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Bookmark or Share- What is explainable AI? - IBMExplainable AI is one of the key requirements for implementing responsible AI, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability.³ To help adopt AI responsibly, organizations need to embed ethical principles into AI applications and processes by building ...
- What is AI Ethics? | IBMExplainability: An AI system should be transparent, particularly about what went into its algorithm’s recommendations, as relevant to a variety of stakeholders with a variety of objectives. Fairness: This refers to the equitable treatment of individuals, or groups of individuals, by an AI system. When properly calibrated, AI can assist humans ...
- How IBM makes AI based on trust, fairness and explainabilityAI Explainability. Explainability in AI is multifaceted. One approach does not fit all cases, because different processes require different explanations. For example, a loan officer asks why you recommended rejection of a loan; the customer wants to know why their loan was denied; the regulator wants proof that your system isn’t discriminatory.
- What Is AI Interpretability? - IBMAI interpretability focuses on understanding the inner workings of an AI model while AI explainability aims to provide reasons for the model's outputs. Interpretability is about transparency, allowing users to comprehend the model's architecture, the features it uses and how it combines them to deliver predictions.
- Explainability example - IBMExplainability is needed to build public confidence in disruptive technology, to promote safer practices, and to facilitate broader societal adoption. There are situations where users may not have access to the full decision process that an AI might go through, e.g. financial investment algorithms .
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