Towards Equity and Algorithmic Fairness in Student Grade Prediction
暂无分享,去创建一个
[1] Chris Piech,et al. Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction , 2018, ArXiv.
[2] Zhe Zhao,et al. Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations , 2017, ArXiv.
[3] Toniann Pitassi,et al. Fairness through awareness , 2011, ITCS '12.
[4] Kevin G. Welner,et al. Closing the opportunity gap : what America must do to give every child an even chance , 2013 .
[5] Erez Shmueli,et al. Algorithmic Fairness , 2020, ArXiv.
[6] Christopher Brooks,et al. Evaluating the Fairness of Predictive Student Models Through Slicing Analysis , 2019, LAK.
[7] Horacio Matos-Díaz,et al. Do student evaluations of teaching depend on the distribution of expected grade? , 2010 .
[8] Tom Routen,et al. Intelligent Tutoring Systems , 1996, Lecture Notes in Computer Science.
[9] Zachary C. Lipton,et al. Algorithmic Fairness from a Non-ideal Perspective , 2020, AIES.
[10] Charlton D. McIlwain. Computerize the Race Problem?: Why We Must Plan for a Just AI Future , 2020, AIES.
[11] Nathan Srebro,et al. Equality of Opportunity in Supervised Learning , 2016, NIPS.
[12] George Chen,et al. Measuring financial implications of an early alert system , 2016, LAK.
[13] C. Weaver,et al. A more explicit grading scale decreases grade inflation in a clinical clerkship. , 2007, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.
[14] Xia Hu,et al. Fairness in Deep Learning: A Computational Perspective , 2019, IEEE Intelligent Systems.
[15] Jon M. Kleinberg,et al. Inherent Trade-Offs in the Fair Determination of Risk Scores , 2016, ITCS.
[16] Zachary A. Pardos,et al. Time slice imputation for personalized goal-based recommendation in higher education , 2019, RecSys.
[17] Allison Woodruff,et al. Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements , 2019, AIES.
[18] Christoph Bartneck,et al. Robots Can Be More Than Black And White: Examining Racial Bias Towards Robots , 2019, AIES.
[19] Toon Calders,et al. Building Classifiers with Independency Constraints , 2009, 2009 IEEE International Conference on Data Mining Workshops.
[20] Iris Howley,et al. Assessing Post-hoc Explainability of the BKT Algorithm , 2020, AIES.
[21] Emma Brunskill,et al. Fairer but Not Fair Enough On the Equitability of Knowledge Tracing , 2019, LAK.
[22] Eitel J. M. Lauría,et al. Early Alert of Academically At-Risk Students: An Open Source Analytics Initiative , 2014, J. Learn. Anal..
[23] Zachary A. Pardos,et al. Goal-based Course Recommendation , 2018, LAK.
[24] Shayan Doroudi,et al. Towards Accurate and Fair Prediction of College Success: Evaluating Different Sources of Student Data , 2020, EDM.
[25] Toniann Pitassi,et al. Learning Adversarially Fair and Transferable Representations , 2018, ICML.
[26] Toon Calders,et al. Data preprocessing techniques for classification without discrimination , 2011, Knowledge and Information Systems.
[27] Ramesh Johari,et al. How a data-driven course planning tool affects college students' GPA: evidence from two field experiments , 2018, L@S.
[28] Mitchell L. Stevens,et al. AI and Holistic Review: Informing Human Reading in College Admissions , 2019, AIES.
[29] Sidney K. D'Mello,et al. Evaluating Fairness and Generalizability in Models Predicting On-Time Graduation from College Applications , 2019, EDM.
[30] George Karypis,et al. Feature Extraction for Next-Term Prediction of Poor Student Performance , 2019, IEEE Transactions on Learning Technologies.
[31] Saif Mohammad,et al. Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems , 2018, *SEMEVAL.
[32] Xing Xie,et al. Fairness-aware News Recommendation with Decomposed Adversarial Learning , 2020, AAAI.
[33] Jieyu Zhao,et al. Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[34] Kris D. Gutiérrez,et al. Social Design Experiments: Toward Equity by Design , 2016, Cultural-Historical Activity Theory Approaches to Design-Based Research.
[35] G. Wyness,et al. Grade Expectations: How well can we predict future grades based on past performance? , 2020 .
[36] Blake Lemoine,et al. Mitigating Unwanted Biases with Adversarial Learning , 2018, AIES.
[37] Hansol Lee,et al. Algorithmic Fairness in Education , 2020, ArXiv.
[38] Matthew D. Pistilli,et al. Course signals at Purdue: using learning analytics to increase student success , 2012, LAK.
[39] Zachary A. Pardos,et al. Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance , 2018, User Modeling and User-Adapted Interaction.
[40] Franco Turini,et al. Discrimination-aware data mining , 2008, KDD.
[41] Krishna P. Gummadi,et al. Accounting for Model Uncertainty in Algorithmic Discrimination , 2019, AIES.
[42] Pratik Gajane,et al. On formalizing fairness in prediction with machine learning , 2017, ArXiv.
[43] Sergey Karayev,et al. Grades are not Normal: Improving Exam Score Models Using the Logit-Normal Distribution , 2019, EDM.