Published on in Vol 10, No 5 (2022): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36388, first published .
Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review

Journals

  1. Landau A, Blanchard A, Atkins N, Salazar S, Cato K, Patton D, Topaz M. Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning–Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers. JMIR Formative Research 2023;7:e40194 View
  2. Al-Ani M, Bai C, Hashky A, Parker A, Vilaro J, Aranda Jr. J, Shickel B, Rashidi P, Bihorac A, Ahmed M, Mardini M. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review. Frontiers in Cardiovascular Medicine 2023;10 View
  3. Barton M, Hamza M, Guevel B. Racial Equity in Healthcare Machine Learning: Illustrating Bias in Models With Minimal Bias Mitigation. Cureus 2023 View
  4. Kim E, Jenness J, Miller A, Halabi R, de Zambotti M, Bagot K, Baker F, Pratap A. Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children. JAMA Network Open 2023;6(3):e235681 View
  5. Sahiner B, Chen W, Samala R, Petrick N. Data drift in medical machine learning: implications and potential remedies. The British Journal of Radiology 2023;96(1150) View
  6. Le J, Shashikumar S, Malhotra A, Nemati S, Wardi G. Making the Improbable Possible: Generalizing Models Designed for a Syndrome-Based, Heterogeneous Patient Landscape. Critical Care Clinics 2023;39(4):751 View
  7. Bays H, Fitch A, Cuda S, Gonsahn-Bollie S, Rickey E, Hablutzel J, Coy R, Censani M. Artificial intelligence and obesity management: An Obesity Medicine Association (OMA) Clinical Practice Statement (CPS) 2023. Obesity Pillars 2023;6:100065 View
  8. Khor S, Haupt E, Hahn E, Lyons L, Shankaran V, Bansal A. Racial and Ethnic Bias in Risk Prediction Models for Colorectal Cancer Recurrence When Race and Ethnicity Are Omitted as Predictors. JAMA Network Open 2023;6(6):e2318495 View
  9. Rana S, Azizul Z, Awan A. A step toward building a unified framework for managing AI bias. PeerJ Computer Science 2023;9:e1630 View
  10. Schuch H, Furtado M, Silva G, Kawachi I, Chiavegatto Filho A, Elani H. Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults. JAMA Network Open 2023;6(11):e2341625 View
  11. Hooper S, Hecker K, Artemiou E. Using Machine Learning in Veterinary Medical Education: An Introduction for Veterinary Medicine Educators. Veterinary Sciences 2023;10(9):537 View
  12. Allareddy V, Oubaidin M, Rampa S, Venugopalan S, Elnagar M, Yadav S, Lee M. Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health. Orthodontics & Craniofacial Research 2023;26(S1):124 View
  13. Balucan F, French B, Shi Y, Kripalani S, Vasilevskis E. Screening for the high-need population using single institution versus state-wide admissions discharge transfer feed. BMC Health Services Research 2023;23(1) View
  14. Gonzalez R, Saha A, Campbell C, Nejat P, Lokker C, Norgan A. Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. Journal of Pathology Informatics 2024;15:100347 View
  15. Kumar R, Sood P, Nirala R, Ade R, Sonaji A. Uses of AI in Field of Radiology- What is State of Doctor & Pateints Communication in Different Disease for Diagnosis Purpose. Journal for Research in Applied Sciences and Biotechnology 2023;2(5):51 View
  16. Banda J, Shah N, Periyakoil V. Characterizing subgroup performance of probabilistic phenotype algorithms within older adults: a case study for dementia, mild cognitive impairment, and Alzheimer’s and Parkinson’s diseases. JAMIA Open 2023;6(2) View
  17. Cary M, Zink A, Wei S, Olson A, Yan M, Senior R, Bessias S, Gadhoumi K, Jean-Pierre G, Wang D, Ledbetter L, Economou-Zavlanos N, Obermeyer Z, Pencina M. Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review. Health Affairs 2023;42(10):1359 View
