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TRV-2026-0813Certified recordPeer-reviewed

Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies

The significant advancements in applying artificial intelligence (AI) to healthcare decision-making, medical diagnosis, and other domains have simultaneously raised concerns about the fairness and bias of AI systems. This is particularly critical in areas like healthcare, employment, criminal justice, credit scoring, and increasingly, in generative AI models (GenAI) that produce synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including generative biases that affect…

Health · The Trace — both readings · certified 2026-08-17 · v1 · article view · machine-readable

Current reading — gain

Using mitigation approaches such as diverse and representative datasets and enhanced transparency and accountability can improve fairness of AI systems applied to healthcare decision-making and medical diagnosis.

Current reading — problem

AI systems applied to healthcare decision-making, medical diagnosis, and other domains can lead to unfair outcomes that perpetuate existing inequalities and reinforce harmful stereotypes, including generative biases in synthetic media.

What this doesn’t fix

Generative AI presents unique challenges requiring tailored mitigation strategies rather than general approaches alone.

Evidence

  • Peer-reviewedSci2023-12-26
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Truvace Impact Record TRV-2026-0813, v1: “Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies.” Truvace, 2026-08-17. /record/TRV-2026-0813 (accessed at citation time). sha256 e8209798c7374d6e…

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