Bias mitigation

As explained in Generative AI in Action

Bias mitigation is the application of methods intended to reduce systematic unfairness or distortion in data, models, or outputs.

Bias mitigation applies methods that reduce systematic unfairness or distortion in training data, model behavior, and generated outputs. In generative AI systems, it is a governance and engineering concern rather than a single model adjustment. section 9.7.2 section 13.3.3

Practical mitigation combines several controls:

• Use diverse and representative data to improve fairness and reduce dataset bias. section 13.3.3
• Detect and mitigate bias in both training data and model outputs. section 13.3.3
• Add human oversight, evaluator cross-validation, monitoring, and continuous evaluation. section 9.7.2 section 13.3.3
• Layer model, safety-system, and application controls, including red-teaming and output filtering. section 13.3.3

Bias mitigation during RLHF

When scaling reinforcement learning from human feedback, use intelligent sampling to prioritize valuable evaluation instances, distribute evaluator work, cross-check feedback, and monitor model performance. These controls help prevent feedback from reinforcing harmful stereotypes. section 9.7.2

Figure 13.11 Harms mitigation layers

Use the harms-mitigation layers to examine how model, safety-system, and application controls work together as defense in depth. section 13.3.3

How Generative AI in Action covers it

How deep it goes

Explains practical controls and layered defenses but stops short of mathematical bias measures or detailed mitigation algorithms.

Examples

Uses RLHF evaluator controls, diverse datasets, red-teaming, Azure AI Content Filters, and layered harms-mitigation controls.

What it assumes

Assumes familiarity with LLMs and model training while introducing evaluation quality, oversight, and defense-in-depth controls.

What you can do after

Apply layered evaluation and oversight to identify bias, reduce harmful outputs, and guide continuous model improvement.

How it connects

Connects bias mitigation with RLHF, active learning, representative data, human oversight, red-teaming, and content filtering.

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  Starting Data Analytics with Generative AI and PythonBias mitigation is the application of measures intended to reduce systematic distortion or unfairness in model behavior or outputs.

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Related concepts

• AI Actconfigured by · The AI Act is a European Union regulation establishing harmonised, risk-based requirements for the development, placing on the market, and use of artificial intelligence systems.
• post-processingaddressed by · Post-processing is additional processing applied to generated or computed output after its initial production to transform, validate, filter, refine, or interpret it.
• content filteringimplemented by · Content filtering is the inspection of content against defined criteria to determine whether it should be blocked, modified, flagged, or removed.
• AI regulationaddressed by · AI regulation is the body of laws, rules, and official requirements governing the development, deployment, disclosure, and use of artificial-intelligence systems.
• data curationimplemented by · Data curation is the systematic selection, review, cleaning, organization, and maintenance of data for a particular purpose.

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.