  18. Risser L, Picard A, Hervier L, Loubes J. Detecting and Processing Unsuspected Sensitive Variables for Robust Machine Learning. Algorithms 2023;16(11):510 View
  19. Smith C, Weathers A, Lewis S. An overview of clinical machine learning applications in neurology. Journal of the Neurological Sciences 2023;455:122799 View
  20. El-Azab S, Nong P. Clinical algorithms, racism, and “fairness” in healthcare: A case of bounded justice. Big Data & Society 2023;10(2) View
  21. Ferrara E. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. SSRN Electronic Journal 2023 View
  22. Tudorie G. Reluctant Republic: A Positive Right for Older People to Refuse AI-Based Technology. Societies 2023;13(12):248 View
  23. Kerr W, McFarlane K. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Current Neurology and Neuroscience Reports 2023;23(12):869 View
  24. Koo C, Yang A, Welch C, Jadav V, Posch L, Thoreson N, Morris D, Chouhdry F, Szabo J, Mendelson D, Margolies L. Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation. Journal of Medical Imaging 2023;10(06) View
  25. Ferrara E. Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies. Sci 2023;6(1):3 View
  26. Dell’Aquila K, Vadlamani A, Maldjian T, Fineberg S, Eligulashvili A, Chung J, Adam R, Hodges L, Hou W, Makower D, Duong T. Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population. Breast Cancer Research 2024;26(1) View
  27. Ożegalska-Łukasik N, Łukasik S. Culturally Responsive Artificial Intelligence – Problems, Challenges and Solutions. Intercultural Relations 2023;7(2(14)):106 View
  28. Patel S, Baum A, Basu S. Prediction of non emergent acute care utilization and cost among patients receiving Medicaid. Scientific Reports 2024;14(1) View
  29. Davenport M, Sirrianni J, Chisolm D. Machine learning data sources in pediatric sleep research: assessing racial/ethnic differences in electronic health record–based clinical notes prior to model training. Frontiers in Sleep 2024;3 View
  30. Yao S, Dai F, Sun P, Zhang W, Qian B, Lu H. Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population. Nature Communications 2024;15(1) View
  31. Yang P, Gregory I, Robichaux C, Holder A, Martin G, Esper A, Kamaleswaran R, Gichoya J, Bhavani S. Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19. Critical Care Explorations 2024;6(3):e1059 View
  32. Chen F, Wang L, Hong J, Jiang J, Zhou L. Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models. Journal of the American Medical Informatics Association 2024;31(5):1172 View
  33. Liu Y, Joly R, Reading Turchioe M, Benda N, Hermann A, Beecy A, Pathak J, Zhang Y. Preparing for the bedside—optimizing a postpartum depression risk prediction model for clinical implementation in a health system. Journal of the American Medical Informatics Association 2024;31(6):1258 View
  34. Saldana C, Burkhardt E, Pennisi A, Oliver K, Olmstead J, Holland D, Gettings J, Mauck D, Austin D, Wortley P, Ochoa K. Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States. Clinical Infectious Diseases 2024;79(3):717 View
  35. Didier A, Nigro A, Noori Z, Omballi M, Pappada S, Hamouda D. Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis. Frontiers in Artificial Intelligence 2024;7 View
  36. Norris M, Obeid N, El‐Emam K. Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders. International Journal of Eating Disorders 2024;57(6):1357 View
  37. Tejani A, Ng Y, Xi Y, Rayan J. Understanding and Mitigating Bias in Imaging Artificial Intelligence. RadioGraphics 2024;44(5) View
  38. Kim J, Hasan A, Kellogg K, Ratliff W, Murray S, Suresh H, Valladares A, Shaw K, Tobey D, Vidal D, Lifson M, Patel M, Raji I, Gao M, Knechtle W, Tang L, Balu S, Sendak M, Guillot G. Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities. PLOS Digital Health 2024;3(5):e0000390 View
