Project description
Ensuring fairness in AI-powered public services
Public service encounters (PSE) are a fundamental part of citizen-state interactions, occurring millions of times each year. As digitalisation accelerates, AI-powered agents are increasingly replacing human civil servants in these interactions. However, the shift raises critical questions about biases in communication and their impact on citizen perceptions. Surprisingly, little is known about the biases that exist in traditional human encounters, making it difficult to assess AI’s influence. In this context, the ERC-funded BiASE-AI project aims to fill this knowledge gap by studying communicative biases in PSE. By leveraging natural language processing, analysing human and AI interactions, and conducting experiments, the project will provide crucial insights to ensure AI enhances public services without compromising fairness or democratic legitimacy.
Objective
Artificial intelligence (AI) holds the potential to assist or even replace civil servants in citizen-state interactions, which refer to those instances in which citizens seek or receive assistance, information, or support from a public agency. In Germany alone, around 130 to 150 million such public service encounters (PSE) occur each year, with the current proportion of digital encounters, facilitated through email or chatbots, standing at 41 percent. This is only the beginning of a revolution that will accelerate due to technological innovation and scarcity in human resources. The goal of this project is to study the consequences of shifting from human to AI-powered conversational agents in PSE.
Paradoxically, while the transition to artificial encounters is already taking place in practice, we still lack understanding of what happens during conventional human encounters. This prevents researchers from evaluating the consequences of shifting from one venue to the other. Therefore, the project’s main research questions are: How can we conceptualize and measure communicative biases in PSE? What biases emerge in the verbal communication of humans and AI agents in PSE? How does variation in communication affect citizen perceptions of the encounter?
AI has the potential to improve service delivery, but there are also risks for the legitimacy of democracy. Without further research, the introduction of AI threatens to merely shift biases from one venue (civil servants) to another (AI agent). As its ground-breaking contributions, the project (1) develops natural language processing tools to operationalize verbal communication; (2) collects data from human encounters in PSE and realizes experiments on AI-based encounters; (3) and analyses such data to formulate and test a theory about communicative biases in PSE. Doing so, (4) the project will produce and disseminate urgently needed practical insights on the application of AI conversational agents in the public sector.
Fields of science (EuroSciVoc)
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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.
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- social sciences political sciences government systems democracy
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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.
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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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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.
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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-ERC - HORIZON ERC Grants
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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) ERC-2024-COG
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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.
88045 FRIEDRICHSHAFEN
Germany
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.