Titlebook: Building Responsible AI Algorithms; A Framework for Tran Toju Duke Book 2023 Toju Duke 2023 Responsible AI.Fairness Metrics.Data Quality.Tr

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DataAfter setting up and understanding the AI principles, the next foundational element and part of responsible AI development is looking at the data and its guiding principles.

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SafetyThe previous chapter looked at fairness and explained how it relates to responsible AI. This chapter looks at AI safety and explains how it contributes to a responsible AI framework.

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ExplainabilityWhile the previous chapter covered Human-in-the-Loop and its importance in building responsible AI algorithms, it’s paramount to ensure the transparency and explainability of ML models following HITL processes. This chapter reviews “explainability,” also known as XAI and its implementation.

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RobustnessIn addition to ensuring that ML models and applications are respectful and cognizant of people’s privacy, and that they don’t infringe on human rights by violating privacy laws, it’s also important that AI technologies be protected from cyberattacks. This involves building robust ML models, which is another fundamental part of responsible AI.

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https://doi.org/10.1007/978-1-4842-9306-5Responsible AI; Fairness Metrics; Data Quality; Transparent AI Models; Explainability; Responsible AI Fra

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APNEA

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