Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide

• URL: http://arxiv.org/abs/2503.07690v1
• Date: Mon, 10 Mar 2025 16:33:15 GMT
• Title: Artificial Intelligence in Deliberation: The AI Penalty and the Emergence of a New Deliberative Divide
• Authors: Andreas Jungherr, Adrian Rauchfleisch,
• Abstract summary: Digital deliberation has expanded democratic participation, yet challenges remain.<n>Recent advances in artificial intelligence (AI) offer potential solutions, but public perceptions of AI's role in deliberation remain underexplored.<n>If AI is integrated into deliberation, public trust, acceptance, and willingness to participate may be affected.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Digital deliberation has expanded democratic participation, yet challenges remain. This includes processing information at scale, moderating discussions, fact-checking, or attracting people to participate. Recent advances in artificial intelligence (AI) offer potential solutions, but public perceptions of AI's role in deliberation remain underexplored. Beyond efficiency, democratic deliberation is about voice and recognition. If AI is integrated into deliberation, public trust, acceptance, and willingness to participate may be affected. We conducted a preregistered survey experiment with a representative sample in Germany (n=1850) to examine how information about AI-enabled deliberation influences willingness to participate and perceptions of deliberative quality. Respondents were randomly assigned to treatments that provided them information about deliberative tasks facilitated by either AI or humans. Our findings reveal a significant AI-penalty. Participants were less willing to engage in AI-facilitated deliberation and rated its quality lower than human-led formats. These effects were moderated by individual predispositions. Perceptions of AI's societal benefits and anthropomorphization of AI showed positive interaction effects on people's interest to participate in AI-enabled deliberative formats and positive quality assessments, while AI risk assessments showed negative interactions with information about AI-enabled deliberation. These results suggest AI-enabled deliberation faces substantial public skepticism, potentially even introducing a new deliberative divide. Unlike traditional participation gaps based on education or demographics, this divide is shaped by attitudes toward AI. As democratic engagement increasingly moves online, ensuring AI's role in deliberation does not discourage participation or deepen inequalities will be a key challenge for future research and policy.

Related papers

• 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)
• Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making [47.33241893184721]
  In AI-assisted decision-making, humans often passively review AI's suggestion and decide whether to accept or reject it as a whole. We propose Human-AI Deliberation, a novel framework to promote human reflection and discussion on conflicting human-AI opinions in decision-making. Based on theories in human deliberation, this framework engages humans and AI in dimension-level opinion elicitation, deliberative discussion, and decision updates.
  arXiv  Detail & Related papers  (2024-03-25T14:34:06Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education [0.0]
  The study investigates the acceptability of different AI applications in education from a multi-stakeholder perspective. It addresses concerns related to data privacy, AI agency, transparency, explainability and the ethical deployment of AI.
  arXiv  Detail & Related papers  (2024-02-22T23:59:59Z)
• Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge [22.21959942886099]
  We introduce a process-oriented notion of appropriate reliance called critical use that centers the human's ability to situate AI predictions against knowledge that is uniquely available to them but unavailable to the AI model. We conduct a randomized online experiment in a complex social decision-making setting: child maltreatment screening. We find that, by providing participants with accelerated, low-stakes opportunities to practice AI-assisted decision-making, novices came to exhibit patterns of disagreement with AI that resemble those of experienced workers.
  arXiv  Detail & Related papers  (2023-08-30T01:54:31Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making [25.913473823070863]
  Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers. This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays.
  arXiv  Detail & Related papers  (2023-02-17T11:07:17Z)
• FATE in AI: Towards Algorithmic Inclusivity and Accessibility [0.0]
  To prevent algorithmic disparities, fairness, accountability, transparency, and ethics (FATE) in AI are being implemented. This study examines FATE-related desiderata, particularly transparency and ethics, in areas of the global South that are underserved by AI. To promote inclusivity, a community-led strategy is proposed to collect and curate representative data for responsible AI design.
  arXiv  Detail & Related papers  (2023-01-03T15:08:10Z)
• Trustworthy AI: A Computational Perspective [54.80482955088197]
  We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
  arXiv  Detail & Related papers  (2021-07-12T14:21:46Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)

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.