  39. Rusinovich Y, Rusinovich V. Confounders in Predictive Medical Models: Roles of Nationality and Immigrant Status. Web3 Journal: ML in Health Science 2024;1(1):d070224 View
  40. Wang Y, Wang L, Zhou Z, Laurentiev J, Lakin J, Zhou L, Hong P. Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases. Journal of Biomedical Informatics 2024;156:104677 View
  41. Noam K, Schmutte T, Bory C, Plant R. Mitigating Racial Bias in Health Care Algorithms: Improving Fairness in Access to Supportive Housing. Psychiatric Services 2024;75(11):1167 View
  42. Baker C, Pease M, Sexton D, Abumoussa A, Chambless L. Artificial intelligence innovations in neurosurgical oncology: a narrative review. Journal of Neuro-Oncology 2024;169(3):489 View
  43. Ganta T, Kia A, Parchure P, Wang M, Besculides M, Mazumdar M, Smith C. Fairness in Predicting Cancer Mortality Across Racial Subgroups. JAMA Network Open 2024;7(7):e2421290 View
  44. Mathis W, Zhao S, Pratt N, Weleff J, De Paoli S. Inductive thematic analysis of healthcare qualitative interviews using open-source large language models: How does it compare to traditional methods?. Computer Methods and Programs in Biomedicine 2024;255:108356 View
  45. Jiang Z, Seyedi S, Griner E, Abbasi A, Rad A, Kwon H, Cotes R, Clifford G, McGinnis R. Evaluating and mitigating unfairness in multimodal remote mental health assessments. PLOS Digital Health 2024;3(7):e0000413 View
  46. Krauss D, Engel L, Ott T, Bräunig J, Richer R, Gambietz M, Albrecht N, Hille E, Ullmann I, Braun M, Dabrock P, Kölpin A, Koelewijn A, Eskofier B, Vossiek M. A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring. IEEE Open Journal of Engineering in Medicine and Biology 2024;5:680 View
  47. Patino G, Roberts L. The Need for Greater Transparency in Journal Submissions That Report Novel Machine Learning Models in Health Professions Education. Academic Medicine 2024;99(9):935 View
  48. Cordella C, Marte M, Liu H, Kiran S. An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications. Perspectives of the ASHA Special Interest Groups 2025;10(2):432 View
  49. Khalil A, Bellesia G, Norton M, Jacobsson B, Haeri S, Egbert M, Malone F, Wapner R, Roman A, Faro R, Madankumar R, Strong N, Silver R, Vohra N, Hyett J, MacPherson C, Prigmore B, Ahmed E, Demko Z, Ortiz J, Souter V, Dar P. The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: an artificial intelligence model. American Journal of Obstetrics and Gynecology 2024;231(5):554.e1 View
  50. Huang Y, Guo J, Donahoo W, Lee Y, Fan Z, Lu Y, Chen W, Tang H, Bilello L, Saguil A, Rosenberg E, Shenkman E, Bian J. A fair individualized polysocial risk score for identifying increased social risk in type 2 diabetes. Nature Communications 2024;15(1) View
  51. McCoy L, Ci Ng F, Sauer C, Yap Legaspi K, Jain B, Gallifant J, McClurkin M, Hammond A, Goode D, Gichoya J, Celi L. Understanding and training for the impact of large language models and artificial intelligence in healthcare practice: a narrative review. BMC Medical Education 2024;24(1) View
  52. Carbunaru S, Neshatvar Y, Do H, Murray K, Ranganath R, Nayan M. Survival After Radical Cystectomy for Bladder Cancer: Development of a Fair Machine Learning Model. JMIR Medical Informatics 2024;12:e63289 View
  53. Park K, Saleem M, Al-Garadi M, Ahmed A. Machine learning applications in studying mental health among immigrants and racial and ethnic minorities: an exploratory scoping review. BMC Medical Informatics and Decision Making 2024;24(1) View
  54. Lucas M, Schootman M, Laryea J, Orcutt S, Li C, Ying J, Rumpel J, Yang C. Bias in Prediction Models to Identify Patients With Colorectal Cancer at High Risk for Readmission After Resection. JCO Clinical Cancer Informatics 2024;(8) View
