呼吁扩展社会影响评估标准，避免过度强调部署与创新并重
Fostering the Ecosystem of AI for Social Impact Requires Expanding and Strengthening Evaluation Standards
- 主张放宽对社会影响的定义，不只看部署与方法创新并重
- 强调需对已部署系统进行更严格的实效评估
- 适合关注公益AI可持续发展的研究者与评审人
近年来，面向社会影响的AI/ML研究日益增多，相关发表平台也逐步完善了实践导向研究的评审标准。然而，现有标准往往更明确认可同时实现系统部署与新型机器学习方法创新的项目。我们认为，这种倾向会扭曲研究人员的激励机制，损害更广泛的社会影响研究生态的可持续性。该生态应鼓励在应用或方法上做出单一贡献的项目，这些项目可能更契合合作方的实际需求。因此，我们主张，在社会影响的机器学习研究中，研究者与评审者必须同步采纳：1）超越部署范畴的更广泛社会影响定义；2）对已部署系统开展更严谨的影响评估。
原文摘要 · Abstract (English)
There has been increasing research interest in AI/ML for social impact, and correspondingly more publication venues have refined review criteria for practice-driven AI/ML research. However, these review guidelines tend to most concretely recognize projects that simultaneously achieve deployment and novel ML methodological innovation. We argue that this introduces incentives for researchers that undermine the sustainability of a broader research ecosystem of social impact, which benefits from projects that make contributions on single front (applied or methodological) that may better meet project partner needs. Our position is that researchers and reviewers in machine learning for social impact must simultaneously adopt: 1) a more expansive conception of social impacts beyond deployment and 2) more rigorous evaluations of the impact of deployed systems.
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