Artificial Intelligence for EU Decision-Making. Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy

• URL: http://arxiv.org/abs/2003.11320v1
• Date: Wed, 25 Mar 2020 10:56:28 GMT
• Title: Artificial Intelligence for EU Decision-Making. Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy
• Authors: Christopher Starke, Marco Luenich
• Abstract summary: Lack of political legitimacy undermines the ability of the European Union to resolve major crises. By integrating digital data into political processes, the EU seeks to base decision-making increasingly on sound empirical evidence. This paper investigates how citizens perceptions of EU input, throughput, and output legitimacy are influenced by three decision-making arrangements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Advancing Science- and Evidence-based AI Policy [163.43609502905707]
  This paper tackles the problem of how to optimize the relationship between evidence and policy to address the opportunities and challenges of AI.<n>An increasing number of efforts address this problem by often either (i) contributing research into the risks of AI and their effective mitigation or (ii) advocating for policy to address these risks.
  arXiv  Detail & Related papers  (2025-08-02T23:20:58Z)
• Aligning Trustworthy AI with Democracy: A Dual Taxonomy of Opportunities and Risks [26.916552909766118]
  This paper introduces a dual taxonomy to evaluate AI's complex relationship with democracy.<n>TheAIRD taxonomy identifies how AI can undermine core democratic principles such as autonomy, fairness, and trust.<n>The AIPD taxonomy highlights AI's potential to enhance transparency, participation, efficiency, and evidence-based policymaking.
  arXiv  Detail & Related papers  (2025-05-19T10:51:08Z)
• AI-Enhanced Deliberative Democracy and the Future of the Collective Will [1.3812010983144802]
  We explore AI-based democratic innovations as discovery tools for reasonable representations of a collective will, sense-making, and agreement-seeking.<n>At the same time, we caution against dangerously misguided uses, such as enabling binding decisions, fostering gradual unpackment or post-rationalizing political outcomes.
  arXiv  Detail & Related papers  (2025-03-06T00:06:22Z)
• Human Decision-making is Susceptible to AI-driven Manipulation [87.24007555151452]
  AI systems may exploit users' cognitive biases and emotional vulnerabilities to steer them toward harmful outcomes. This study examined human susceptibility to such manipulation in financial and emotional decision-making contexts.
  arXiv  Detail & Related papers  (2025-02-11T15:56:22Z)
• Biased AI can Influence Political Decision-Making [64.9461133083473]
  This paper presents two experiments investigating the effects of partisan bias in AI language models on political decision-making. We found that participants exposed to politically biased models were significantly more likely to adopt opinions and make decisions aligning with the AI's bias.
  arXiv  Detail & Related papers  (2024-10-08T22:56:00Z)
• From Experts to the Public: Governing Multimodal Language Models in Politically Sensitive Video Analysis [48.14390493099495]
  This paper examines the governance of large language models (MM-LLMs) through individual and collective deliberation. We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI.
  arXiv  Detail & Related papers  (2024-09-15T03:17:38Z)
• Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning [72.80902932543474]
  Understanding human behavior from observed data is critical for transparency and accountability in decision-making. Consider real-world settings such as healthcare, in which modeling a decision-maker's policy is challenging. We propose a data-driven representation of decision-making behavior that inheres transparency by design, accommodates partial observability, and operates completely offline.
  arXiv  Detail & Related papers  (2023-10-28T13:06:14Z)
• Key Factors Affecting European Reactions to AI in European Full and Flawed Democracies [1.104960878651584]
  This study examines the key factors that affect European reactions to artificial intelligence (AI) in the context of full and flawed democracies in Europe. It is observed that flawed democracies tend to exhibit higher levels of trust in government entities compared to their counterparts in full democracies. Individuals residing in flawed democracies demonstrate a more positive attitude toward AI when compared to respondents from full democracies.
  arXiv  Detail & Related papers  (2023-10-04T22:11:28Z)
• AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions [6.356355538824237]
  We argue that reliance and decision quality are often inappropriately conflated in the current literature on AI-assisted decision-making. Our research highlights the importance of distinguishing between reliance behavior and decision quality in AI-assisted decision-making.
  arXiv  Detail & Related papers  (2023-04-18T08:08:05Z)
• Causal Fairness Analysis [68.12191782657437]
  We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings. The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms. Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
  arXiv  Detail & Related papers  (2022-07-23T01:06:34Z)
• A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making [0.0]
  Automated decision systems (ADS) are increasingly used for consequential decision-making. Non-transparent systems are prone to yield unfair outcomes because their sanity is challenging to assess and calibrate. I aim to make the following three main contributions through my doctoral thesis.
  arXiv  Detail & Related papers  (2022-04-29T18:31:04Z)
• Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
  We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
  arXiv  Detail & Related papers  (2022-03-14T17:40:42Z)
• "A cold, technical decision-maker": Can AI provide explainability, negotiability, and humanity? [47.36687555570123]
  We present results of a qualitative study of algorithmic decision-making, comprised of five workshops conducted with a total of 60 participants. We discuss participants' consideration of humanity in decision-making, and introduce the concept of 'negotiability,' the ability to go beyond formal criteria and work flexibly around the system.
  arXiv  Detail & Related papers  (2020-12-01T22:36:54Z)
• Contestable Black Boxes [10.552465253379134]
  This paper investigates the type of assurances that are needed in the contesting process when algorithmic black-boxes are involved. We argue that specialised complementary methodologies to evaluate automated decision-making in the case of a particular decision being contested need to be developed.
  arXiv  Detail & Related papers  (2020-06-09T09:09:00Z)
• Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding [33.58862183373374]
  We assess robustness of OPE methods under unobserved confounding. We show that even small amounts of per-decision confounding can heavily bias OPE methods. We propose an efficient loss-minimization-based procedure for computing worst-case bounds.
  arXiv  Detail & Related papers  (2020-03-12T05:20:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.