  55. Ladin K, Cuddeback J, Duru O, Goel S, Harvey W, Park J, Paulus J, Sackey J, Sharp R, Steyerberg E, Ustun B, van Klaveren D, Weingart S, Kent D. Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor. npj Digital Medicine 2024;7(1) View
  56. Sarullo K, Swamidass S. Understanding and mitigating the impact of race with adversarial autoencoders. Communications Medicine 2024;4(1) View
  57. Grant J, Javaid A, Carrick R, Koester M, Kassamali A, Kim C, Isakadze N, Wu K, Blaha M, Whelton S, Arbab-Zadeh A, Orringer C, Blumenthal R, Martin S, Marvel F. Digital health innovation and artificial intelligence in cardiovascular care: a case-based review. npj Cardiovascular Health 2024;1(1) View
  58. Dudek N, Chakhvadze M, Kobakhidze S, Kantidze O, Gankin Y. Supervised machine learning for microbiomics: Bridging the gap between current and best practices. Machine Learning with Applications 2024;18:100607 View
  59. Mazurenko O, Hirsh A, Harle C, Shen J, McNamee C, Vest J, He Z. Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department. PLOS ONE 2024;19(11):e0312193 View
  60. Lee G, Goodman D, Chang T. Impact of Demographic Modifiers on Readability of Myopia Education Materials Generated by Large Language Models. Clinical Ophthalmology 2024;Volume 18:3591 View
  61. Murray B, Thota D, Baker C, Stierwalt J. Key Insights for the Ethical and Appropriate Use of Artificial Intelligence by Medical Learners. Military Medicine 2025;190(7-8):e1381 View
  62. Colacci M, Huang Y, Postill G, Zhelnov P, Fennelly O, Verma A, Straus S, Tricco A. Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms. Journal of Clinical Epidemiology 2025;178:111606 View
  63. Wagh V, Scott M, Kraeutner S. Quantifying Similarities Between MediaPipe and a Known Standard to Address Issues in Tracking 2D Upper Limb Trajectories: Proof of Concept Study. JMIR Formative Research 2024;8:e56682 View
  64. Topaz M, Davoudi A, Evans L, Sridharan S, Song J, Chae S, Barrón Y, Hobensack M, Scharp D, Cato K, Rossetti S, Kapela P, Xu Z, Gupta P, Zhang Z, Mcdonald M, Bowles K. Building a Time-Series Model to Predict Hospitalization Risks in Home Health Care: Insights Into Development, Accuracy, and Fairness. Journal of the American Medical Directors Association 2025;26(2):105417 View
  65. Van Eyghen H. AI Algorithms as (un)virtuous knowers. Discover Artificial Intelligence 2025;5(1) View
  66. Lammert J, Roberts A, McRae K, Batterink L, Butler B. Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches. Journal of Speech, Language, and Hearing Research 2025;68(2):705 View
  67. Yue M, Jong M, Dai Y, Lau W. Students as AI literate designers: a pedagogical framework for learning and teaching AI literacy in elementary education. Journal of Research on Technology in Education 2025:1 View
  68. Daus Z. Distribution, Recognition, and Just Medical AI. Philosophy & Technology 2025;38(1) View
  69. Karches K. Hermeneutics as impediment to AI in medicine. Theoretical Medicine and Bioethics 2025;46(1):31 View
  70. Wei S, Guo X, He S, Zhang C, Chen Z, Chen J, Huang Y, Zhang F, Liu Q. Application of Machine Learning for Patients With Cardiac Arrest: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2025;27:e67871 View
  71. Dang V, Campello V, Hernández-González J, Lekadir K. Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare. Journal of Healthcare Informatics Research 2025;9(3):465 View
  72. Heffernan A, Ganguli R, Sears I, Stephen A, Heffernan D. Choice of Machine Learning Models Is Important to Predict Post-Operative Infections in Surgical Patients. Surgical Infections 2025;26(7):520 View
  73. Amirian S, Gao F, Littlefield N, Hill J, Yates A, Plate J, Pantanowitz L, Rashidi H, Tafti A. State-of-the-Art in Responsible, Explainable, and Fair AI for Medical Image Analysis. IEEE Access 2025;13:58229 View
