Understanding and improving fairness in cognitive diagnosis  

作　　者：Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN 

机构地区：[1]Anhui Province Key Laboratory of Big Data Analysis and Application,School of Computer Science and Technology&School of Data Science,University of Science and Technology of China,Hefei 230026,China [2]State Key Laboratory of Cognitive Intelligence,Hefei 230088,China [3]School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China

出　　处：《Science China(Information Sciences)》2024年第5期95-110,共16页中国科学（信息科学）（英文版）

基　　金：supported in part by National Key Research and Development Program of China(Grant No.2021YFF0901003);National Natural Science Foundation of China(Grant Nos.61922073,U20A20229);University Synergy Innovation Program of Anhui Province(Grant No.GXXT-2022-042)。

摘　　要：Intelligent education is a significant application of artificial intelligence.One of the key research topics in intelligence education is cognitive diagnosis,which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g.,Geometry).To the best of our knowledge,most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues;for instance,the diagnosis of students may be affected by various sensitive attributes(e.g.,region).In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1)Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2)If yes,how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results?To this end,we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances,and the trend of unfairness varies among different cognitive diagnosis models.Then,we make a theoretical analysis to explain the reasons behind this phenomenon.To resolve the unfairness problem in existing cognitive diagnosis models,we propose a general fairness-aware cognitive diagnosis framework,FairCD.Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency.To achieve this,we divide student proficiency in existing cognitive diagnosis models into two components:bias proficiency and fair proficiency.We design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result.Extensive experiments on the Program for International Student Assessment(PISA)dataset clearly show the effectiveness of our framework.

关 键 词：FAIRNESS intelligent education cognitive diagnosis PSYCHOMETRICS adversarial learning 

分 类 号：G434[文化科学—教育学] TP18[文化科学—教育技术学]