Bias in artificial intelligence (AI), machine learning (ML), and deep learning (DL) models presents a critical challenge to achieving fairness and trustworthiness in high-stakes fields like healthcare, finance, and criminal justice. Documented instances include facial recognition systems failing significantly more often on darker-skinned women and healthcare algorithms systematically underestimating the care needs of Black patients due to flawed data proxies. This study offers a comprehensive review of bias in AI, analyzing its sources, detection methods, and bias mitigation strategies. The authors systematically trace how bias propagates throughout the entire AI lifecycle, from initial data collection to final model deployment. The review then evaluates state-of-the-art mitigation techniques, such as pre-processing (e.g. data re-sampling), in-processing (e.g. adversarial debiasing), and post-processing methods. A recurring theme identified is the fairness-accuracy trade-off, where efforts to improve group fairness by 10–15% often result in a modest 2–5% reduction in overall model accuracy. Through case studies in hiring, healthcare, and predictive policing, this work illustrates the real-world applicability, strengths, and limitations of these approaches. Furthermore, this research examines the evolving legal and ethical landscape, including frameworks like the EU AI Act and the “Right to Explanation” under GDPR, underscoring the necessity for regulatory compliance and interdisciplinary collaboration. Key challenges such as scalable fairness auditing and context-dependent fairness definitions are highlighted. Finally, this study discusses emerging trends like bias-aware federated learning and explainable AI as promising future research directions, providing a roadmap for developing more transparent, inclusive, and equitable AI systems.
Bias in Artificial Intelligence (AI) has significant implications across several facets of our lives. More specifically, the algorithms developed to aid AI learn and the data model upon which that algorithm depends require to be evaluated, iteratively, to avoid bias. Depending on the application, the result produced from AI could have significant ramifications. This article explores how machine learning algorithms function, reviews some common biases, and shares ways to identify, reduce, and eliminate bias in machine learning.
This study explored a comprehensive review of bias in AI, ML, and DL models, including methods, impacts, and future directions and has not utilized any datasets.
Artificial Intelligence (AI) is the theory and development of computer systems capable of performing tasks that historically required human intelligence, such as recognizing speech, making decisions, and identifying patterns. AI encompasses a wide variety of technologies, including machine learning, deep learning, and natural language processing (NLP). [1]
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“Algorithms are harnessing volumes of macro- and micro-data to influence decisions affecting users in a range of tasks, from making movie recommendations to helping banks determine the creditworthiness of individuals.”
