ArticleAI and ethics2022
AI bias: exploring discriminatory algorithmic decision-making models and the application of possible machine-centric solutions adapted from the pharmaceutical industry.
Article in AI and ethics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 229 citations in OpenAlex.
- 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 : JAMIA · 2024Pooled it
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- Multi-Modal, Machine Learning-Driven Framework Integrating Multi-Omics for Personalized Chronic Kidney Disease Management.Journal of clinical medicine · 2026Review
- Evaluation of the Accuracy of Probabilistic Record Linkage Across Sociodemographic Categories in 4 Databases: Exploratory Study.JMIR formative research · 2026Article
- Wearable Biosensing and Machine Learning for Data-Driven Training and Coaching Support.Biosensors · 2026Review
- Problematizing the role of artificial intelligence in hiring and organizational inequalities: A multidisciplinary review.Human relations; studies towards the integration of the social sciences · 2026Article
- Ethical oversight of AI-driven paediatric trials: a proactive, risk-sensitive interim review model.Frontiers in digital health · 2026Article
- Reassessing demographic bias in face attribute classification: a statistically grounded multi-model evaluation on FairFace and UTKFace.Frontiers in artificial intelligence · 2026Article
- The digital transformation of clinical teaching: a review of AI and data-driven quality assurance systems in medical education.Frontiers in public health · 2026Review
- The paradox of efficiency: institutional interfaces, residuals, and the erosion of innovation drivers in AI-augmented organizations.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence and the transformation of education: a systematic narrative review towards adaptive epistemic ecosystems and post-linear pedagogy.Frontiers in artificial intelligence · 2026Review
- Generative artificial intelligence in scientific publishing: Expectations for authors and reviewers atPreventive medicine reports · 2026Article
- CarD-T: an automated pipeline for the nomination and analysis of potential human carcinogens.Carcinogenesis · 2025Article
- A Systematic Fairness Evaluation of Racial Bias in Alzheimer's Disease Diagnosis Using Machine Learning Models.bioRxiv : the preprint server for biology · 2025Article
- Data-Driven Strategies in Orthopaedics: Optimizing Surgical Precision and Patient Outcomes.Indian journal of orthopaedics · 2025Review
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review.PLOS digital health · 2025Article
- Clinical and Operational Applications of Artificial Intelligence and Machine Learning in Pharmacy: A Narrative Review of Real-World Applications.Pharmacy (Basel, Switzerland) · 2025Article
- Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review.Journal of medical Internet research · 2024Article
- Artificial intelligence bias in the prediction and detection of cardiovascular disease.NPJ cardiovascular health · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A new and unorthodox approach to deal with discriminatory bias in Artificial Intelligence is needed. As it is explored in detail, the current literature is a dichotomy with studies originating from the contrasting fields of study of either philosophy and sociology or data science and programming. It is suggested that there is a need instead for an integration of both academic approaches, and needs to be machine-centric rather than human-centric applied with a deep understanding of societal and individual prejudices. This article is a novel approach developed into a framework of action: a bias impact assessment to raise awareness of bias and why, a clear set of methodologies as shown in a table comparing with the four stages of pharmaceutical trials, and a summary flowchart. Finally, this study concludes the need for a transnational independent body with enough power to guarantee the implementation of those solutions.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.