Christopher Starke

Kimon Kieslich, Birte Keller, Christopher Starke

Despite the immense societal importance of ethically designing artificial intelligence (AI), little research on the public perceptions of ethical AI principles exists. This becomes even more striking when considering that ethical AI 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, 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 AI in tax fraud detection. The results of a large conjoint survey (n=1099) suggest that, by and large, German respondents found the ethical principles 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 attributes, but also in the importance level of the attributes themselves. We further describe how these groups are constituted in terms of sociodemographics as well as opinions on AI. Societal implications as well as design challenges are discussed.

Christopher Starke, Janine Baleis, Birte Keller et al.

Algorithmic decision-making (ADM) 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 taking people's fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (a) algorithmic predictors, (b) human predictors, (c) comparative effects (human decision-making vs. algorithmic decision-making), and (d) consequences of ADM. 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 ADM.

Nils Köbis, Christopher Starke, Iyad Rahwan

Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens' views, hence putting "society in the loop".

Christopher Starke, Marco Luenich

A lack of political legitimacy undermines the ability of the European Union 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 systems have the potential to increase political legitimacy by identifying pressing societal issues, forecasting potential policy outcomes, informing the policy process, 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. First, independent human decision-making, HDM, Second, independent algorithmic decision-making, ADM, and, third, hybrid decision-making by EU politicians and AI-based systems together. The results of a pre-registered online experiment with 572 respondents suggest that existing EU decision-making arrangements are still perceived as the most democratic - 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 hybrid decision-making involving both ADM and democratically elected EU institutions. Where ADM systems are the sole decision-maker, respondents tend to perceive these as illegitimate. The paper discusses the implications of these findings for EU legitimacy and data-driven policy-making.