AI For Fairer Public Services Globally: Key Findings From International Research

An international consortium of researchers led by Mainz University has published findings on how artificial intelligence (AI) can enhance fairness in the distribution of public social services globally. The study, supported by a EUR 1.5 million grant from the Volkswagen Foundation and conducted over three and a half years, analyzed AI’s role in decisions such as state pensions, unemployment benefits, asylum applications, and kindergarten placements across nine countries in Germany, Spain, Estonia, Ukraine, the USA, Nigeria, Iran, India, and China.

The research highlighted significant variations in fairness criteria, including cultural factors like India’s caste system and China’s “good citizenship” score, underscoring the need for adaptable AI systems that reflect diverse societal contexts and involve all stakeholders, particularly vulnerable groups, in their development.

Artificial Intelligence for Fair Distribution of Public Social Services Globally

Artificial intelligence (AI) is increasingly utilized globally to enhance fairness in distributing public social services. AI systems assist in entitlement decisions for state-paid pensions, unemployment benefits, asylum applications, and kindergarten placements. These technologies aim to apply fairness criteria consistently across diverse contexts, though challenges arise due to differing cultural and societal norms. For instance, India‘s caste system influences social benefit distribution, while China employs a “good citizenship” score to determine access to services. Even within Europe, concepts of fairness vary significantly.

The AI FORA project, coordinated by Johannes Gutenberg University Mainz (JGU), conducted comparative analyses of AI-based social assessments across nine countries on four continents: Germany, Spain, Estonia, Ukraine, the USA, Nigeria, Iran, India, and China. The findings highlight how cultural and contextual factors shape the criteria for accessing state services, often subject to negotiation within societies. This underscores the need for flexible, adaptive AI systems that account for diverse perspectives, including those of vulnerable groups.

The project emphasizes that a one-size-fits-all AI solution is insufficient for global application. Instead, participatory approaches involving all stakeholders are essential to develop context-sensitive and fair AI systems. The researchers aim to publish further findings on policy-relevant modeling and simulation results, demonstrating how AI can be improved to address fairness and discrimination in public social service allocation.

Collaborative Research on AI in Social Assessments Across Nine Countries

The findings from the AI FORA project demonstrate that fairness in AI-supported social assessments is not a static concept but dynamic and context-dependent. This requires ongoing dialogue and stakeholder collaboration to ensure AI systems remain aligned with societal values and priorities. The project’s comparative analysis provides valuable insights for policymakers and developers seeking to implement AI technologies that promote equity and reduce discrimination in the distribution of public social services.

Developing Context-Sensitive and Adaptive AI Systems for Fairness

Developing context-sensitive AI systems requires a deep understanding of cultural and societal norms. These systems must be designed to adapt to diverse definitions of fairness, which vary across regions and communities. For instance, in some cultures, fairness may prioritize equitable resource distribution, while in others, it may emphasize merit-based allocation. To address these differences, AI systems must incorporate flexible frameworks that allow for customization based on local values.

The AI FORA project highlights the importance of participatory design in creating fair AI systems. By involving stakeholders from diverse backgrounds, including marginalized groups, developers can ensure that AI tools reflect a broader range of perspectives. This approach not only enhances the fairness of decision-making processes but also increases public trust in AI technologies.

Dynamic societal changes further complicate the development of adaptive AI systems. As cultural norms evolve, AI systems must be capable of updating their algorithms to align with new values and priorities. This requires ongoing monitoring and feedback mechanisms to ensure that AI remains responsive to shifting societal needs.

Another critical aspect of developing context-sensitive AI is integrating machine learning models that learn from diverse datasets. By training models on data representative of different cultural contexts, developers can reduce biases and improve the accuracy of fairness-related decisions. However, this process must be carefully managed to avoid reinforcing existing inequalities or introducing new forms of discrimination.

Ultimately, the success of adaptive AI systems depends on their ability to balance technical precision with cultural sensitivity. By prioritizing collaboration, transparency, and continuous improvement, developers can create tools that promote equity and reduce disparities in public social service allocation.