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Responsible Research and Innovation in Data Science and AI
Responsible Research and Innovation in Data Science and AI
What is Responsible Research and Innovation
What is Responsible Research and Innovation
About this Module - What is Responsible Research and Innovation
Understanding Responsibility
Collective and Distributed Responsibility
Defining Responsible Research and Innovation
The Scope and Horizon of Responsibility
The Project Lifecycle Model
The Project Lifecycle Model
About this Module - The Project Lifecycle
What is the Project Lifecycle?
Project Design
Model Development
System Deployment
The SAFE-D Modules
The SAFE-D Modules
About these Modules - The SAFE-D Principles
Fairness
Fairness
About this Module - Fairness
What is Fairness?
Sociocultural Fairness
Statistical Fairness
Identifying and Mitigating Bias
Explainability
Explainability
About this Module - Explainability
What is Explainability?
Project Transparency
Model Interpretability
Situated Explanations
Public Engagement in Data Science and AI
Public Engagement in Data Science and AI
What is Public Engagement?
What is Public Engagement?
Climbing the Ladder
Goals of Public Engagement
The Value(s) of Public Engagement
The Value(s) of Public Engagement
Deliberative Values
Responsible Public Engagement
Facilitating Public Engagement
Facilitating Public Engagement
When should you engage
How should you engage
Practical Guidance
Practical Guidance
Storytelling with Data
Communicating Uncertainty
Visualising Uncertainty
Public Trust and Assurance
Public Trust and Assurance
Public Trust in Science and Technology
AI Ethics and Governance
AI Ethics and Governance
Practical Ethics
Practical Ethics
Introduction to Metaethics
Introduction to Normative Theories
AI Harms and Values
AI Harms and Values
AI Harms
AI Values
AI Sustainability
AI Sustainability
Stakeholder Engagement Process
Stakeholder Impact Assessment
Fairness & Bias Mitigation, Accountability, and Governance
Fairness & Bias Mitigation, Accountability, and Governance
Introduction to Fairness
AI Fairness
Bias Mitigation
Accountability
AI Governance
Transparency, Explainability, and CARE and ACT Principles
Transparency, Explainability, and CARE and ACT Principles
Transparency & Explainability
Consider Context
Anticipate Impacts
Reflect on Purpose, Positionality, and Power
Engage Inclusively
Act Responsibly
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