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March 21, 2026Open Access

Algorithmic Bias in AI Systems: Ethical Risks and Fairness Solutions

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Authors

SSSayyed Insha SufiMMMisbah Momin

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Overview

The study examines algorithmic bias in AI systems, indicating the need for ethical oversight and fairness solutions.

Key Points

  • This research aims to explore algorithmic bias in AI systems and its ethical implications, emphasizing the necessity for fairness.
  • Conducted a qualitative review of interdisciplinary literature
  • Analyzed case studies involving hiring tools, facial recognition, and credit scoring
  • Evaluated current fairness techniques and metrics
  • Identified biased data and design choices as primary causes of unfair outcomes
  • Revealed limitations of existing fairness techniques and transparency measures
  • Recommended stronger governance and shared responsibility for fair AI development

Cite This Study

Sufi et al. (2026) studied this question.

synapsesocial.com/papers/69be38906e48c4981c67905ahttps://doi.org/10.5281/zenodo.18218025
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Ensuring Fairness in Artificial Intelligence: A Study on Algorithmic Bias2026
  2. 2Algorithmic Bias in Artificial Intelligence: Strategies for Fairness and Ethical Decision-Making2026
  3. 3Fairness in Artificial Intelligence: Understanding and Mitigating Algorithmic Bias2026
  4. 4ALGORITHMIC BIAS AND SOCIAL INEQUALITY IN AI DECISION-MAKING SYSTEMS FROM A SOCIOLOGICAL PERSPECTIVE2024
  5. 5Ethical Considerations in Artificial Intelligence: Addressing Bias and Fairness in Algorithmic Decision-Making2024 · 5 citations