Health Citizens – European Research Institute

Tag: Evaluation Bias

  • A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

    A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

    This study examines classification parity across sex and finds that female adolescents have systematically under-diagnosed mental health disorders: their model’s accuracy was ~4 % lower and false negative rate ~9 % higher compared to male patients. The source of the bias resides in the textual data, namely notes corresponding to male patients tended to be on average 500 words longer and had distinct word usage. To mitigate this, the authors introduce a de-biasing method, based on neutralizing biased terms (gendered words and pronouns) and reducing sentences to essential clinical information. After correcting, diagnostic bias is reduced by up to 27%.

    This emphasizes how linguistically transmitted bias—ensuing from word choice and gendered language—consistently leads to the under-diagnosis of mental health disorders among female adolescents, which critically undermines the impartiality of medical diagnosis and treatment.

    Learn more about this study here: https://doi.org/10.48550/arXiv.2501.00129

    Reference

    Ive, J., Bondaronek, P., Yadav, V., Santel, D., Glauser, T., Cheng, T., Strawn, J.R., Agasthya, G., Tschida, J., Choo, S., Chandrashekar, M., Kapadia, A.J., & Pestian, J.P. (2024). A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection. 

  • The Role of Gender: Gender Fairness in the Detection of Depression Symptoms on Social Media

    The Role of Gender: Gender Fairness in the Detection of Depression Symptoms on Social Media

    The study found that the BDI-Sen dataset used for depression symptom detection on social media exhibits gender bias, with machine learning models such as mentalBERT showing predictive disparities that generally favour male users. Although bias mitigation techniques like data augmentation reduced the bias, they did not eliminate it completely.

    The existence of this bias affects the fairness and reliability of AI systems in detecting depression symptoms, leading to unequal predictive performance across genders. This can result in under- or over-identification of depression symptoms in certain groups, thereby compromising the validity of such systems for clinical or mental health monitoring.

    Learn more about this study here: https://studenttheses.uu.nl/handle/20.500.12932/47734

    Reference

    Gierschmann, Lara (2024), The Role of Gender: Gender Fairness in the Detection of Depression Symptoms on Social Media, Utrecht University, unpublished Master Thesis

  • Gender Bias in AI’s Perception of Cardiovascular Risk

    Gender Bias in AI’s Perception of Cardiovascular Risk

    The study investigated gender bias in GPT-4’s assessment of coronary artery disease risk and showed that there was a substantial shift in the perception of risk between men and women when a psychiatric comorbidity was added to the vignette, even when they presented identical complaints.

    This resulted in women being assessed as having as lower risk of CAD when concurrently having a psychiatric condition.

    Learn more about this study here: https://www.jmir.org/2024/1/e54242

    Reference

    Achtari M, Salihu A, Muller O, Abbé E, Clair C, Schwarz J, Fournier S
    Gender Bias in AI’s Perception of Cardiovascular Risk
    J Med Internet Res 2024;26:e54242
    DOI: 10.2196/54242

  • Fairness in AI-Based Mental Health: Clinician Perspectives and Bias Mitigation

    Fairness in AI-Based Mental Health: Clinician Perspectives and Bias Mitigation

    Considering how there is limited research on fairness in automated decision making systems in the clinical domain, particularly in the mental health domain, this study explores clinicians’ perceptions of AI fairness through two distinct scenarios: violence risk assessment and depression phenotype recognition using textual clinical notes.

    Clinicians were engaged with through semi-structured interviews to understand their fairness perceptions and to identify appropriate quantitative fairness objectives for these scenarios. Then, a set of bias mitigation strategies were compared, developed to improve at least one of the four selected fairness objectives. The findings underscore the importance of carefully selecting fairness measures, as prioritizing less relevant measures can have a detrimental rather than a beneficial effect on model behavior in real-world clinical use.

    Learn more about the article here: https://doi.org/10.1609/aies.v7i1.31732

    Reference

    Sogancioglu, G., Mosteiro, P., Salah, A. A., Scheepers, F., & Kaya, H. (2024). Fairness in AI-Based Mental Health: Clinician Perspectives and Bias Mitigation. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7(1), 1390-1400.

  • Deconstructing demographic bias in speech-based machine learning models for digital health

    Deconstructing demographic bias in speech-based machine learning models for digital health

    This study investigates algorithmic bias in AI tools that predict depression risk using smartphone-sensed behavioral data.

    It finds that the model underperforms across several demographic subgroups, including gender, race, age, and socioeconomic status, often misclassifying individuals with depression as low-risk. For example, older adults and Black or low-income individuals were frequently ranked lower in risk than healthier younger or White individuals.

    These biases stem from inconsistent relationships between sensed behaviors and depression across groups. The authors emphasized the need for subgroup-specific modeling to improve fairness and reliability in mental health AI tools.

    Learn more about this study here: https://doi.org/10.3389/fdgth.2024.1351637

    Reference

    Yang M, El-Attar AA and Chaspari T (2024) Deconstructing demographic bias in speech-based machine learning models for digital health. Front. Digit. Health 6: 1351637. 

  • Bias Discovery in Machine Learning Models for Mental Health

    Bias Discovery in Machine Learning Models for Mental Health

    This article examined how AI can unintentionally reproduce social and demographic biases when applied to mental health prediction. Using benzodiazepine prescriptions as a proxy for conditions such as depression and anxiety, a study analyzed machine learning models trained on patient data to identify systematic disparities.

    It found that women are more frequently predicted to receive such treatments, reflecting gender bias, while the models perform less accurately for minority ethnic groups, indicating representation and evaluation bias. The AI models here are not used to prescribe drugs but rather to predict treatment likelihoods, revealing how bias in healthcare data can lead to inequitable AI performance in the context of depression-related care.

    Learn more about the article here: https://doi.org/10.3390/info13050237

    Reference

    Mosteiro, P.J., Kuiper, J., Masthoff, J., Scheepers, F., & Spruit, M. (2022). Bias Discovery in Machine Learning Models for Mental Health. Inf., 13, 237.

  • Digital health tools for the passive monitoring of depression: a systematic review of methods

    Digital health tools for the passive monitoring of depression: a systematic review of methods

    This systematic review examines studies linking passive data from smartphones and wearables to depression, identifying key methodological flaws and threats to reproducibility. It highlights biases such as representation, measurement, and evaluation bias, stemming from small, homogenous samples and inconsistent feature construction.

    Although gender and race are not explicitly discussed, the lack of diversity in study populations suggests potential demographic bias. The review calls for improved reporting standards and broader sample inclusion to enhance generalizability and clinical relevance. These improvements are essential for ensuring that digital mental health tools are equitable and reliable across diverse populations.

    Learn more about this review here: https://doi.org/10.1038/s41746-021-00548-8

    Reference

    De Angel, V., Lewis, S., White, K., Oetzmann, C., Leightley, D., Oprea, E., Lavelle, G., Matcham, F., Pace, A., Mohr, D. C., Dobson, R., & Hotopf, M. (2022). Digital health tools for the passive monitoring of depression: a systematic review of methods. NPJ digital medicine, 5(1), 3.