MAMMOth was a 36 – month Horizon Europe Research and Innovation Action, funded by the European Union under Grant Agreement ID: 101070285. MAMMOth developed an innovative fairness-aware AI framework that provides several tools and techniques for the discovery and mitigation of multi-discrimination, and for promoting the accountability of AI-systems with respect to multiple protected attributes and for tabular data and more complex network and visual data.
The MAMMOth consortium, which was coordinated by the Centre for Research and Technology Hellas (CERTH), includes computer scientists, AI experts, social scientists, public communication experts, ethics and data protection experts, as well as parties who represent communities of vulnerable and/or underrepresented groups in AI research.
MAMMOth made available both standalone open-source methods and an integrated open source “bias toolkit” (MAI-Bias) that combines new methods with third-party fairness libraries and components.
In addition to the developed research methods, algorithms and tools, the project engaged with communities of vulnerable and/or underrepresented groups in AI research (e.g. the LGBTIQ community, minority groups, migrants), implementing a co-creation strategy to ensure that genuine needs and pains are at the center of the research agenda. Furthermore, the multi-disciplinary approach of the project, which was supported by social science and ethics experts, grounded the project work in valid social science and humanity principles, and moved beyond a simplistic data-driven view of AI bias. It therefore contributed to the discovery of the possible underlying sources of bias and discrimination.
The MAMMOth tools were designed for three sectors of interest:
1. Algorithm-based decision making in finance: The goal was to identify attributes contributing to AI bias in credit scoring and debt repayment, and to develop and test an algorithmic decision-making system that reduces bias in financial services.
2. Decision making in face verification systems: The goal was to address inequalities in the access of minorities to online services using remote face verification, e.g. in the context of digital identity authentication/Know Your Customer (KYC) procedures.
3. Bias in academic collaborations and citations: The goal was to investigate how intersectional biases in search engines like Google Scholar affect the visibility of scholars and measure their impact on the academic network.
