How deeply does Inside AI cover fairness?

Defines fairness through equitable treatment and bias avoidance, but does not specify metrics or implementation procedures.

Fairness

As explained in Inside AI

Fairness is the property of treating people equitably and avoiding unjustified disparities in system outcomes.

Fairness means treating people equitably and avoiding unjustified disparities in system outcomes. For AI that affects citizens, fairness requires more than accurate predictions: systems should demonstrate a lack of bias and explain decisions in plain language. section 10.2

Criminal-justice decisions

Consider a program deciding whether someone should receive incarceration or probation. If medication or social support could address behavioral problems, relying on an opaque system may produce harmful and unfair outcomes. section 10.2

Fairness belongs within broader validation of reliability, privacy, transparency, and explicability. Systems affecting individuals need stringent technical and ethical review before deployment. section 10.2

How Inside AI covers it

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Also explained in

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Think Like a Software Engineering ManagerFairness is the equitable and impartial treatment of people according to consistent standards and processes.
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Generative AI in ActionFairness is the property of treating people equitably and avoiding unjustified disparities in the outcomes or treatment produced by an AI system.
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Starting Data Analytics with Generative AI and PythonThe property of treating relevant individuals or groups equitably and avoiding unjustified disparities in AI systems or their use.
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Introduction to Generative AI, Second EditionFairness is the property of treating individuals and groups equitably in an algorithmic system's design, operation, and outcomes.
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Machine Learning System DesignThe property that a model’s behavior or performance does not produce unjustified disparities across relevant demographic or other groups.
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Causal AIFairness is the property that an algorithmic or statistical system treats individuals or groups according to specified equity criteria.
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Building Reliable AI SystemsThe property of an AI system treating people and groups equitably and avoiding unjustified disparities in outcomes.
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The Art of AI Product DevelopmentFairness is the property of treating relevant people or groups equitably in an AI system's behavior and outcomes.
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Financial AI in PracticeThe principle that decisions and outcomes should treat affected people or groups equitably and avoid unjustified discrimination.

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Related concepts

bias detectionmeasured by · Bias detection is the systematic assessment of data, decisions, or system behavior to identify distortions or unjustified disparities across groups or conditions.
machine learningpart of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
livenesskind of · Liveness is a correctness property requiring that a system eventually makes specified progress or produces a desired event or condition.
Gini impuritymeasured by · Gini impurity is a measure of class-label heterogeneity in a set or decision-tree node, calculated from the proportions of its classes.
diversity, equity, and inclusionpart of · Diversity, equity, and inclusion is an organizational principle and practice promoting representation, fairness, participation, and belonging for people with different identities and backgrounds.

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