  74. Vargas-Santiago M, León-Velasco D, Maldonado-Sifuentes C, Chanona-Hernandez L. A State-of-the-Art Review of Artificial Intelligence (AI) Applications in Healthcare: Advances in Diabetes, Cancer, Epidemiology, and Mortality Prediction. Computers 2025;14(4):143 View
  75. Hochkamp F, Scheidler A, Rabe M. Review of Maturity Models for Data Mining and Proposal of a Data Preparation Maturity Model Prototype for Data Mining. Computers 2025;14(4):146 View
  76. Ma D, Fan C, Sano T, Kawabata K, Nishikubo H, Imanishi D, Sakuma T, Maruo K, Yamamoto Y, Matsuoka T, Yashiro M. Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer. Journal of Personalized Medicine 2025;15(5):166 View
  77. Xue L, Yin R, Cole E, Lo‐Ciganic W, Gellad W, Donohue J, Tang L. Development and evaluation of a machine learning model to predict acute care for opioid use disorder among Medicaid enrollees engaged in a community‐based treatment program. Addiction 2025;120(9):1780 View
  78. Krishnadas R. Ethnic Bias in Prediction and Decision Making Algorithms in Precision Psychiatry: Challenges in a Shrinking World. Journal of Psychosocial Rehabilitation and Mental Health 2025;12(2):191 View
  79. Hobensack M, Davoudi A, Song J, Cato K, Bowles K, Topaz M. Comparing the influence of social risk factors on machine learning model performance across racial and ethnic groups in home healthcare. Nursing Outlook 2025;73(3):102431 View
  80. Chappidi S, Belue M, Harmon S, Jagasia S, Zhuge Y, Tasci E, Turkbey B, Singh J, Camphausen K, Krauze A, Grosan C. From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities. PLOS Digital Health 2025;4(5):e0000755 View
  81. Li B, Jiang X, Zhang K, Harmanci A, Malin B, Gao H, Shi X, Kuo P. Enhancing fairness in disease prediction by optimizing multiple domain adversarial networks. PLOS Digital Health 2025;4(5):e0000830 View
  82. Deshpande R, Mlombwa D, Celi L, Gallifant J, D’couto H. Race Against the Machine Learning Courses. ACM Transactions on Intelligent Systems and Technology 2025 View
  83. Mackin S, Major V, Chunara R, Newton-Dame R. Identifying and mitigating algorithmic bias in the safety net. npj Digital Medicine 2025;8(1) View
  84. Edwards C, Erstad B, Ng V. The role of artificial intelligence in emergency medicine pharmacy practice. American Journal of Health-System Pharmacy 2025 View
  85. Yang Y, Liao C, Keyvanshokooh E, Shao H, Weber M, Pasquel F, Garcia G. A Responsible Framework for Assessing, Selecting, and Explaining Machine Learning Models in Cardiovascular Disease Outcomes Among People With Type 2 Diabetes: Methodology and Validation Study. JMIR Medical Informatics 2025;13:e66200 View
  86. Abber S, Billman Miller M, Hamilton A, Ortiz S, Jacobucci R, Essayli J, Joiner T, Smith A, Forrest L. Bulimia nervosa severity levels based on shape/weight overvaluation explain more variance in clinical characteristics than DSM-5 severity levels. Psychological Medicine 2025;55 View
  87. Shaaban S, Ji Y. Health Disparities and Precision Medicine. Advances in Molecular Pathology 2025 View
  88. Jackson L, Delaney K, Bobo J, Grant C, Hassett L, Wang L, Weinshilboum R, Croarkin P, Moyer A, Gentry M, Athreya A. Quantifying Sample Representation in Global Pharmacogenomic Studies of Major Depressive Disorder: A Systematic Review. Clinical and Translational Science 2025;18(7) View
  89. Patel S, Barnett M, Basu S. Predicting quality measure completion among 14 million low-income patients enrolled in medicaid. npj Digital Medicine 2025;8(1) View
  90. Horsfall L, Bondaronek P, Ive J, Poduval S. Clinical Algorithms and the Legacy of Race-Based Correction: Historical Errors, Contemporary Revisions and Equity-Oriented Methodologies for Epidemiologists. Clinical Epidemiology 2025;Volume 17:647 View
