Collaborative Research: IIS-III Towards Fair Outlier Detection

异常值检测是机器学习和数据挖掘中的一个常见问题，其中分析了一系列实例/记录/对象，系统识别出那些突出的。这有可能成为人工智能方法的争议性使用，因为典型的结果是给物品/个人贴上不寻常的标签，通常带有负面含义。异常值检测广泛应用于欺诈检测、监视和警务等领域。有许多异常值检测算法，但它们通常不具有公平性意识，这意味着它们可能会无意中歧视受保护的地位群体或子群体，而这些群体往往与众不同。该奖项解决了将公平性编码到各种类型的异常点检测算法中的问题，包括基于传统数据挖掘的算法和基于现代深度学习的算法。为异常值检测增加公平性将使其能够用于更广泛的任务，同时确保这些算法不会歧视。该项目包括三个核心任务，将在社交媒体和医学成像应用方面进行评估。第一项任务包括定义如何衡量公平。第二个任务探讨如何为诸如审计算法的输出以识别不公平以及如何对异常值检测算法的结果进行后处理以满足公平性要求等任务编码公平性。最后，第三个任务探讨了为用于离群值检测的现代基于深度学习的算法添加公平性。将公平性考虑纳入机器学习算法是一个重要且相对未被充分研究的问题——这可能是由于算法类型的多样性。本项目探讨了如何将公平性机制纳入各种异常值检测算法。对于更传统的离群值检测算法，它探索审计算法以确定输出是否不公平，然后对输出进行最小限度的后处理以使其更公平。这样做将涉及将这些问题形成离散的优化问题，搜索不公平的例子，并搜索哪些实例可以在异常类和正常类之间移动以提高公平性。对于离群值检测的深度学习公式，该项目将探索通过多种不同的策略将公平性直接编码到训练算法中，其核心目标是确定哪种策略最合适、最有用。深度公平异常值检测的一个特殊挑战是，异常值可以以几种方式呈现：i)使用阈值，ii)作为有序列表，或iii)作为分数。该项目将研究这三种环境。它将评估社交媒体平台上用于账户和内容过滤的第一种异常值检测（使用SNAP），以及采用异常值检测进行数据预处理的最后两种医学成像应用（与加州大学戴维斯分校医学中心）。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Outlier detection is a common problem in machine learning and data mining, in which a collection of instances/records/objects is analyzed and the system identifies ones that stand out. It has the potential of being a controversial use of AI methods, as a typical outcome is to label an item/individual as being unusual, often with negative connotations. Outlier detection is used extensively in the context of fraud detection, surveillance, and policing in numerous domains. There are many outlier-detection algorithms, but they are typically not fairness-aware, meaning they could inadvertently discriminate against protected status groups or subgroups, which often stand out from the norm. This award addresses the problem of encoding fairness into various types of outlier-detection algorithms, both traditional data-mining based as well as modern deep learning based. Adding fairness to outlier detection will allow it to be used in a wider variety of tasks while ensuring that these algorithms do not discriminate. The project consists of three core tasks, to be evaluated on social media and medical imaging applications. The first task consists of defining how to measure fairness. The second task explores how to encode fairness for tasks such as auditing the output of an algorithm to identify unfairness and how to post-process the results of an outlier-detection algorithm to meet fairness requirements. Finally, the third task explores adding fairness to modern deep learning-based algorithms used for outlier detection.Incorporating fairness considerations into machine-learning algorithms is an important and relatively understudied problem—potentially due to the wide variety of algorithm types. This project explores how to include fairness mechanisms into a wide variety of outlier-detection algorithms. For more traditional outlier-detection algorithms, it explores auditing the algorithm to determine if the output is unfair and then minimally post-processing the output to make it fairer. Doing so will involve formulating these problems as discrete optimization problems that search for examples of unfairness and search for which instances to move between the outlier and normal classes to elevate fairness. For deep learning formulations of outlier detection, the project will explore directly encoding fairness into the training algorithms via a number of different strategies, with a core goal of determining which is the most appropriate and useful. A particular challenge for deep fair outlier detection is that outliers can be presented in several ways: i) using thresholds, ii) as an ordered list, or iii) as a score. The project will study all three settings. It will evaluate the first type of outlier detection on a social media platform for account and content filtering (with SNAP), and the last two types on medical imaging applications that employ outlier detection for data preprocessing (with UC Davis Medical Center).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.