Bias
A systematic difference in how an AI system treats or represents people, groups, objects, or outcomes that can lead to unfair or discriminatory results.
Definitions (4)
Systematic errors in AI systems that produce unfair or discriminatory outcomes, particularly against protected groups, which the regulation requires to be identified, assessed, and mitigated through bias prevention measures and impact assessments.
Systematic distortions in AI outputs or decision-making that produce unequal, unfair, or discriminatory outcomes for individuals or groups, arising from data, model design, labeling, or deployment contexts; the Plan frames bias as a rights-related risk to be detected, measured and mitigated.
Understood as undesirable outcomes where AI systems might generate, reproduce, reinforce, or perpetuate unfair treatment. The guidelines aim to prevent this through careful design and oversight.
The guidance also implicitly touches upon concepts like Bias, referring to systematic errors in an AI model's output that could lead to unfair or inaccurate results, particularly across diverse patient populations.
Related Terms
Data Quality
The fitness of dataset(s) for an AI system’s intended purpose, judged by attributes like relevance, accuracy, representativeness, completeness and provenance....
Fairness
Absence of unjustified bias or discrimination in AI outputs and equitable treatment and access across individuals and groups....
Bias Mitigation
Measures to identify and reduce discriminatory outcomes in AI systems....
Algorithmic Discrimination
Unfair or unlawful differential treatment or impact caused by an AI or algorithmic system against individuals or groups, often on protected characteristics....
Training Data
Data used to develop and train AI models to perform their intended functions....