairness & Society, Social Sciences & Humanities

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Citations in the corpus

Defending Against Neural Fake News (2019, background): “We conclude with a sketch of the ethical territory that must be mapped out in order to understand our responsibilities as researchers when studying fake news, and the potential negative implications of releasing models (Hecht et al., 2018; Zellers, 2019; Solaiman et al., 2019).”
BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search (2019, background): “Because of the recent push for explicitly reasoning about the impact of research in AI (Hecht et al. 2018), we are hopeful that neural architecture search will be used to benefit society.”
Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning (2019, background): “• Encourage impact evaluation both positive and negative, for research publications, presentations, and proposals (such as that proposed by the ACM FCA [19]).”
Behavioral Use Licensing for Responsible AI (2020, background): “Recently there have also been calls for AI researchers to consider the negative impacts of their work [33].”
Just What do You Think You're Doing, Dave?' A Checklist for Responsible Data Use in NLP (2021, background): “…in dire need of research into the cost–benefit of the use of its systems (Rogers, 2021). of these (and many other) issues see Ashurst et al. (2020), Hecht et al. (2018), and the NAACL 2021 Ethics FAQ • responsible data use/reuse: whether the collection methodology is sufficiently described,…”
A Study on Encodings for Neural Architecture Search (2020, background): “Due to the recent push for the AI community to be more conscious and prescient about the societal impact of its work [3], we are hoping that future AI models, including ones influenced by our work, will have a positive impact on society.”
Data Science as Political Action: Grounding Data Science in a Politics of Justice (2018, background): “As one example of a reform that emphasizes impacts as a central concern, in 2018 the ACM Future of Computing Academy proposed that peer reviewers should consider the potential negative implications of submitted work and that conducting “anti-social research” should factor negatively into promotion a”
Local Search is State of the Art for Neural Architecture Search Benchmarks (2020, background): “Because of the recent push for explicitly reasoning about the impact of research in AI [12], we are hopeful that downstream deep learning applications will be used to beneﬁt society.”