Collaborative Research: IIS-III: Small Towards Fair Outlier Detection

协作研究：IIS-III：小到公平的异常值检测

• 批准号: 2310481
• 负责人: Ian Davidson
• 金额: $ 29.69万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310481&HistoricalAwards=false
• 关键词: Collaborative; Research; IIS; III; Small

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.

异常值检测是机器学习和数据挖掘中的一个常见问题，其中分析实例/记录/对象的集合，并系统识别出突出的异常值，作为一种典型的人工智能方法，它有可能成为有争议的使用。结果是将某个项目/个人标记为异常，通常具有负面含义。 异常值检测通常用于许多领域的欺诈检测、监视和警务环境中。有许多异常值检测算法，但通常没有。公平意识，意义他们可能会无意中歧视受保护的状态群体或子群体，而这些群体或子群体通常会脱颖而出。该奖项解决了将公平性编码到各种类型的异常值检测算法中的问题，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。增加异常值检测的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。该项目由三个核心任务组成，将在社交媒体和医学成像应用程序上进行评估。定义如何衡量公平性。第三个任务探讨了如何对任务的公平性进行编码，例如审核算法的输出以识别不公平性，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。最后，第三个任务探讨了为现代深度学习添加公平性。用于异常值检测的基于算法。将公平性考虑因素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多种多样，该项目探讨了如何将公平机制纳入各种异常值检测中。对于更传统的异常值检测算法，它探索审核算法以确定输出是否不公平，然后对输出进行最少的后处理以使其更公平，这样做将涉及将这些问题表述为搜索示例的离散优化问题。对于异常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个是最多的深度公平异常值检测的一个特殊挑战是，异常值可以通过多种方式呈现：i）使用阈值，ii）作为有序列表，或 iii）作为分数。将评估社交媒体平台上用于帐户和内容过滤的第一种异常值检测（使用 SNAP），以及使用异常值检测进行数据预处理的医学成像应用程序的最后两种类型（与加州大学戴维斯分校医学中心一起）。该奖项 NSF法定的使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。