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如果有的话，公共部门的价值观如何进入公共部门的机器学习系统？How do public sector values get into public sector machine learning systems, if at all? |
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| 课程网址: | http://videolectures.net/lawandethics2017_veale_public_sector/ |
| 主讲教师: | Michael Veale |
| 开课单位: | 伦敦大学 |
| 开课时间: | 2017-07-24 |
| 课程语种: | 英语 |
| 中文简介: |  更多的采用机器学习算法的决策支持系统每天都在公共部门进行试点和部署，以帮助发现个人和公司在税收，保护儿童和维持治安等方面的不法行为。尽管有些人欢迎这种趋势，因为出现了更多基于证据的行政决策，但另一些人担心，这种系统的不透明性和可感知的客观性会在他们进行正当程序时通过后门带来不必要的偏差。对这些系统的研究主要是试图从外部对其进行查找或反向工程，从而缺少在不同机构环境中获得，部署和管理这些技术的人员。为了填补这一空白，对参与公共部门机器学习项目的不同部门和国家的25名公务员和技术人员进行了识别和采访。他们被问及他们在这些技术方面的经验，重点是他们如何理解和解决所遇到的运营障碍和道德问题。对这些访谈的分析表明，在该领域，诸如“公平意识”或可解释的机器学习系统之类的最新技术方法对责任的承担很有希望。但是，这些访谈还提出了一些问题和问题，这些问题和问题目前都未被重视，并且仅靠技术解决方案是不可能解决的。这项研究表明，应用机器学习的治理机制如果要成功确保新的数据驱动的决策支持系统对社会有益，就必须对地面压力和环境更加敏感。 |
| 课程简介: | More machine learning algorithm–powered decision-support systems are piloted and deployed in the public sector each day to help detect individuals and corporate wrongdoing in areas such as taxation, child protection and policing. While some welcome this trend as the dawn of more evidence-based administrative decisionmaking, others worry that the opacity and perceived objectivity of such systems usher in unwanted biases through the back door just as they kick due process out. Studies of these systems have primarily attempted to look-in or reverse-engineer them from the outside, missing the people that obtain, deploy and manage these technologies within diverse institutional contexts. To help fill this gap, 25 public servants and technologists from different sectors and countries involved in public sector machine learning projects were identified and interviewed. They were asked about their experiences with these technologies, focussing on how they understood and approached operational barriers and ethical issues they encountered. Analysis of these interviews shows promising roles for recent technological approaches to responsibility in this field such as ‘fairness-aware’ or interpretable machine learning systems. Yet these interviews also raise questions and issues that are both currently underemphasised and unlikely to be resolved by technical solutions alone. This research suggests that governance mechanisms for appliedmachine-learning must be more sensitive to on-the-ground pressures and contexts if they are to succeed in ensuring new data-driven decision-support systems are societally beneficial. |
| 关 键 词: | 机器学习; 公共部门; 决策支持系统 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-02-25：cjy |
| 最后编审: | 2021-05-06：cjy |
| 阅读次数: | 45 |