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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.
Data Justice in Practice: A Guide for Developers
The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use.
Advancing Data Justice Research and Practice: An Integrated Literature Review
The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a broader understanding of what equitable, freedom-promoting, and rights-sustaining data collection, governance, and use should look like in increasingly dynamic and global data innovation ecosystems.
Data Justice Stories: A Repository of Case Studies
The idea of "data justice" is of recent academic vintage.
Human rights, democracy, and the rule of law assurance framework for AI systems: A proposal
The HUDERAF combines the procedural requirements for principles-based human rights due diligence with the governance mechanisms needed to set up technical and socio-technical guardrails for responsible and trustworthy AI innovation practices.
Artificial intelligence, human rights, democracy, and the rule of law: a primer
In September 2019, the Council of Europe's Committee of Ministers adopted the terms of reference for the Ad Hoc Committee on Artificial Intelligence (CAHAI).
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context
As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle.
The relationship between trust in AI and trustworthy machine learning technologies
To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services.
