Search Results for author:
Found 4 papers, 0 papers with code
AI Fairness in Practice
In this workbook, we tackle this challenge by exploring how a context-based and society-centred approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow.
AI Sustainability in Practice Part One: Foundations for Sustainable AI Projects
Sustainable AI projects are continuously responsive to the transformative effects as well as short-, medium-, and long-term impacts on individuals and society that the design, development, and deployment of AI technologies may have.
AI Sustainability in Practice Part Two: Sustainability Throughout the AI Workflow
The sustainability of AI systems depends on the capacity of project teams to proceed with a continuous sensitivity to their potential real-world impacts and transformative effects.
AI Ethics and Governance in Practice: An Introduction
AI systems may have transformative and long-term effects on individuals and society.
