Algorithmic Bias

Technical explanation

Bias can arise from historical conditions, problem framing, sampling, labels, measurement, proxies, model design, optimisation objectives, deployment context, or human interpretation. It is socio-technical: even statistically accurate models can create harmful outcomes when categories, thresholds, or uses are inappropriate.

Business relevance

Unmanaged bias can create legal exposure, exclusion, poor decisions, reputational damage, and loss of trust. Identifying it requires stakeholder analysis, representative evaluation, documented trade-offs, and monitoring after deployment.

Implementation example

A hiring model is evaluated by role and relevant demographic groups. The organisation discovers that a proxy feature disadvantages qualified applicants, removes it, adjusts the process, and adds human review and appeal routes.

Limitations and common misconceptions

Not every difference in outcomes proves unlawful discrimination, and no single fairness metric works for every context. Some fairness criteria conflict mathematically, so acceptable trade-offs require explicit governance and domain expertise.

Topics

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