  91. Li Y, Yao L, Lee Y, Huang Y, Merkel P, Vina E, Yeh Y, Li Y, Allen J, Bian J, Guo J. A fair machine learning model to predict flares of systemic lupus erythematosus. JAMIA Open 2025;8(4) View
  92. Were M, Li A, Malin B, Yin Z, Coco J, Collins B, Clayton E, Novak L, Hendricks-Sturrup R, Oluyomi A, Anders S, Yan C. Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health. Journal of Medical Internet Research 2025;27:e73996 View
  93. Mackin S, Major V, Chunara R, Newton-Dame R. Post-processing methods for mitigating algorithmic bias in healthcare classification models: An extended umbrella review. BMC Digital Health 2025;3(1) View
  94. Calahorrano Latorre E. La inclusión de la diversidad en los sistemas de inteligencia artificial de soporte a decisiones sanitarias como manifestación del enfoque de derechos humanos. Justicia &Derecho 2025:1 View
  95. Mahamadou A, Trotsyuk A. Revisiting Technical Bias Mitigation Strategies. Annual Review of Biomedical Data Science 2025;8(1):287 View
  96. Bai W, Ye X, Xie Y, Van Zandt S, Huang X, Sengupta D. Reducing AI Model Biases with a Bilevel Learning Framework: A Case Study of Leveraging Twitter Data for Damage Estimation. Annals of the American Association of Geographers 2025:1 View
  97. Collins C, Fackler J, Sacco M, Jacobs M. Critical conversations: a user-centric approach to chatbots for history taking in the pediatric intensive care unit. Frontiers in Pediatrics 2025;13 View
  98. Vest J, Wu W, Gregory M, Kasturi S, Mendonca E, Bian J, Magoc T, Grannis S, McNamee C, Harle C. Performance of 4 Methods to Assess Health-Related Social Needs. JAMA Network Open 2025;8(8):e2527426 View
  99. Morelli V. Foundations of Artificial Intelligence. Primary Care: Clinics in Office Practice 2025 View
  100. Chinnachamy T. Biasbarrier a Fairness and Equity Filter for LLM Responses Under Algorithmic Accountability Acts. International Journal of Scientific Research and Modern Technology 2025:83 View

Books/Policy Documents

  1. Montasari R. Cyberspace, Cyberterrorism and the International Security in the Fourth Industrial Revolution. View
  2. Swathi N, Chakrabarti M, Muzzamil M, Hamdar H, Jaber A, Chamoun A, Al Amin F, Rathod P. Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry. View
  3. Kansal M, Sibal R, Ram P. Electronic Governance with Emerging Technologies. View
  4. Ferrara E. Intelligent Decision Technologies. View
  5. Jones R. Ensuring Secure and Ethical STM Research in the AI Era. View

Conference Proceedings

  1. Shih J, Mohanty V, Katsis Y, Subramonyam H. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. Leveraging Large Language Models to Enhance Domain Expert Inclusion in Data Science Workflows View
  2. Garg S, Huo Z, Sim K, Schwartz S, Chua M, Aksënova A, Munkhdalai T, King L, Wright D, Mengesha Z, Hwang D, Sainath T, Beaufays F, Mengibar P. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Improving Speech Recognition for African American English with Audio Classification View
  3. Amarasinghe P, Pham D, Tran B, Nguyen S, Sun Y, Alahakoon D. Proceedings of the Genetic and Evolutionary Computation Conference. Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streams View
  4. Pfeifer R, Vhaduri S, Wilson M, Keller J. 2024 IEEE 20th International Conference on Body Sensor Networks (BSN). Toward Mitigating Sex Bias in Pilot Trainees' Stress and Fatigue Modeling View
  5. Dehghani F, Malik N, Lin J, Bayat S, Bento M. 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM). Fairness in Healthcare: Assessing Data Bias and Algorithmic Fairness View
  6. Ekoniak J, Asadinia M. 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC). HEALTH-ML: A Machine Learning Framework for Equity-Driven Public Health Outcome Prediction View