Assessing Gender Bias in Predictive Algorithms using eXplainable AI

Predictive algorithms have a powerful potential to offer benefits in areas as varied as medicine or education. However, these algorithms and the data they use are built by humans, consequently, they can inherit the bias and prejudices present in humans. The outcomes can systematically repeat errors that create unfair results, which can even lead to situations of discrimination (e.g. gender, social or racial). In order to illustrate how important is to count with a diverse training dataset to avoid bias, we manipulate a well-known facial expression recognition dataset to explore gender bias and discuss its implications.

[1]  Silvia Ramis,et al.  A Contrario Detection of Faces with a Short Cascade of Classifiers , 2019, Image Process. Line.

[2]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[3]  Ali Kashif Bashir,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2013, ICIRA 2013.

[4]  Catherine E. Tucker,et al.  Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads , 2019, Manag. Sci..

[5]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[6]  Susan Leavy,et al.  Gender Bias in Artificial Intelligence: The Need for Diversity and Gender Theory in Machine Learning , 2018, 2018 IEEE/ACM 1st International Workshop on Gender Equality in Software Engineering (GE).

[7]  Fabio Vitali,et al.  Modelling GDPR-Compliant Explanations for Trustworthy AI , 2020, EGOVIS.

[8]  Amit Dhurandhar,et al.  One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques , 2019, ArXiv.

[9]  Qian Yang,et al.  Designing Theory-Driven User-Centric Explainable AI , 2019, CHI.

[10]  Q. Liao,et al.  Questioning the AI: Informing Design Practices for Explainable AI User Experiences , 2020, CHI.

[11]  David Gunning,et al.  DARPA's explainable artificial intelligence (XAI) program , 2019, IUI.

[12]  Gholamreza Anbarjafari,et al.  Responsible AI: Gender bias assessment in emotion recognition , 2021, ArXiv.

[13]  Tobias Huber,et al.  Relevance-Based Data Masking: A Model-Agnostic Transfer Learning Approach for Facial Expression Recognition , 2020, Frontiers in Computer Science.

[14]  Cuntai Guan,et al.  A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Alex Pentland,et al.  Fair, Transparent, and Accountable Algorithmic Decision-making Processes , 2017, Philosophy & Technology.

[16]  Andrea Brennen,et al.  What Do People Really Want When They Say They Want "Explainable AI?" We Asked 60 Stakeholders. , 2020, CHI Extended Abstracts.

[17]  Edilson de Aguiar,et al.  Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order , 2017, Pattern Recognit..

[18]  E. Guney,et al.  Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare , 2020, npj Digital Medicine.

[19]  Дмитрий Валерьевич Шмунк,et al.  Image enhancement method , 2009 .

[20]  P. Ekman.  An argument for basic emotions , 1992 .

[21]  David W. Aha,et al.  DARPA's Explainable Artificial Intelligence (XAI) Program , 2019, AI Mag..

[22]  Lijun Yin,et al.  FERA 2015 - second Facial Expression Recognition and Analysis challenge , 2015, 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[23]  Michael Chromik,et al.  A Taxonomy for Human Subject Evaluation of Black-Box Explanations in XAI , 2020, ExSS-ATEC@IUI.

[24]  Luís C. Lamb,et al.  Assessing gender bias in machine translation: a case study with Google Translate , 2018, Neural Computing and Applications.

[25]  Natalia Criado,et al.  Bias and Discrimination in AI: A Cross-Disciplinary Perspective , 2020, IEEE Technology and Society Magazine.

[26]  Tim Miller,et al.  Explanation in Artificial Intelligence: Insights from the Social Sciences , 2017, Artif. Intell..

[27]  Su Jian,et al.  An Image Enhancement Method Based on Gamma Correction , 2009, 2009 Second International Symposium on Computational Intelligence and Design.

[28]  Silvia Ramis Guarinos.  Facial detection and expression recognition applied to social robots , 2019 .