Impact Matters! An Audit Method to Evaluate AI Projects and their Impact for Sustainability and Public Interest
Theresa Z\"uger, Laura State, Lena Winter

TL;DR
This paper introduces the Impact-AI-method, a qualitative audit framework designed to evaluate AI projects' impacts on society and sustainability, promoting transparency and public debate.
Contribution
It presents a novel audit method based on a dual-regulatory framework of public interest and sustainability for assessing AI projects.
Findings
Developed a comprehensive interview-based audit process
Created a catalog of assessment criteria for impact evaluation
Designed an accessible output for broad civil society debate
Abstract
The overall rapid increase of artificial intelligence (AI) use is linked to various initiatives that propose AI 'for good'. However, there is a lack of transparency in the goals of such projects, as well as a missing evaluation of their actual impacts on society and the planet. We close this gap by proposing public interest and sustainability as a regulatory dual-concept, together creating the necessary framework for a just and sustainable development that can be operationalized and utilized for the assessment of AI systems. Based on this framework, and building on existing work in auditing, we introduce the Impact-AI-method, a qualitative audit method to evaluate concrete AI projects with respect to public interest and sustainability. The interview-based method captures a project's governance structure, its theory of change, AI model and data characteristics, and social, environmental,…
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Taxonomy
TopicsEthics and Social Impacts of AI · Sustainability and Climate Change Governance · Innovation, Sustainability, Human-Machine Systems
