AI Explainability in Practice explores the crucial concept of explainability in AI systems. It delves into why understanding how AI systems reach decisions is critical and equips project teams with practical strategies for building explainable AI. This program empowers developers to create AI systems that are not only effective but also transparent and trustworthy.
Recommended for:
- Developers, engineers, and data scientists working on AI projects
- Project managers and decision-makers overseeing AI implementation
- Policymakers and regulators shaping AI governance frameworks
- Anyone interested in understanding and trusting AI decision-making processes
You will:
- Gain a comprehensive understanding of the importance of explainability in AI.
- Explore different approaches to explain AI models and their functionalities.
- Learn about the benefits of explainable AI for various stakeholders.
- Develop practical skills for designing and building explainable AI systems.
- Discover best practices for communicating AI decisions and outcomes to users.
The AI Ethics and Governance in Practice series
Here is the list of titles available in AI Ethics and Governance in Practice series:
- AI Ethics and Governance in Practice: An Introduction
- AI Sustainability in Practice
- AI Fairness in Practice
- Responsible Data Stewardship in Practice
- AI Safety in Practice
- AI Explainability in Practice
- AI Accountability in Practice
Detailed Overview
AI Explainability in Practice tackles the critical aspect of transparency in AI systems. Developed by The Alan Turing Institute, this program recognizes the growing need for explainable AI. As AI systems become more complex, understanding how they reach decisions becomes crucial for building trust and ensuring responsible AI development.
The program begins by defining explainability in AI as “the ability to understand how an AI system arrives at a particular decision or prediction.” It highlights the importance of explainability for various stakeholders:
- Developers and Engineers: Understanding how AI models function allows for debugging, improving performance, and identifying potential biases.
- Decision-makers: Explainability allows decision-makers to understand the rationale behind AI recommendations before taking action.
- Users: Transparent AI systems increase user trust and confidence in the technology.
- Regulators: Explainability helps regulators assess the fairness and accountability of AI systems.
The program explores different approaches to explainability, each offering varying levels of transparency:
- Model-Agnostic Explainable Techniques (MAETs): These techniques explain the decision-making process of any black-box model by analyzing its behavior on input data.
- Feature Importance: This approach highlights which features in the data have the most significant influence on the AI model’s predictions.
- Counterfactual Explanations: These explanations explore how changing specific input data points might alter the model’s output.
- Decision Trees and Rule-Based Systems: These inherently simpler models offer inherent explainability by presenting the reasoning behind decisions through a series of rules.
AI Explainability in Practice emphasizes that explainability needs to be tailored to the specific AI application and its intended audience. The program offers practical strategies for building explainable AI systems from the ground up:
- Choosing Explainable Models: Selecting algorithms that are inherently interpretable or lend themselves well to explanation techniques.
- Designing for Explainability: Integrating explainability considerations into the design process from the outset.
- Capturing Explainable Information: Recording data and information during model development that can later be used to explain decision-making processes.
- Communicating Explanations Effectively: Presenting AI explanations in a clear, concise, and understandable manner for the target audience.
By implementing these strategies and techniques, developers can build AI systems that are not only effective but also transparent and trustworthy. This program equips project teams to contribute to responsible AI development that fosters trust and ensures AI technology benefits all of society.
Citation and Licensing
Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ. Burr, C., Aitken, M., Mahomed, S., Wong, J., Waller, M., and Fischer, C. (2024). AI Explainibility in Practice. This workbook is published by The Alan Turing Institute and is publicly available on their website: https://www.turing.ac.uk/news/publications/ai-ethics-and-governance-practice-ai-explainability-practice
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