Ethical Challenges in Financial Data Science: Addressing Algorithmic Bias and Institutional Pressures
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Abstract
As automated systems become increasingly embedded in financial institutions, data scientists face heightened ethical tensions surrounding algorithmic bias and institutional demands. This study investigates the moral complexities encountered by practitioners responsible for designing and managing algorithmic decision-making systems. Through an interpretative phenomenological approach, the research draws on in-depth interviews with ten data science professionals in the financial sector. Participant selection was based on criteria including experience in algorithmic decision-making and ethical awareness. Findings reveal recurring conflicts between ethical awareness and performance-oriented cultures, often forcing practitioners to compromise or suppress their moral judgments. Data analysis involved thematic coding to identify key ethical issues and coping strategies. Participants described challenges such as algorithmic opacity, lack of explainability, and organizational resistance to ethical concerns. To reconcile these dilemmas, practitioners developed informal coping strategies ranging from silent resistance and moral rationalization to small-scale interventions demonstrating an ongoing negotiation between personal integrity and institutional constraints. Moreover, there was a strong desire for structured ethical frameworks and collective accountability within teams and organizations. These findings highlight that ethical practice in data science is not only shaped by technical solutions or policy initiatives, but also by lived experiences and the sociocultural contexts of professional work. This research calls for institutional support structures that elevate ethical reflection as an integral component of data-driven environments. It also contributes to a more human-centered understanding of algorithmic ethics, emphasizing the need to prioritize practitioner voices in discussions about responsible AI implementation.
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References
Arun, S., Sykes, E. R., & Tanbeer, S. (2024). RemoteHealthConnect: Innovating patient monitoring with wearable technology and custom visualization. Digital Health, 10. Scopus. https://doi.org/10.1177/20552076241300748
Bruno, E., Biondi, A., Thorpe, S., & Richardson, M. P. (2020). Patients self-mastery of wearable devices for seizure detection: A direct user-experience. Seizure, 81, 236–240. Scopus. https://doi.org/10.1016/j.seizure.2020.08.023
Carnazzo, C., Spada, S., Lamacchia, S., Manuri, F., Sanna, A., & Cavatorta, M. P. (2024). Virtual reality in ergonomics by wearable devices: Experiences from the automotive sector. Journal of Workplace Learning, 36(7), 621–635. Scopus. https://doi.org/10.1108/JWL-03-2024-0064
Chen, M., Jiang, Y., Guizani, N., Zhou, J., Tao, G., Yin, J., & Hwang, K. (2020). Living with I-Fabric: Smart Living Powered by Intelligent Fabric and Deep Analytics. IEEE Network, 34(5), 156–163. Scopus. https://doi.org/10.1109/MNET.011.1900570
Choi, K. Y., Elhaouij, N., Lee, J., Picard, R. W., & Ishii, H. (2022). Design and Evaluation of a Clippable and Personalizable Pneumatic-haptic Feedback Device for Breathing Guidance. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(1). Scopus. https://doi.org/10.1145/3517234
Choi, S., Sajib, M. R. U. Z., Manzano, J., & Chlebek, C. J. (2023). mHealth Technology Experiences of Middle-Aged and Older Individuals With Visual Impairments: Cross-Sectional Interview Study. JMIR Formative Research, 7(1). Scopus. https://doi.org/10.2196/52410
Chong, A. Y. L., Blut, M., & Zheng, S. (2022). Factors influencing the acceptance of healthcare information technologies: A meta-analysis. Information and Management, 59(3). Scopus. https://doi.org/10.1016/j.im.2022.103604
Connelly, K., Molchan, H., Bidanta, R., Siddh, S., Lowens, B., Caine, K., Demiris, G., Siek, K., & Reeder, B. (2021). Evaluation framework for selecting wearable activity monitors for research. mHealth, 7. Scopus. https://doi.org/10.21037/mhealth-19-253
Cooper, D. M., Bhuskute, N., & Walsh, G. (2022). Exploring the Impact and Acceptance of Wearable Sensor Technology for Pre- and Postoperative Rehabilitation in Knee Replacement Patients: A U.K.-Based Pilot Study. JBJS Open Access, 7(2). Scopus. https://doi.org/10.2106/JBJS.OA.21.00154
Cristiano, A., Musteata, S., De Silvestri, S., Bellandi, V., Ceravolo, P., Cesari, M., Azzolino, D., Sanna, A., & Trojaniello, D. (2022). Older Adults’ and Clinicians’ Perspectives on a Smart Health Platform for the Aging Population: Design and Evaluation Study. JMIR Aging, 5(1). Scopus. https://doi.org/10.2196/29623
Damre, S. S., Shendkar, B. D., Kulkarni, N., Chandre, P. R., & Deshmukh, S. (2024). Smart Healthcare Wearable Device for Early Disease Detection Using Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(4s), 158–166. Scopus.
