Explaining Algorithms: How Transparency Shapes Public Support

The Digital Services Act and the AI Act adopted by European institutions require algorithmic decisionmaking systems to meet transparency obligations through the provision of explanations of their functioning. As algorithmic decision-making systems increasingly shape individuals' economic and social lives, this paper experimentally tests whether adding a non-technical explanation to a neutral system description affects public acceptance. The study relies on a large-scale survey experiment on nationally representative adult samples in France, Germany, and Italy in which each respondent evaluates six algorithmic and AI systems spanning finance, health, public services, employment, online commerce, and digital media. We measure the economically relevant dimensions of adoption and legitimacy, including beliefs, evaluative attitudes, and willingness to delegate decisions. Explanations yield measurable, though modest, increases in willingness to delegate. A mechanism-consistent decomposition shows that these effects arise primarily through improved attitudes toward the systems, while direct effects and belief shifts play a secondary role. Overall, explanations reliably move acceptance in the intended direction, but do not eliminate persistent concerns, especially those related to privacy. The results highlight both the promise and limits of information disclosure as a regulatory tool.