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Accuracy: Accuracy is also one of data protection’s foundational principles and it relates to inputs not outputs. In our guidance on AI and data protection we use the term statistical accuracy to describe what you term as accuracy here, so we see the term ‘statistical accuracy’ as more appropriate to avoid confusing organisations processing personal data in the context of AI.
Bias: Humans (reviewers, labellers, etc) can also be the source of bias, not just the datasets themselves. With that in mind, describing bias as the outcome of ‘errors’ may not capture all of its sources.
Fairness: Fairness as well as is one of data protection’s foundational principles. The definition given in the dictionary appears to conflate fairness with anti-discrimination. But fairness in data protection even though it encompasses discrimination concerns, is broader than discrimination. The ICO has said “fairness means that you should only handle personal data in ways that people would reasonably expect and not use it in ways that have unjustified adverse effects on them”. An unfair decision allocated indiscriminately across groups is not automatically fair.
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