Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI

• URL: http://arxiv.org/abs/2412.01459v2
• Date: Tue, 26 Aug 2025 10:08:51 GMT
• Title: Perception Gaps in Risk, Benefit, and Value Between Experts and Public Challenge Socially Accepted AI
• Authors: Philipp Brauner, Felix Glawe, Gian Luca Liehner, Luisa Vervier, Martina Ziefle,
• Abstract summary: This study examines how the general public and academic AI experts perceive AI's capabilities and impact.<n>The scenarios span domains such as sustainability, healthcare, job performance, societal inequality, art, and warfare.<n>Experts consistently anticipate higher probabilities, perceive lower risks, report greater benefits, and express more positive sentiment toward AI compared to the non-experts.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial Intelligence (AI) is reshaping many societal domains, raising critical questions about its risks, benefits, and the potential misalignment between public and academic perspectives. This study examines how the general public (N=1110) -- individuals who interact with or are impacted by AI technologies -- and academic AI experts (N=119) -- those elites shaping AI development -- perceive AI's capabilities and impact across 71 scenarios. These scenarios span domains such as sustainability, healthcare, job performance, societal inequality, art, and warfare. Participants evaluated these scenarios across four dimensions using the psychometric model: likelihood, perceived risk and benefit, and overall value (or sentiment). The results suggest significant differences: experts consistently anticipate higher probabilities, perceive lower risks, report greater benefits, and express more positive sentiment toward AI compared to the non-experts. Moreover, both groups apply different weighting schemes: experts discount risk more heavily relative to benefit than non-experts. Visual mappings of these evaluations uncover areas convergent evaluations (e.g., AI performing medical diagnoses or criminal use) as well as tension points (e.g., decision of legal cases, political decision making), highlighting areas where communication and policy interventions may be needed. These findings underscore a critical translational challenge: if AI research and deployment are to align with societal priorities, the perception gap between developers and the public must be better understood and addressed. Our results provide an empirical foundation for value-sensitive AI governance and trust-building strategies across stakeholder groups.

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)
• Fully Autonomous AI Agents Should Not be Developed [58.88624302082713]
  This paper argues that fully autonomous AI agents should not be developed. In support of this position, we build from prior scientific literature and current product marketing to delineate different AI agent levels. Our analysis reveals that risks to people increase with the autonomy of a system.
  arXiv  Detail & Related papers  (2025-02-04T19:00:06Z)
• Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s) [0.0]
  Authors argue that ethical concerns about AI deployment vary significantly based on implementation context and specific use cases. They propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing.
  arXiv  Detail & Related papers  (2025-01-20T19:38:21Z)
• AI Perceptions Across Cultures: Similarities and Differences in Expectations, Risks, Benefits, Tradeoffs, and Value in Germany and China [0.20971479389679332]
  This study explores public mental models of AI using micro scenarios to assess reactions to 71 statements about AI's potential future impacts. German participants tended toward more cautious assessments, whereas Chinese participants expressed greater optimism regarding AI's societal benefits.
  arXiv  Detail & Related papers  (2024-12-18T13:34:44Z)
• Mapping Public Perception of Artificial Intelligence: Expectations, Risk-Benefit Tradeoffs, and Value As Determinants for Societal Acceptance [0.20971479389679332]
  Using a representative sample of 1100 participants from Germany, this study examines mental models of AI.<n>Participants quantitatively evaluated 71 statements about AI's future capabilities.<n>We present rankings of these projections alongside visual mappings illustrating public risk-benefit tradeoffs.
  arXiv  Detail & Related papers  (2024-11-28T20:03:01Z)
• Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems [2.444630714797783]
  We review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias. We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making.
  arXiv  Detail & Related papers  (2024-08-28T06:04:25Z)
• Implications for Governance in Public Perceptions of Societal-scale AI Risks [0.29022435221103454]
  Voters perceive AI risks as both more likely and more impactful than experts, and also advocate for slower AI development. Policy interventions may best assuage collective concerns if they attempt to more carefully balance mitigation efforts across all classes of societal-scale risks.
  arXiv  Detail & Related papers  (2024-06-10T11:52:25Z)
• The Narrow Depth and Breadth of Corporate Responsible AI Research [3.364518262921329]
  We show that the majority of AI firms show limited or no engagement in this critical subfield of AI. Leading AI firms exhibit significantly lower output in responsible AI research compared to their conventional AI research. Our results highlight the urgent need for industry to publicly engage in responsible AI research.
  arXiv  Detail & Related papers  (2024-05-20T17:26:43Z)
• Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
  Next several decades may well be a turning point for humanity, comparable to the industrial revolution. Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts. We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
  arXiv  Detail & Related papers  (2024-04-06T22:18:31Z)
• 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)
• 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)
• How Different Groups Prioritize Ethical Values for Responsible AI [75.40051547428592]
  Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible AI technologies. While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies they interact with and might be affected by. We conducted a survey examining how individuals perceive and prioritize responsible AI values across three groups.
  arXiv  Detail & Related papers  (2022-05-16T14:39:37Z)
• 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)
• Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making [53.62514158534574]
  We study whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. We show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making.
  arXiv  Detail & Related papers  (2020-01-07T15:33:48Z)

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.