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

INSPIRE奖的部分资金来自计算机与信息科学与工程管理局信息与智能系统部的信息集成与信息学计划、社会、行为与经济科学局行为与认知科学部门的感知、行为与认知计划以及董事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决策，一个常见的例子是推荐系统。在这些系统中，计算机被训练来复制成百上千人的合作推荐，如果可能的话。用于训练这些系统的大部分数据不是来自受控随机样本，而是基于算法的输出(例如，用户点击哪些搜索引擎结果？)从用户获得，这在过程中引入了偏差，并最终影响结果的质量。这个项目通过研究最初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，以及如何最终根据机器学习过程中的人类偏见进行调整来解决这个问题。解决的具体领域是过滤(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提供显著的进步。该项目建立在基于隐马尔可夫模型(HMM)的机器学习算法分析的基础上。正式的分析最初着眼于“盲点”--用户没有获得完整(或随机抽样)数据的偏见的影响。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。结果将被用来开发基于解毒剂(改变学习模型以减少偏见)和反应性学习(考虑到人和机器偏见的主动学习)的改进的机器学习方法。PI还计划利用所学到的经验教训，提供在网络挖掘课程中使用认知科学的例子，以及机器学习在数据科学中对心理学家课程的影响。

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.