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It is a property of both the AI model and the human user, as it depends on the level of transparency, explainability, and comprehensibility of the model, as well as the background knowledge, expectations, and cognitive skills of the user.
AI interpretability is important for ensuring the trust, accountability, fairness, and safety of AI systems, especially in high-stakes domains such as healthcare, finance, and law. AI interpretability is not a binary or absolute concept, but rather a relative and contextual one, as different users may have different needs and preferences for how much and what kind of information they want to receive from an AI system.
What is interpretability of AI?
Interpretability of AI is the degree to which a human can understand the internal logic, structure, and behavior of an AI model. Interpretability can be measured by how well a human can predict the output of the model given an input, or how well a human can explain the rationale behind the output.
Interpretability can also be influenced by the type, format, and quality of the information provided by the model, such as feature importance, decision rules, or visualizations.
What is Interpretive AI?
Interpretive AI is a branch of AI that focuses on developing and applying methods and techniques for enhancing the interpretability of AI models. Interpretive AI aims to provide human-understandable explanations for the decisions and actions of AI systems, as well as to enable human feedback and interaction with the systems.
Interpretive AI can be divided into two main approaches: intrinsic and post-hoc. Intrinsic interpretability refers to designing AI models that are inherently interpretable, such as linear models, decision trees, or rule-based systems. Interpretability refers to meeting the transparency requirement at the task definition level, whereas explanation refers to a post-hoc (after training) evaluation of the model understandability.
What is the meaning of interpretability?
Interpretability is the quality of being clear, understandable, and meaningful. In the context of AI, interpretability means that the AI system can provide information that helps the human user to comprehend its functioning, reasoning, and outcomes. Interpretability can also mean that the AI system can adapt to the human user’s needs, preferences, and goals, and that the human user can influence the AI system’s behavior and performance.
What is interpretability in intelligent systems?
Interpretability in intelligent systems is the ability of the system to communicate and justify its internal processes, decisions, and actions to the human user or stakeholder. Interpretability in intelligent systems can be achieved by using various methods, such as natural language generation, visual analytics, interactive interfaces, or causal inference.
Interpretability in intelligent systems can have various benefits, such as increasing the user’s trust, satisfaction, and engagement, improving the system’s accuracy, robustness, and efficiency, and facilitating the system’s verification, validation, and debugging.
Why is AI interpretability important?
AI interpretability is important for several reasons, such as:
- Trust: AI interpretability can help to build and maintain the user’s trust in the AI system, by providing transparency, accountability, and feedback. Trust is essential for the user’s acceptance, adoption, and satisfaction with the AI system, especially in domains where the user’s safety, privacy, or well-being are at stake.
- Ethics: AI interpretability can help to ensure the ethical and responsible use of AI, by revealing and preventing potential biases, errors, or harms caused by the AI system. Ethics is crucial for the social and legal acceptance, regulation, and governance of AI, especially in domains where the AI system affects the rights, values, or interests of the user or other stakeholders.
- Learning: AI interpretability can help to enhance the user’s learning and understanding of the AI system, by providing insights, explanations, and guidance. Learning is important for the user’s competence, confidence, and collaboration with the AI system, especially in domains where the user needs to acquire new skills, knowledge, or strategies.
Examples of AI Interpretability
Some examples of AI interpretability and its track records are:
- LIME: LIME is a post-hoc interpretability method that stands for Local Interpretable Model-agnostic Explanations. It works by perturbing the input of a black-box model and observing the changes in the output. It then fits a simple and interpretable model, such as a linear regression or a decision tree, to approximate the behavior of the black-box model in the local neighborhood of the input. LIME can provide feature importance, decision rules, or visualizations to explain the predictions of any type of model. LIME has been applied to various domains, such as text classification, image recognition, or sentiment analysis
- SHAP: SHAP is a post-hoc interpretability method that stands for SHapley Additive exPlanations. It is based on the concept of Shapley values, which are a game-theoretic measure of the contribution of each feature to the prediction of a model. SHAP can provide feature importance, feature interactions, or visualizations to explain the predictions of any type of model. SHAP has been applied to various domains, such as healthcare, finance, or education
- Optimal Decision Trees: Optimal Decision Trees are a type of intrinsic interpretable model that generate interpretable decision trees from data. They use optimization techniques, such as integer programming or genetic algorithms, to find the optimal tree structure and parameters that maximize the accuracy and interpretability of the model. Optimal Decision Trees can provide decision rules, feature importance, or visualizations to explain the predictions of the model. Optimal Decision Trees have been applied to various domains, such as marketing, fraud detection, or risk management3
Some terms related to AI interpretability are:
- Explainable AI: Explainable AI is a branch of AI that focuses on developing and applying methods and techniques for providing human-understandable explanations for the decisions and actions of AI systems.
- Transparent AI: Transparent AI is a branch of AI that focuses on developing and applying methods and techniques for making the internal logic, structure, and behavior of AI models more accessible and observable to the human user or stakeholder.
- Comprehensible AI: Comprehensible AI is a branch of AI that focuses on developing and applying methods and techniques for making the AI models more compatible and aligned with the human user’s cognitive skills, background knowledge, and expectations.
AI interpretability is a key aspect of creating helpful and high-quality content for Google Search. It can help to improve the trust, ethics, and learning of the users and stakeholders of AI systems, as well as to enhance the accuracy, robustness, and efficiency of the systems.
AI interpretability can be achieved by using various methods and techniques, such as LIME, SHAP, or Optimal Decision Trees, that can provide feature importance, decision rules, or visualizations to explain the predictions of any type of model.
AI interpretability can also be influenced by the type, format, and quality of the information provided by the model, as well as the background knowledge, expectations, and cognitive skills of the user. AI interpretability is a dynamic and evolving field that requires constant research and innovation to address the challenges and opportunities of AI.