INSPIRE: Not Unbiased: The Implications of Human-Algorithm Interaction on Training Data and Algorithm Performance

INSPIRE：并非公正：人机交互对训练数据和算法性能的影响

• 批准号: 1549981
• 负责人: Olfa Nasraoui
• 金额: $ 81.32万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549981&HistoricalAwards=false
• 关键词: INSPIRE; Unbiased; Implications; Human; Algorithm

This INSPIRE award is partially funded by the Information Integration and Informatics program in the Division of Information and Intelligent Systems in the Directorate for Computer & Information Science & Engineering, the Perception, Action & Cognition program in the Division of Behavioral and Cognitive Sciences in the Directorate for Social, Behavioral & Economic Sciences, and the Office of Integrative Activities in the Office of the Director.One of the most common uses of machine learning is to learn to replicate human decisions, a common example is recommender systems. In these systems, computers are trained to replicate the recommendation a collaboration of hundreds or thousands of humans would give, if that were possible. Most of the data used to train these systems are not from a controlled random sample, but are obtained from users based on outputs of algorithms (e.g., which search engine results do users click on?), which introduces bias into the process and ultimately impacts the quality of the results. This project addresses this problem by examining how the human decision process that creates these data in the first place is affected by the data coming from machine algorithms, how this in turn impacts the algorithms themselves, and how to ultimately adjust for human bias in the machine learning process. Specific areas tackled are filtering (e.g., web search) and recommender systems. The deep research into how the human decision process affects machine learning, and how machine learning impacts the human decision process, can provide significant advances in the accuracy and utility of systems using machine learning.The project builds on analysis of machine learning algorithms based on Hidden Markov Models (HMMs). The formal analysis initially looks at "blind spots" - the impact of bias from users not getting complete (or a random sample) of data. Further analysis will be based on the outcome of two human experiments: Two category recommendation (labeling items, with items to be labelled chosen by random, active learning, and filter-based algorithms), and movie recommendation. The results will be used to develop improved machine learning approaches based on antidotes (altering learned models to reduce bias) and reactive learning (active learning that takes into account the human and machine biases). The PIs also have plans to capitalize on the lessons learned by providing examples of the use of cognitive science in a Web Mining course, and of the impact of machine learning in Data Science for Psychologists courses.

该INSPIRE奖的部分资金来自计算机与信息科学与工程系信息与智能系统部的信息集成与信息学项目，社会、行为与经济科学部行为与认知科学部的感知、行动与认知项目，以及主任办公室的综合活动办公室。机器学习最常见的用途之一是学习复制人类的决策，一个常见的例子是推荐系统。在这些系统中，计算机经过训练，可以复制数百或数千人合作给出的建议（如果可能的话）。用于训练这些系统的大多数数据不是来自受控的随机样本，而是基于算法输出从用户那里获得的（例如，用户点击了哪些搜索引擎结果？），这在过程中引入了偏见，并最终影响了结果的质量。该项目通过研究首先创建这些数据的人类决策过程如何受到来自机器算法的数据的影响，这反过来又如何影响算法本身，以及如何最终调整机器学习过程中的人类偏见来解决这个问题。具体处理的领域是过滤（例如，网络搜索）和推荐系统。深入研究人类决策过程如何影响机器学习，以及机器学习如何影响人类决策过程，可以在使用机器学习的系统的准确性和实用性方面取得重大进展。该项目建立在对基于隐马尔可夫模型（hmm）的机器学习算法的分析之上。正式分析最初着眼于“盲点”——用户不完整（或随机抽样）数据的偏见影响。进一步的分析将基于两个人类实验的结果：两个类别推荐（标记项目，通过随机、主动学习和基于过滤器的算法选择要标记的项目）和电影推荐。研究结果将用于开发基于解毒剂（改变学习模型以减少偏差）和反应性学习（考虑人类和机器偏差的主动学习）的改进机器学习方法。pi还计划通过在网络挖掘课程中提供认知科学的应用实例，以及在心理学家数据科学课程中提供机器学习的影响，来利用所学到的经验教训。