A lack of political legitimacy undermines the ability of the European Union (EU) to resolve major crises and threatens the stability of the system as a whole. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. In particular, artificial intelligence (AI) systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, and evaluating policy effectiveness. This paper investigates how citizens’ perceptions of EU input, throughput, and output legitimacy are influenced by three distinct decision-making arrangements: (a) independent human decision-making by EU politicians; (b) independent algorithmic decision-making (ADM) by AI-based systems; and (c) hybrid decision-making (HyDM) by EU politicians and AI-based systems together. The results of a preregistered online experiment (n = 572) suggest that existing EU decision-making arrangements are still perceived as the most participatory and accessible for citizens (input legitimacy). However, regarding the decision-making process itself (throughput legitimacy) and its policy outcomes (output legitimacy), no difference was observed between the status quo and HyDM. Respondents tend to perceive ADM systems as the sole decision-maker to be illegitimate. The paper discusses the implications of these findings for (a) EU legitimacy and (b) data-driven policy-making and outlines (c) avenues for future research.

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Despite the immense societal importance of ethically designing artificial intelligence, little research on the public perceptions of ethical artificial intelligence principles exists. This becomes even more striking when considering that ethical artificial intelligence development has the aim to be human-centric and of benefit for the whole society. In this study, we investigate how ethical principles (explainability, fairness, security, accountability, accuracy, privacy, and machine autonomy) are weighted in comparison to each other. This is especially important, since simultaneously considering ethical principles is not only costly, but sometimes even impossible, as developers must make specific trade-off decisions. In this paper, we give first answers on the relative importance of ethical principles given a specific use case—the use of artificial intelligence in tax fraud detection. The results of a large conjoint survey ([Formula: see text]) suggest that, by and large, German respondents evaluate the ethical principles as equally important. However, subsequent cluster analysis shows that different preference models for ethically designed systems exist among the German population. These clusters substantially differ not only in the preferred ethical principles but also in the importance levels of the principles themselves. We further describe how these groups are constituted in terms of sociodemographics as well as opinions on artificial intelligence. Societal implications, as well as design challenges, are discussed.

The ethical and psychological consequences of using Artificial Intelligence (AI) to manipulate our perception of others is an increasing phenomenon as image-altering filters proliferate on social media and video conferencing technologies. Here, we investigate the potential impact of a particular appearance-altering technology-blur filters-to investigate how individuals' behavior changes towards others. Our results consistently indicate an increase in selfish behavior at the expense of blurred individuals, suggesting blur filters can facilitate moral disengagement via depersonalization. These findings underscore the urgency for broader ethical discussions on AI technologies that alter our perception of others, encompassing transparency, consent, and the consequences of knowing that others can manipulate one's appearance. We highlight the potential role of anticipatory experiments in informing and developing responsible guidelines and policies ahead of technological reality.

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Algorithmic decision-making increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of algorithmic decision-making. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible algorithmic decision-making.

Political communication via social media might well counter the eroding political trust. In particular, social networking sites (SNS) enable direct flows of communication between citizens and the political elite, thereby reducing social and political gaps. Based on the concept of personalization of politics, we argue that interactions with politicians on SNS affect trust in government through a two-step process: First, interactions on SNS make citizens evaluate politicians’ characters more favorably. Second, these evaluations serve as cues for the citizens to develop or withdraw trust in government. We test indirect effects using four character traits as mediators: leadership, benevolence, responsiveness, and likeability. A representative online survey ( n = 1117; in Germany) reveals that interactions with politicians on SNS increase the perceived likeability of candidates, and thus also trust in government. However, they do not affect the evaluation of the other traits: leadership, benevolence, and responsiveness.