Daniels, K., Vonck, S., Robijns, J., Quadflieg, K., Bergs, J., Spooren, A., Hansen, D., & Bonnechère, B. (2025). Exploring the Feasibility of a 5-Week mHealth Intervention to Enhance Physical Activity and an Active, Healthy Lifestyle in Community-Dwelling Older Adults: Mixed Methods Study. JMIR Aging, 8. Scopus. https://doi.org/10.2196/63348
Dawson, J. K., Ede, A., Phan, M., Sequeira, A., Teng, H.-L., & Donlin, A. (2024). Feasibility and Acceptability of a Mobile Health Exercise Intervention for Inactive Adults: 3-Arm Randomized Controlled Pilot Trial. JMIR Formative Research, 8. Scopus. https://doi.org/10.2196/52428
Debard, G., De Witte, N., Sels, R., Mertens, M., Van Daele, T., & Bonroy, B. (2020). Making wearable technology available for mental healthcare through an online platform with stress detection algorithms: The CareWear project. Journal of Sensors, 2020. Scopus. https://doi.org/10.1155/2020/8846077
Delmastro, F., Dolciotti, C., La Rosa, D., Di Martino, F., Magrini, M., Coscetti, S., & Palumbo, F. (2019). Experimenting mobile and e-health services with frail MCI older people. Information (Switzerland), 10(8). Scopus. https://doi.org/10.3390/info10080253
Domingos, C., Costa, P., Santos, N. C., & Pêgo, J. M. (2022). Usability, Acceptability, and Satisfaction of a Wearable Activity Tracker in Older Adults: Observational Study in a Real-Life Context in Northern Portugal. Journal of Medical Internet Research, 24(1). Scopus. https://doi.org/10.2196/26652
Doty, J. L., Brady, S. S., Popelka, J. M., Rietveld, L., Garcia-Huidobro, D., Doty, M. J., Linares, R., Svetaz, M. V., & Allen, M. L. (2020). Designing a mobile app to enhance parenting skills of latinx parents: A community-based participatory approach. JMIR Formative Research, 4(1). Scopus. https://doi.org/10.2196/12618
Drummond, K., Lambert, G., Tahasildar, B., & Carli, F. (2022). Successes and challenges of implementing teleprehabilitation for onco-surgical candidates and patients’ experience: A retrospective pilot-cohort study. Scientific Reports, 12(1). Scopus. https://doi.org/10.1038/s41598-022-10810-y
Durrani, S., Cao, S., Bo, N., Pai, J. K., Baker, J., Rawlings, L., Qureshi, Z. P., Sigua, N. L., Manchanda, S., & Khan, B. (2022). A Feasibility Study: Testing Whether a Sleep Application Providing Objective Sleep Data to Physicians Improves Patient–Physician Communication Regarding Sleep Experiences, Habits, and Behaviors. Advances in Therapy, 39(4), 1612–1629. Scopus. https://doi.org/10.1007/s12325-021-02013-0
Egan, S., Brama, P., & McGrath, D. (2019). Irish equine industry stakeholder perspectives of objective technology for biomechanical analyses in the field. Animals, 9(8). Scopus. https://doi.org/10.3390/ani9080539
Erdeniz, S. P., Menychtas, A., Maglogiannis, I., Felfernig, A., & Tran, T. N. T. (2020). Recommender systems for IoT enabled quantified-self applications. Evolving Systems, 11(2), 291–304. Scopus. https://doi.org/10.1007/s12530-019-09302-8
Ferreira, J., França, M., Rei, M., Peixoto, R., Armand Larsen, S., Bernini, A., Lopes, L., Conde, C., & Claro, J. (2024). Towards user-centered design of medical devices for SUDEP prediction and prevention: Insights from persons with epilepsy and caregivers. Epilepsy and Behavior, 161. Scopus. https://doi.org/10.1016/j.yebeh.2024.110034
Hirczy, S., Zabetian, C., & Lin, Y.-H. (2024). The current state of wearable device use in Parkinson’s disease: A survey of individuals with Parkinson’s. Frontiers in Digital Health, 6. Scopus. https://doi.org/10.3389/fdgth.2024.1472691
Hoa Nguyen, H. T., Bui, L. K., Tran, T. N., Thuy Nguyen, N. T., Phuong, A. H., Pham, H. H., Taylor-Robinson, A. W., Duc, T. Q., & Thanh Nguyen, H. T. (2024). The i-CanManage program to improve exercise and symptom management for Vietnamese women after cancer: A pilot randomized controlled trial protocol. Digital Health, 10. Scopus. https://doi.org/10.1177/20552076241293974
Hough, R., Dunn, J. L., & Hepburn, L.-A. (2024). Re-imagining the future state of the ventricular assist device controller interface through human-centered design. Artificial Organs, 48(11), 1313–1345. Scopus. https://doi.org/10.1111/aor.14817
Katib, I., Albassam, E., Sharaf, S. A., & Ragab, M. (2025). Safeguarding IoT consumer devices: Deep learning with TinyML driven real-time anomaly detection for predictive maintenance. Ain Shams Engineering Journal, 16(2). Scopus. https://doi.org/10.1016/j.asej.2025.103281
