Project description
Innovative toolset for controlling bias in AI technology
Novel trends have spurred substantial growth in the use of artificial intelligence (AI) model technology across diverse industries, including finance, education, and healthcare. However, this expansion has raised concerns regarding bias in these algorithms and their compliance with the EU AI Act. In this context, the ERC-funded Act.AI project endeavours to address this challenge by developing a solution that leverages statistical matching to mitigate and audit bias in AI models. These biases are becoming increasingly difficult to identify. The project therefore aims to create an easily integrable tool for AI workflows, providing continuous monitoring and remediation capabilities.
Objective
The vision behind Act.AI is to utilize statistical matching for mitigating and auditing bias in Artificial Intelligence (AI) models. AI has been rapidly growing in various industries, from financial services to healthcare, education, and job recruitment. However, as AI algorithms have become increasingly sophisticated and pervasive in decision-making processes, concerns have arisen about their fairness and compliance with regulations. In particular, the EU AI Act requires that AI providers in high-risk applications -- such as employment, credit, or healthcare -- to identify (and thereby address) discrimination by their algorithms against certain demographics of people. However, ensuring compliance with the Act can be challenging, particularly for AI startups that may not have the resources or expertise to fully understand and implement the Act's requirements. Addressing existing disconnects between AI fairness toolkits' capabilities and current practitioner needs, the Act.AI tool can be easily integrated into any AI workflow, in a plug and play fashion, to continuously monitor and improve its fairness. A key aspect of Act.AI is the ability to operate with different types of data (tabular, images, and text) in a variety of contexts (binary and multiclass classification and regression). It is also able to match datasets in different domains including out-of-distribution data even if these datasets have different numbers of variables or features. To ensure usability of Act.AI it will integrate feedback from relevant stakeholders from two immediate target markets: financial service and healthcare.
Fields of science
Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Funding Scheme
HORIZON-ERC-POC - HORIZON ERC Proof of Concept GrantsHost institution
48009 Bilbao
Spain