Project description
Evaluating training data filtering to reduce AI harms
Translators increasingly rely on large language models (LLMs) trained on extensive datasets, meaning that the presence of harmful or biased content in this data can be reflected in their translations. However, the effectiveness of current filtering methods is underexplored. With the support of the Marie Skłodowska-Curie Actions programme, the DISHARM project will create a framework for evaluating data filtering strategies that reduce harmful content in training datasets. It will introduce an open leaderboard to compare the effectiveness of these strategies in addressing discrimination against vulnerable identities. Using natural language processing and semantic web technologies, the project will identify the most affected identities and the specific harms they face. It will help meet the legal obligations under the EU AI Act.
Objective
Large Language Models (LLMs) learn everything they know of the World from what they find in training datasets: if datasets include harmful content, it is more likely that they learn how to produce discriminating outputs. Therefore, reducing the presence of harmful contents in the input training dataset is a crucial step to develop safer and fairer technologies. However, the effectiveness of existing data filtering strategies for harm reduction is still an understudied topic in NLP research.
DISHARM's main research objective is to develop the first framework for the systematic evaluation of data filtering strategies with the goal of reducing harmful contents in training datasets. The project foresees the implementation of the first open leaderboard of data filtering strategies, which will enable a comparison of their effects in mitigating or exacerbating harms against different vulnerable identities. The leaderboard is implemented through a system that combines NLP and Semantic Web technologies for revealing which identities are the most discriminated against inside textual datasets and in particular, which are the types of harms that these vulnerable categories are subjected to. In recognizing the social implications of its research objectives, DISHARM will be co-designed through a community-centered approach: the effects of data filtering strategies on vulnerable identities will be evaluated in collaboration with Hate Trackers and Facing Facts, two networks of activists against discrimination who operate in 18 EU countries. DISHARM aligns closely with the objectives of the EU on the regulation of Artificial Intelligence, aiming to provide a new standard for the evaluation of data filtering strategies for LLM training data. This is expected to have a significant impact in the field of NLP studies on bias and fairness and in the actual implementation of legal obligations provided by the EU AI Act on the detection and mitigation of biases in datasets.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
You need to log in or register to use this function
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
2300 KOBENHAVN
Denmark
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.