Kononova, A., Li, L., Kamp, K., Bowen, M., Rikard, R. V., Cotten, S., & Peng, W. (2019). The use of wearable activity trackers among older adults: Focus group study of tracker perceptions, motivators, and barriers in the maintenance stage of behavior change. JMIR mHealth and uHealth, 7(4). Scopus. https://doi.org/10.2196/mhealth.9832
Kytö, M., Koivusalo, S., Tuomonen, H., Strömberg, L., Ruonala, A., Marttinen, P., Heinonen, S., & Jacucci, G. (2023). Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors. JMIR Diabetes, 8(1). Scopus. https://doi.org/10.2196/43979
Luo, H., Yang, G., Jin, Z., Cai, Z., Li, Y., Lu, Y., Wang, J., Yang, H., Zheng, Y., & Xu, K. (2025). Textile hybrid electronics for monolithically multimodal wearable monitoring and therapy. International Journal of Extreme Manufacturing, 7(3). Scopus. https://doi.org/10.1088/2631-7990/adb5dd
Mohammadi, M., Assaf, G., & Assaad, R. H. (2024). Real-time spatial-temporal mapping and visualization of thermal comfort and HVAC control by integrating immersive augmented reality technologies and IoT-enabled wireless sensor networks: Towards immersive human-building interactions. Journal of Building Engineering, 94. Scopus. https://doi.org/10.1016/j.jobe.2024.109887
Nissen, M., Perez, C. A., Jaeger, K. M., Bleher, H., Flaucher, M., Huebner, H., Danzberger, N., Titzmann, A., Pontones, C. A., Fasching, P. A., Beckmann, M. W., Eskofier, B. M., & Leutheuser, H. (2023). Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study. JMIR Pediatrics and Parenting, 6(1). Scopus. https://doi.org/10.2196/50765
Pizzo, A. D., Baker, B. J., Jones, G. J., & Funk, D. C. (2021). Sport experience design: Wearable fitness technology in the health and fitness industry. Journal of Sport Management, 35(2), 130–143. Scopus. https://doi.org/10.1123/JSM.2020-0150
Rony, R. J., Amir, S., Ahmed, N., Atiba, S., Verdezoto, N., Sparkes, V., & Stawarz, K. (2024). Understanding the Sociocultural Challenges and Opportunities for Affordable Wearables to Support Poststroke Upper-Limb Rehabilitation: Qualitative Study. JMIR Rehabilitation and Assistive Technologies, 11. Scopus. https://doi.org/10.2196/54699
Szczepura, A., Holliday, N., Neville, C., Johnson, K., Khan Khan, A. J., Oxford, S. W., & Nduka, C. (2020). Raising the digital profile of facial palsy: National surveys of patients’ and clinicians’ experiences of changing UK treatment pathways and views on the future role of digital technology. Journal of Medical Internet Research, 22(10). Scopus. https://doi.org/10.2196/20406
van den Bergh, R., Evers, L. J. W., de Vries, N. M., Silva de Lima, A. L., Bloem, B. R., Valenti, G., & Meinders, M. J. (2023). Usability and utility of a remote monitoring system to support physiotherapy for people with Parkinson’s disease. Frontiers in Neurology, 14. Scopus. https://doi.org/10.3389/fneur.2023.1251395
Varma, P., Narayan Senapati, J., Sharma, V., & Singh Choudhary, A. (2022). Technological Advancements in Personal Protective Equipment: A Future Perspective. Health Leadership and Quality of Life, 1. Scopus. https://doi.org/10.56294/hl2022136
Wróbel-Lachowska, M., Dominiak, J., Woźniak, M. P., Bartłomiejczyk, N., Diethei, D., Wysokińska, A., Niess, J., Grudzień, K., Woźniak, P. W., & Romanowski, A. (2023). ‘That’s when I put it on’: Stakeholder perspectives in large-scale remote health monitoring for older adults. Personal and Ubiquitous Computing, 27(6), 2193–2210. Scopus. https://doi.org/10.1007/s00779-023-01753-w
Xiangfang, R., Lei, S., Miaomiao, L., Xiying, Z., & Han, C. (2021). Research and sustainable design of wearable sensor for clothing based on body area network. Cognitive Computation and Systems, 3(3), 206–220. Scopus. https://doi.org/10.1049/ccs2.12014
Yen, H.-Y., Liao, Y., & Huang, H.-Y. (2022). Smart Wearable Device Users’ Behavior Is Essential for Physical Activity Improvement. International Journal of Behavioral Medicine, 29(3), 278–285. Scopus. https://doi.org/10.1007/s12529-021-10013-1
Zhou, C., Yuan, F., Huang, T., Zhang, Y., & Kaner, J. (2022). The Impact of Interface Design Element Features on Task Performance in Older Adults: Evidence from Eye-Tracking and EEG Signals. International Journal of Environmental Research and Public Health, 19(15). Scopus. https://doi.org/10.3390/ijerph19159251