A Survey of AI Reliance

• URL: http://arxiv.org/abs/2408.03948v2
• Date: Sat, 30 Aug 2025 14:03:45 GMT
• Title: A Survey of AI Reliance
• Authors: Sven Eckhardt, Niklas Kühl, Mateusz Dolata, Gerhard Schwabe,
• Abstract summary: This survey presents a novel, comprehensive sociotechnical perspective on AI reliance.<n>The survey introduces a categorization framework resulting in a morphological box, which guides rigorous AI reliance research.
• Score: 10.930678550455568
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although artificial intelligence (AI) systems are becoming increasingly indispensable, research into how humans rely on these systems (AI reliance) is lagging behind. To advance this research, this survey presents a novel, comprehensive sociotechnical perspective on AI reliance, essential to fully understand the phenomenon. To address these challenges, the survey introduces a categorization framework resulting in a morphological box, which guides rigorous AI reliance research. Further, the survey identifies the core influences on AI reliance within the components of a sociotechnical system and discusses current limitations alongside emerging future research avenues to form a research agenda.

Related papers

• ResearcherBench: Evaluating Deep AI Research Systems on the Frontiers of Scientific Inquiry [22.615102398311432]
  We introduce ResearcherBench, the first benchmark focused on evaluating the capabilities of deep AI research systems.<n>We compiled a dataset of 65 research questions expertly selected from real-world scientific scenarios.<n>OpenAI Deep Research and Gemini Deep Research significantly outperform other systems, with particular strength in open-ended consulting questions.
  arXiv  Detail & Related papers  (2025-07-22T06:51:26Z)
• AI4Research: A Survey of Artificial Intelligence for Scientific Research [55.5452803680643]
  We present a comprehensive survey on AI for Research (AI4Research)<n>We first introduce a systematic taxonomy to classify five mainstream tasks in AI4Research.<n>We identify key research gaps and highlight promising future directions.
  arXiv  Detail & Related papers  (2025-07-02T17:19:20Z)
• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)
• Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences [50.9036832382286]
  We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
  arXiv  Detail & Related papers  (2025-05-22T12:52:34Z)
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research [58.944125758758936]
  The Science of Science (SoS) explores the mechanisms underlying scientific discovery.<n>The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS.<n>We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them.
  arXiv  Detail & Related papers  (2025-05-17T15:01:33Z)
• Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions [0.0]
  Agentic AI systems are capable of reasoning, planning, and autonomous decision-making.<n>They are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results.
  arXiv  Detail & Related papers  (2025-03-12T01:00:05Z)
• Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
  We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Rolling in the deep of cognitive and AI biases [1.556153237434314]
  We argue that there is urgent need to understand AI as a sociotechnical system, inseparable from the conditions in which it is designed, developed and deployed. We address this critical issue by following a radical new methodology under which human cognitive biases become core entities in our AI fairness overview. We introduce a new mapping, which justifies the humans to AI biases and we detect relevant fairness intensities and inter-dependencies.
  arXiv  Detail & Related papers  (2024-07-30T21:34:04Z)
• Trust in AI: Progress, Challenges, and Future Directions [6.724854390957174]
  The increasing use of artificial intelligence (AI) systems in our daily life explains the significance of trust/distrust in AI from a user perspective. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion.
  arXiv  Detail & Related papers  (2024-03-12T20:26:49Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape [5.852005817069381]
  The study critically examined the current state and future trajectory of generative Artificial Intelligence (AI) It explored how innovations like Google's Gemini and the anticipated OpenAI Q* project are reshaping research priorities and applications across various domains. The study highlighted the importance of incorporating ethical and human-centric methods in AI development, ensuring alignment with societal norms and welfare.
  arXiv  Detail & Related papers  (2023-12-18T01:11:39Z)
• Analyzing Character and Consciousness in AI-Generated Social Content: A Case Study of Chirper, the AI Social Network [0.0]
  The study embarks on a comprehensive exploration of AI behavior, analyzing the effects of diverse settings on Chirper's responses. Through a series of cognitive tests, the study gauges the self-awareness and pattern recognition prowess of Chirpers. An intriguing aspect of the research is the exploration of the potential influence of a Chirper's handle or personality type on its performance.
  arXiv  Detail & Related papers  (2023-08-30T15:40:18Z)
• AI empowering research: 10 ways how science can benefit from AI [0.0]
  This article explores the transformative impact of artificial intelligence (AI) on scientific research. It highlights ten ways in which AI is revolutionizing the work of scientists, including powerful referencing tools, improved understanding of research problems, enhanced research question generation, optimized research design, stub data generation, data transformation, advanced data analysis, and AI-assisted reporting.
  arXiv  Detail & Related papers  (2023-07-17T18:41:18Z)
• Enhancing Artificial intelligence Policies with Fusion and Forecasting: Insights from Indian Patents Using Network Analysis [0.0]
  This paper presents a study of the interconnectivity and interdependence of various Artificial intelligence (AI) technologies. By analyzing the technologies through different time windows and quantifying their importance, we have revealed important insights into the crucial components shaping the AI landscape.
  arXiv  Detail & Related papers  (2023-04-20T18:37:11Z)
• 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)
• The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies [97.5153823429076]
  The benefits, challenges and drawbacks of AI in this field are reviewed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
  arXiv  Detail & Related papers  (2022-12-08T23:23:39Z)
• Characterising Research Areas in the field of AI [68.8204255655161]
  We identified the main conceptual themes by performing clustering analysis on the co-occurrence network of topics. The results highlight the growing academic interest in research themes like deep learning, machine learning, and internet of things.
  arXiv  Detail & Related papers  (2022-05-26T16:30:30Z)
• On the Influence of Explainable AI on Automation Bias [0.0]
  We aim to shed light on the potential to influence automation bias by explainable AI (XAI) We conduct an online experiment with regard to hotel review classifications and discuss first results.
  arXiv  Detail & Related papers  (2022-04-19T12:54:23Z)
• Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making [0.0]
  We aim to enable humans not to rely on AI advice blindly but rather to distinguish its quality and act upon it to make better decisions. Current research lacks a metric for appropriate reliance (AR) on AI advice on a case-by-case basis. We propose to view AR as a two-dimensional construct that measures the ability to discriminate advice quality and behave accordingly.
  arXiv  Detail & Related papers  (2022-04-14T12:18:51Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)
• 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.