公共部门机器学习在现实世界中应用的透明性和不透明性的逻辑和实践
Logics and practices of transparency and opacity in real-world applications of public sector machine learning
摘要：
机器学习系统越来越多地用于支持各个部门的公共部门决策。鉴于对这些领域问责制的关注，以及对有意或无意偏见的指责，人们越来越多地呼吁这些技术的透明度。然而，很少有人考虑过，那些参与机器学习系统的人是如何理解有关透明度的逻辑和实践的，这些系统已经在公共机构中试用和部署。这篇简短的论文从对5个经合组织国家的27个此类行为者（主要是公务员和相关承包商）的采访中提炼出关于透明度的见解。考虑到透明度和不透明与信任和买进，更好的决策，并避免游戏，它试图为那些希望开发社会技术方法的人提供有用的见解，这些方法可能对实地的从业人员有用。这篇论文的扩展存档版本可以作为Veale M.，Van Kleek M.，&宾斯河。（2018年）。“公平和问责制设计需要算法支持高风险公共部门决策”2018年CHI计算机系统人为因素会议（CHI'18），http://doi.org/10.1145/3173574.3174014。
英文摘要：
Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors. Given concerns around accountability in these domains, and amidst accusations of intentional or unintentional bias, there have been increased calls for transparency of these technologies. Few, however, have considered how logics and practices concerning transparency have been understood by those involved in the machine learning systems already being piloted and deployed in public bodies today. This short paper distils insights about transparency on the ground from interviews with 27 such actors, largely public servants and relevant contractors, across 5 OECD countries. Considering transparency and opacity in relation to trust and buy-in, better decision-making, and the avoidance of gaming, it seeks to provide useful insights for those hoping to develop socio-technical approaches to transparency that might be useful to practitioners on-the-ground. An extended, archival version of this paper is available as Veale M., Van Kleek M., & Binns R. (2018). `Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making' Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18), http://doi.org/10.1145/3173574.3174014.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
