BIGDATA: Collaborative Research: F: Algorithmic Fairness: A Systemic and Foundational Treatment of Nondiscriminatory Data Mining

数据驱动的建模已经超越了消费者预测和建议的领域，进入了对我们日常生活产生深远影响的政策和规划领域。数据分析工具正被用于预测犯罪、选择工作候选人、识别安全威胁、确定信用风险，甚至为患者制定治疗计划和干预措施。自动化学习和挖掘工具可以处理数量惊人、种类繁多的数据，以检测模式并做出预测。正如迅速变得清晰的那样，这些工具也可能引入歧视行为，并在它们所训练的系统中放大偏见。在这个项目中，pi将研究算法决策中的歧视和偏见问题。通过研究数据管道的各个方面（从数据准备到学习、评估和反馈），他们将开发用于分析、审计和设计自动化决策系统的工具，这些系统将是公平、负责和透明的。作为扩大这项研究影响的具体目标，pi将开发一门课程，教育下一代数据科学家关于算法决策的伦理、法律和社会影响，目的是让他们在进入劳动力市场时将这些理解应用到他们的工作中。ppi最初的努力吸引了来自计算机科学中代表性不足的群体的学生，他们将继续这些努力。pi还将探索这项研究的法律和政策影响，并为政策制定者、律师、记者和其他从业者使用其工具制定最佳实践指南。pi将以三种方式探索这个项目的技术主题。首先，他们将为算法公平性的推理建立一个健全的理论框架。这个框架仔细地分离了机制、信念和假设，以便对学习公平性的本质做出明确的隐含的假设。其次，通过检查与学习相关的整个任务管道，他们将确定迄今为止尚未探索的领域，其中偏见可能无意中引入学习，以及与确保公平相关的新问题。这包括数据准备的初始阶段、各种公平意识学习和评估。他们还将研究反馈的问题：当基于有偏见的学习模型的行为可能会导致反馈循环，改变现实并导致更多的偏见。

Data-driven modeling has moved beyond the realm of consumer predictions and recommendations into areas of policy and planning that have a profound impact on our daily lives. The tools of data analysis are being harnessed to predict crime, select candidates for jobs, identify security threats, determine credit risk, and even decide treatment plans and interventions for patients. Automated learning and mining tools can crunch incredible amounts and variety of data in order to detect patterns and make predictions. As is rapidly becoming clear, these tools can also introduce discriminatory behavior and amplify biases in the systems they are trained on. In this project, the PIs will study the problems of discrimination and bias in algorithmic decision-making. By studying all aspects of the data pipeline (from data preparation to learning, evaluation, and feedback), they will develop tools for analyzing, auditing, and designing automated decision-making systems that will be fair, accountable, and transparent. As specific goals to broaden the impact of this research, the PIs will develop a course curriculum to educate the next generation of data scientists on the ethical, legal, and societal implications of algorithmic decision-making, with the intent that they will then take this understanding into their jobs as they enter the workforce. Initial efforts by the PIs have attracted students from underrepresented groups in computer science, and they will continue these efforts. The PIs will also explore the legal and policy ramifications of this research, and develop best practice guidelines for the use of their tools by policy makers, lawyers, journalists, and other practitioners.The PIs will explore the technical subject of this project in three ways. Firstly, they will develop a sound theoretical framework for reasoning about algorithmic fairness. This framework carefully separates mechanisms, beliefs, and assumptions in order to make explicit implicitly held assumptions about the nature of fairness in learning. Secondly, by examining the entire pipeline of tasks associated with learning, they will identify hitherto unexplored areas where bias may be unintentionally introduced into learning as well as novel problems associated with ensuring fairness. These include the initial stages of data preparation, various kinds of fairness-aware learning, and evaluation. They will also investigate the problem of feedback: when actions based on a biased learned model might cause a feedback loop that changes reality and leads to more bias.