Flexible and Adaptive Fairness-aware Learning in Non-stationary Data Streams

Artificial intelligence (AI)-based decision-making systems are employed nowadays in an ever growing number of online as well as offline services-some of great importance. Depending on sophisticated learning algorithms and available data, these systems are increasingly becoming automated and data-driven. However, these systems can impact individuals and communities with ethical or legal consequences. Numerous approaches have therefore been proposed to develop decision-making systems that are discrimination-conscious by-design. However, these methods assume the underlying data distribution is stationary without drift, which is counterfactual in many realworld applications. In addition, their focus has been largely on minimizing discrimination while maximizing prediction performance without necessary flexibility in customizing the tradeoff according to different applications. To this end, we propose a learning algorithm for fair classification that also adapts to evolving data streams and further allows for a flexible control on the degree of accuracy and fairness. The positive results on a set of discriminated and non-stationary data streams demonstrate the effectiveness and flexibility of this approach.

[1]  Ting Zhu,et al.  2016 Ieee International Conference on Big Data (big Data) Wearable Sensor Based Human Posture Recognition , 2022 .

[2]  Eirini Ntoutsi,et al.  FAHT: An Adaptive Fairness-aware Decision Tree Classifier , 2019, IJCAI.

[3]  Xia Hu,et al.  Fairness in Deep Learning: A Computational Perspective , 2019, IEEE Intelligent Systems.

[4]  Toon Calders,et al.  Three naive Bayes approaches for discrimination-free classification , 2010, Data Mining and Knowledge Discovery.

[5]  Toon Calders,et al.  Building Classifiers with Independency Constraints , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[6]  Josep Domingo-Ferrer,et al.  Discrimination- and privacy-aware patterns , 2014, Data Mining and Knowledge Discovery.

[7]  Jian Tang,et al.  Using the machine learning approach to predict patient survival from high-dimensional survival data , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[8]  João Gama,et al.  Ensemble learning for data stream analysis: A survey , 2017, Inf. Fusion.

[9]  Albert Bifet,et al.  Adaptive XGBoost for Evolving Data Streams , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[10]  Andreas Krause,et al.  Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning , 2019, KDD.

[11]  Ricard Gavaldà,et al.  Adaptive Learning from Evolving Data Streams , 2009, IDA.

[12]  Jianwu Wang,et al.  A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[13]  Michal Wozniak,et al.  Data stream classification using active learned neural networks , 2019, Neurocomputing.

[14]  Khaled Ghédira,et al.  Discussion and review on evolving data streams and concept drift adapting , 2018, Evol. Syst..

[15]  Krishna P. Gummadi,et al.  Fairness Constraints: Mechanisms for Fair Classification , 2015, AISTATS.

[16]  Jianwu Wang,et al.  Content-bootstrapped Collaborative Filtering for Medical Article Recommendations , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[17]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[18]  Zhe Zhao,et al.  Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations , 2017, ArXiv.

[19]  Julia Rubin,et al.  Fairness Definitions Explained , 2018, 2018 IEEE/ACM International Workshop on Software Fairness (FairWare).

[20]  Edward Raff,et al.  What About Applied Fairness? , 2018, ArXiv.

[21]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..

[22]  Judy Goldsmith,et al.  Why Teaching Ethics to AI Practitioners Is Important , 2017, AAAI.

[23]  John Salvatier,et al.  When Will AI Exceed Human Performance? Evidence from AI Experts , 2017, ArXiv.

[24]  Michael Carl Tschantz,et al.  Automated Experiments on Ad Privacy Settings , 2014, Proc. Priv. Enhancing Technol..

[25]  Albert Bifet,et al.  FEAT: A Fairness-Enhancing and Concept-Adapting Decision Tree Classifier , 2020, DS.

[26]  Liuhua Zhang,et al.  A COMPARISON OF DIFFERENT PATTERN RECOGNITION METHODS WITH ENTROPY BASED FEATURE REDUCTION IN EARLY BREAST CANCER CLASSIFICATION , 2014 .

[27]  Michalis Vazirgiannis,et al.  Error-space representations for multi-dimensional data streams with temporal dependence , 2018, Pattern Analysis and Applications.

[28]  Jianwu Wang,et al.  A Hybrid Learning Framework for Imbalanced Stream Classification , 2017, 2017 IEEE International Congress on Big Data (BigData Congress).

[29]  Toon Calders,et al.  Discrimination Aware Decision Tree Learning , 2010, 2010 IEEE International Conference on Data Mining.

[30]  M. Ghassemi,et al.  Can AI Help Reduce Disparities in General Medical and Mental Health Care? , 2019, AMA journal of ethics.

[31]  Allison Woodruff,et al.  Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements , 2019, AIES.

[32]  Nathan Srebro,et al.  Equality of Opportunity in Supervised Learning , 2016, NIPS.

[33]  Krishna P. Gummadi,et al.  Fairness Constraints: A Flexible Approach for Fair Classification , 2019, J. Mach. Learn. Res..

[34]  Miao Jiang,et al.  Achieving Outcome Fairness in Machine Learning Models for Social Decision Problems , 2020, IJCAI.

[35]  George Forman,et al.  Tackling concept drift by temporal inductive transfer , 2006, SIGIR.

[36]  Albert Bifet,et al.  Sentiment Knowledge Discovery in Twitter Streaming Data , 2010, Discovery Science.

[37]  Eirini Ntoutsi,et al.  Fairness-enhancing interventions in stream classification , 2019, DEXA.

[38]  Steven Mills,et al.  Fair Forests: Regularized Tree Induction to Minimize Model Bias , 2017, AIES.

[39]  Phebe Vayanos,et al.  Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making , 2019, AAAI.

[40]  Jianwu Wang,et al.  On Fairness-Aware Learning for Non-discriminative Decision-Making , 2019, 2019 International Conference on Data Mining Workshops (ICDMW).

[41]  Wenbin Zhang,et al.  PhD Forum: Recognizing Human Posture from Time-Changing Wearable Sensor Data Streams , 2017, 2017 IEEE International Conference on Smart Computing (SMARTCOMP).

[42]  Reuben Binns,et al.  Fairness in Machine Learning: Lessons from Political Philosophy , 2017, FAT.

[43]  Michael Skirpan,et al.  The Authority of "Fair" in Machine Learning , 2017, arXiv.org.

[44]  Toon Calders,et al.  Classifying without discriminating , 2009, 2009 2nd International Conference on Computer, Control and Communication.

[45]  Krishna P. Gummadi,et al.  Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment , 2016, WWW.