Gender and Racial Fairness in Depression Research using Social Media

Carlos Aguirre · Keith Harrigian · Mark H. Dredze

37 citations0/0 files storedConference of the European Chapter of the Association for Computational Linguistics

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Ethics and Social Impacts of AI Artificial Intelligence in Healthcare Mental Health via Writing

Abstract

Multiple studies have demonstrated that behavior on internet-based social media platforms can be indicative of an individual's mental health status.The widespread availability of such data has spurred interest in mental health research from a computational lens.While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, such as gender and racial/ethnic groups.Here, we analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial demographic groups.We find that model performance systematically differs for underrepresented groups and that these discrepancies cannot be fully explained by trivial data representation issues.Our study concludes with recommendations on how to avoid these biases in future research.

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References

2003The Epidemiology of Major Depressive Disorder 2016Equality of Opportunity in Supervised Learning 2017Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments 2018Epidemiology of Adult DSM-5 Major Depressive Disorder and Its Specifiers in the United States 2013Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings 2021Predicting Depression via Social Media 2018Fairness in Criminal Justice Risk Assessments: The State of the Art

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2022Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review 2023Gender Bias When Using Artificial Intelligence to Assess Anorexia Nervosa on Social Media: Data-Driven Study 2023Human behavior in the time of COVID-19: Learning from big data

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