Bias detection

As explained in Generative AI in Action

Bias detection is the process of identifying systematic distortions or unfair tendencies in data, models, or outputs.

Bias detection looks for systematic distortions in training data or model outputs. In generative AI, it is one part of a broader effort to mitigate harms, alongside representative data, human oversight, and user education. section 13.3.3

Fairness audits can help identify whether model outputs systematically disadvantage certain groups. Detection supports harm mitigation, but is distinct from the steps taken to address identified bias. section 13.3.3 section 13.4.2

Tools named for assessing potential bias in model outputs include:

• IBM AI Fairness 360 section 13.4.2
• Google TensorFlow Fairness Indicators section 13.4.2

Figure 13.11 Harms mitigation layers

Shows bias detection in the context of an iterative, layered approach to mitigating generative-AI harms. section 13.3.3

How Generative AI in Action covers it

How deep it goes

Names detection techniques and audit tools, but does not provide an audit procedure or measurement protocol.

Examples

Names AI Fairness 360 and TensorFlow Fairness Indicators for assessing potential bias in model outputs.

What you can do after

Evaluate model outputs for potential bias and systematic disadvantage using fairness-audit tools.

How it connects

Connects bias detection to representative data, fairness evaluation, model evaluation, red-teaming, and explainability.

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Also explained in

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  Starting Data Analytics with Generative AI and PythonBias detection is the process of identifying systematic distortions or unequal treatment in model behavior or outputs.

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Related concepts

• fairnessmeasures · Fairness is the principle or property of allocating treatment, opportunities, or outcomes equitably among relevant people, groups, or participants according to appropriate criteria.
• biasaddresses · Bias is a systematic deviation or tendency in data, measurements, estimates, judgments, or outputs that skews them relative to a relevant target or impartial baseline.
• responsible AIpart of · Responsible AI is the development, deployment, and operation of AI systems with attention to principles such as safety, fairness, accountability, transparency, privacy, and societal impact.
• auditpart of · An audit is a systematic examination of records, activities, systems, or controls against defined criteria to assess compliance, correctness, or accountability.

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.