Fetching the paper…
Reading the bibliography…
The integration of artificial intelligence (AI) into human decision-making processes at the workplace presents both opportunities and challenges.
Performance as a function of resultant achievement motivation (perceived ability) and perceived difficulty
Andy Kukla. 1974 · 1974
Earlier work this paper cites.
Self-efficacy: toward a unifying theory of behavioral change
Albert Bandura. 1977 · 1977
Earlier work this paper cites.
Controllability and human stress: Method, evidence and theory
Suzanne M Miller. 1979 · 1979
Earlier work this paper cites.
The role of self-efficacy in achieving health behavior change
Victor J Strecher, Brenda McEvoy DeVellis, Marshall H Becker, and Irwin M Rosenstock. 1986 · 1986
Earlier work this paper cites.
Cognitive load theory, learning difficulty, and instructional design
John Sweller. 1994 · 1994
Earlier work this paper cites.
Self-efficacy
Albert Bandura and Sebastian Wessels. 1997 · 1997
Earlier work this paper cites.
Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load
Melina Klepsch, Florian Schmitz, and Tina Seufert. 2017 · 1997
Earlier work this paper cites.
Delegating to software agents
Allen E Milewski and Steven H Lewis. 1997 · 1997
Earlier work this paper cites.
The Concept of Information Overload: A Review of Literature From Organization Science, Accounting, Marketing, MIS, and Related Disciplines
Martin Eppler and Jeanne Mengis. 2004 · 2004
Earlier work this paper cites.
The influence of perceived task difficulty on task performance
Dominick Scasserra. 2008 · 2008
Earlier work this paper cites.
Instructional manipulation checks: Detecting satisficing to increase statistical power
Daniel M Oppenheimer, Tom Meyvis, and Nicolas Davidenko. 2009 · 2009
Earlier work this paper cites.
Reconsidering Baron and Kenny: Myths and truths about mediation analysis
Xinshu Zhao, John G Lynch Jr, and Qimei Chen. 2010 · 2010
Earlier work this paper cites.
Self-efficacy in the workplace: Implications for motivation and performance
Fred C Lunenburg. 2011 · 2011
Earlier work this paper cites.
The time on task effect in reading and problem solving is moderated by task difficulty and skill: insights from a computer-based large-scale assessment
Frank Goldhammer, Johannes Naumann, Annette Stelter, Krisztina Tóth, Heiko Rölke, and Eckhard Klieme. 2014 · 2014
Earlier work this paper cites.
Statistical mediation analysis with a multicategorical independent variable
Andrew F Hayes and Kristopher J Preacher. 2014 · 2014
Earlier work this paper cites.
Attention by design: Using attention checks to detect inattentive respondents and improve data quality
James D Abbey and Margaret G Meloy. 2017 · 2017
Earlier work this paper cites.
Introduction to mediation, moderation, and conditional process analysis: A regression-based approach
Andrew F Hayes. 2017 · 2017
Earlier work this paper cites.
Beyond the Turk: Alternative platforms for crowdsourcing behavioral research
Eyal Peer, Laura Brandimarte, Sonam Samat, and Alessandro Acquisti. 2017 · 2017
Earlier work this paper cites.
Leaders’ behaviors matter: The role of delegation in promoting employees’ feedback-seeking behavior
Xiyang Zhang, Jing Qian, Bin Wang, Zhuyun Jin, Jiachen Wang, and Yu Wang. 2017 · 2017
Earlier work this paper cites.
Prolific.ac—A subject pool for online experiments
Stefan Palan and Christian Schitter. 2018 · 2018
Earlier work this paper cites.
Delegating decisions: Recruiting others to make choices we might regret
Mary Steffel and Elanor F Williams. 2018 · 2018
Earlier work this paper cites.
Artificial intelligence in healthcare
Kun-Hsing Yu, Andrew L. Beam, and Isaac S. Kohane. 2018 · 2018
Earlier work this paper cites.
Beyond accuracy: The role of mental models in human-AI team performance. In Proceedings of the AAAI conference on human computation and crowdsourcing , Vol. 7. 2–11
Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S Lasecki, Daniel S Weld, and Eric Horvitz. 2019 · 2019
Earlier work this paper cites.
"Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making
Carrie J. Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019 · 2019
Earlier work this paper cites.
The principles and limits of algorithm-in-the-loop decision making
Ben Green and Yiling Chen. 2019 · 2019
Earlier work this paper cites.
