Health Citizens – European Research Institute

Tag: Measurement Bias

  • Developing personalized algorithms for sensing mental health symptoms in daily life

    Developing personalized algorithms for sensing mental health symptoms in daily life

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

    It finds that these tools underperform in larger, more diverse populations because the behavioral patterns used to predict depression are inconsistent across demographic and socioeconomic subgroups.

    Specifically, the AI models often misclassify individuals from certain groups—such as older adults or those from different racial or gender backgrounds—as being at lower risk than they actually are. The authors emphasize the need for tailored, subgroup-aware approaches to improve reliability and fairness in mental health prediction tools. This work highlights the importance of addressing demographic bias to ensure equitable AI deployment in mental healthcare.

    Learn more about this study here: https://doi.org/10.1038/s44184-025-00147-5

    Reference

    Timmons, A.C., Tutul, A.A., Avramidis, K. et al. Developing personalized algorithms for sensing mental health symptoms in daily life. npj Mental Health Res 4, 34 (2025).

  • 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. 

  • 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.

  • Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121

    Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121

    Depression and anxiety are common, often co-occurring mental health disorders that complicate diagnosis due to overlapping symptoms and reliance on subjective assessments.

    Standard diagnostic tools are widely used but can introduce bias, as they depend on self-reported symptoms and clinician interpretation, which vary across individuals. These methods also fail to account for neurobiological factors such as neurotransmitter imbalances and altered brain connectivity.

    Similarly, clinical AI/ML models used in healthcare often lack demographic diversity in their training data, with most studies failing to report race and gender, leading to biased outputs and reduced fairness. EEG offers a promising, objective approach to monitoring brain activity, potentially improving diagnostic accuracy and helping address biases in mental health assessment, as this study found.

    Learn more about it here: https://doi.org/10.3390/brainsci14101018

    Reference

    Yousufi, M., Damaševičius, R., & Maskeliūnas, R. (2024). Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121. Brain sciences, 14(10), 1018.

  • 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. 

  • 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.