Measuring Ethics in AI with AI: A Methodology and Dataset Construction
This work addresses the need for metrics to inform decision-makers about ethical impacts in AI, though it appears incremental as it applies existing AI methods to a new domain.
The paper tackles the problem of measuring ethics in AI by training a model to classify publications related to ethical issues, using a manually curated dataset and evaluating a large set of research papers, with results including the development of a methodology and dataset for this purpose.
Recently, the use of sound measures and metrics in Artificial Intelligence has become the subject of interest of academia, government, and industry. Efforts towards measuring different phenomena have gained traction in the AI community, as illustrated by the publication of several influential field reports and policy documents. These metrics are designed to help decision takers to inform themselves about the fast-moving and impacting influences of key advances in Artificial Intelligence in general and Machine Learning in particular. In this paper we propose to use such newfound capabilities of AI technologies to augment our AI measuring capabilities. We do so by training a model to classify publications related to ethical issues and concerns. In our methodology we use an expert, manually curated dataset as the training set and then evaluate a large set of research papers. Finally, we highlight the implications of AI metrics, in particular their contribution towards developing trustful and fair AI-based tools and technologies. Keywords: AI Ethics; AI Fairness; AI Measurement. Ethics in Computer Science.