Algorithm appreciation: People prefer algorithmic to human judgment
Jennifer M. Logg, Julia A. Minson, and Don A. Moore. 2019 · 2019
Earlier work this paper cites.
Ask not what AI can do, but what AI should do: Towards a framework of task delegability
Brian Lubars and Chenhao Tan. 2019 · 2019
Earlier work this paper cites.
The algorithmic automation problem: Prediction, triage, and human effort
Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, and Sendhil Mullainathan. 2019 · 2019
Earlier work this paper cites.
Understanding the Effect of Accuracy on Trust in Machine Learning Models. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19) . Association for Computing Machinery, New York, NY, USA, 1–12
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach. 2019 · 2019
Cited alongside, same era.
Human-in-the-loop artificial intelligence
Fabio Massimo Zanzotto. 2019 · 2019
Cited alongside, same era.
COGAM: measuring and moderating cognitive load in machine learning model explanations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems . 1–14
Ashraf Abdul, Christian von der Weth, Mohan Kankanhalli, and Brian Y Lim. 2020 · 2020
Cited alongside, same era.
"Why is ’Chicago’ Deceptive?" Towards Building Model-Driven Tutorials for Humans. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20) . Association for Computing Machinery, New York, NY, USA, 1–13
Vivian Lai, Han Liu, and Chenhao Tan. 2020 · 2020
Cited alongside, same era.
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society . 617–626
Max Schemmer, Patrick Hemmer, Maximilian Nitsche, Niklas Kühl, and Michael Vössing. 2022a · 2022
Later among the works it cites.
On the Influence of Explainable AI on Automation Bias. In Proceedings of the 30th European Conference on Information Systems (ECIS), Timi
Max Schemmer, Niklas Kühl, Carina Benz, and Gerhard Satzger. 2022b · 2022
Later among the works it cites.
Calibrating Users’ Mental Models for Delegation to AI. In ICIS 2022 Proceedings
Anna Taudien, Andreas Fuegener, Alok Gupta, and Wolfgang Ketter. 2022a · 2022
Later among the works it cites.
The Effect of AI Advice on Human Confidence in Decision-Making
Anna Taudien, Andreas Fügener, Alok Gupta, and Wolfgang Ketter. 2022b · 2022
Later among the works it cites.
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Nikhil Agarwal, Alex Moehring, Pranav Rajpurkar, and Tobias Salz. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Consistent estimators for learning to defer to an expert. In International Conference on Machine Learning . PMLR, 7076–7087
Hussein Mozannar and David Sontag. 2020 · 2020
Cited alongside, same era.
Bryan Wilder, Eric Horvitz, and Ece Kamar. 2020 · 2020
Cited alongside, same era.
Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–16
Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021 · 2021
Cited alongside, same era.
You’d Better Stop! Understanding Human Reliance on Machine Learning Models under Covariate Shift. In Proceedings of the 13th ACM Web Science Conference 2021 (Virtual Event, United Kingdom) (WebSci ’21) . Association for Computing Machinery, New York, NY, USA, 120–129
Chun-Wei Chiang and Ming Yin. 2021 · 2021
Cited alongside, same era.
Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. 2021 · 2021
Cited alongside, same era.
The who in explainable ai: How ai background shapes perceptions of ai explanations
Upol Ehsan, Samir Passi, Q Vera Liao, Larry Chan, I Lee, Michael Muller, Mark O Riedl, et al · 2021
Cited alongside, same era.
Juliana Jansen Ferreira and Mateus Monteiro. 2021 · 2021
Cited alongside, same era.
Exploring User Heterogeneity in Human Delegation Behavior towards AI. In ICIS 2021 Proceedings
Andreas Fuegener, Joern Grahl, Alok Gupta, Wolfgang Ketter, and Anna Taudien. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
AI Shall Have No Dominion: On How to Measure Technology Dominance in AI-Supported Human Decision-Making. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 354, 20 pages
Federico Cabitza, Andrea Campagner, Riccardo Angius, Chiara Natali, and Carlo Reverberi. 2023 · 2023
Later among the works it cites.
Improving human-AI collaboration with descriptions of AI behavior
Ángel Alexander Cabrera, Adam Perer, and Jason I Hong. 2023 · 2023
Later among the works it cites.
