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AI Accountability in Practice

AI Accountability in Practice delves into the critical issue of accountability in AI systems. This program explores who is responsible for the actions and decisions of AI, and how to establish clear lines of accountability throughout the AI lifecycle. It equips project teams with practical strategies for ensuring their AI initiatives are developed, deployed, and operated responsibly.

Recommended for:

  • Developers, engineers, and project managers working on AI projects
  • Policymakers and regulators shaping AI governance frameworks
  • Business leaders implementing AI technologies within their organizations
  • Anyone interested in the responsible development and use of AI

You will:

  • Gain a comprehensive understanding of the concept of accountability in AI.
  • Explore the challenges of assigning accountability in complex AI systems.
  • Learn about different models for allocating accountability in AI development and deployment.
  • Develop practical skills for establishing clear lines of accountability within your AI projects.
  • Discover best practices for managing risk and mitigating potential harms associated with AI.

The AI Ethics and Governance in Practice series

Here is the list of titles available in AI Ethics and Governance in Practice series:

Detailed Overview

AI Accountability in Practice tackles the critical question of who is responsible for the outcomes of AI systems. Developed by The Alan Turing Institute, this program recognizes the growing complexity of AI development, where multiple stakeholders are involved. It emphasizes the importance of establishing clear lines of accountability to ensure responsible AI practices.

The program begins by defining accountability in AI as “the ability to identify and hold actors responsible for the development, deployment, and use of AI systems, and any harms they may cause.” It acknowledges the challenges associated with assigning accountability due to the potential involvement of various actors throughout the AI lifecycle:

  • Data Scientists and Engineers: Responsible for developing and training the AI model.
  • Project Managers and Decision-makers: Oversee the implementation and deployment of the AI system.
  • Organizations Deploying the AI: Hold responsibility for how the AI system is used and the outcomes it produces.
  • Policymakers and Regulators: Responsible for establishing legal frameworks that govern AI development and use.

The program explores different models for allocating accountability in AI, including:

  • Liability-based Accountability: This traditional model assigns legal liability for harms caused by AI to specific actors, such as developers or users.
  • Transparency-based Accountability: This approach emphasizes the importance of explainable AI, allowing stakeholders to understand how decisions are made and who is responsible.
  • Governance-based Accountability: This model focuses on establishing clear governance frameworks and procedures to ensure responsible AI development and deployment.

AI Accountability in Practice emphasizes the need for a multifaceted approach to achieving accountability in AI. The program offers practical strategies that project teams can implement:

  • Mapping Responsibilities: Clearly defining roles and responsibilities for all stakeholders involved in the AI project.
  • Implementing Risk Management Processes: Identifying potential risks associated with the AI system and establishing mitigation strategies.
  • Designing for Explainability: Building AI models that are transparent and allow for understanding of decision-making processes.
  • Monitoring and Auditing: Establishing ongoing monitoring frameworks to track the performance of AI systems and identify any potential issues.

By following these strategies, project teams can build trust and ensure responsible development and deployment of AI. The program concludes by acknowledging the ongoing development of AI governance frameworks and the need for adaptation. It encourages stakeholders to remain informed about evolving legal and ethical considerations surrounding accountability in AI.

Citation and Licensing

Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ., Burr, C., Hadjiloizou, S., and Fischer, C. (2024). AI Accountability 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-accountability-practice

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AI Accountability in Practice
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