The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age
Peter Cardon, Carolin Fleischmann, Jolanta Aritz, Minna Logemann, and Jeanette Heidewald. 2023 · 2023
Later among the works it cites.
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, and Gagan Bansal. 2023 · 2023
Later among the works it cites.
A European approach to artificial intelligence
European Commission. 2023 · 2023
Later among the works it cites.
Fairfed: Enabling group fairness in federated learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 37. 7494–7502
Yahya H Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, and A Salman Avestimehr. 2023 · 2023
Later among the works it cites.
Exploring Challenges and Opportunities to Support Designers in Learning to Co-Create with AI-Based Manufacturing Design Tools. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI ’23) . Association for Computing Machinery, New York, NY, USA, Article 226, 20 pages
Frederic Gmeiner, Humphrey Yang, Lining Yao, Kenneth Holstein, and Nikolas Martelaro. 2023 · 2023
Later among the works it cites.
Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction. In Proceedings of the 28th International Conference on Intelligent User Interfaces . 453–463
Patrick Hemmer, Monika Westphal, Max Schemmer, Sebastian Vetter, Michael Vössing, and Gerhard Satzger. 2023 · 2023
Later among the works it cites.
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study
Lukas-Valentin Herm. 2023 · 2023
Later among the works it cites.
Toward supporting perceptual complementarity in human-AI collaboration via reflection on unobservables
Kenneth Holstein, Maria De-Arteaga, Lakshmi Tumati, and Yanghuidi Cheng. 2023 · 2023
Later among the works it cites.
Training Towards Critical Use: Learning to Situate AI Predictions Relative to Human Knowledge. In Proceedings of The ACM Collective Intelligence Conference (CI ’23) . Association for Computing Machinery, New York, NY, USA, 63–78
Anna Kawakami, Luke Guerdan, Yanghuidi Cheng, Kate Glazko, Matthew Lee, Scott Carter, Nikos Arechiga, Haiyi Zhu, and Kenneth Holstein. 2023 · 2023
Later among the works it cites.
AI Knowledge: Improving AI Delegation through Human Enablement. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–17
Marc Pinski, Martin Adam, and Alexander Benlian. 2023 · 2023
Later among the works it cites.
AI Literacy-Towards Measuring Human Competency in Artificial Intelligence. In Hawaii International Conference on Systems Sciences (HICSS-56)
Marc Pinski and Alexander Benlian. 2023 · 2023
Later among the works it cites.
Four Challenges for IML Designers: Lessons of an Interactive Customer Segmentation Prototype in a Global Manufacturing Company. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA ’23) . Association for Computing Machinery, New York, NY, USA, Article 143, 6 pages
Muhammad Raees, Vassilis-Javed Khan, and Konstantinos Papangelis. 2023 · 2023
Later among the works it cites.
Max Schemmer, Joshua Holstein, Niklas Bauer, Niklas Kühl, and Gerhard Satzger. 2023 · 2023
Later among the works it cites.
On the Perception of Difficulty: Differences between Humans and AI
Philipp Spitzer, Joshua Holstein, Michael Vössing, and Niklas Kühl. 2023 · 2023
Later among the works it cites.
An extension of the theory of technology dominance: Capturing the underlying causal complexity
Steve G. Sutton, Vicky Arnold, and Matthew Holt. 2023 · 2023
Later among the works it cites.
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence
Patrick Hemmer, Max Schemmer, Niklas Kühl, Michael Vössing, and Gerhard Satzger. 2024 · 2024
Closest in time.
Understanding Data Understanding: A Framework to Navigate the Intricacies of Data Analytics
Joshua Holstein, Philipp Spitzer, Marieke Hoell, Michael Vössing, and Niklas Kühl. 2024 · 2024
Closest in time.
The Impact of Imperfect XAI on Human-AI Decision-Making
Katelyn Morrison, Philipp Spitzer, Violet Turri, Michelle Feng, Niklas Kühl, and Adam Perer. 2024 · 2024
Closest in time.
Transferring Domain Knowledge with (X) AI-Based Learning Systems
Philipp Spitzer, Niklas Kühl, Marc Goutier, Manuel Kaschura, and Gerhard Satzger. 2024 · 2024
Closest in time.
Designing AI Assistants for Novices: Bridging Knowledge Gaps in Onboarding
Joshua Holstein, Patrick Müller, Michael Vössing, and Hansjoerg Fromm. 2025 · 2025
Closest in time.