Research Library
All Research Papers
24 papers across 8 topics, curated from leading journals in psychology, HCI, and AI ethics.
Algorithm Aversion: People Erroneously Avoid Algorithms After Seeing Them Err
A landmark study demonstrating that people are more likely to abandon algorithmic forecasters after witnessing them make errors, even when the algorithm consistently outperforms hu...
Understanding the Effect of Accuracy on Trust in Machine Learning Models
This study investigates how stated and observed accuracy levels influence human trust in machine learning models. The findings reveal that trust is not a simple linear function of ...
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
A theoretical framework that formalizes the concept of trust in AI by analyzing its prerequisites, causes, and goals. The paper distinguishes between contractual trust (based on ex...
The Mind in the Machine: Anthropomorphism Increases Trust in an Autonomous Vehicle
Demonstrates that anthropomorphizing autonomous vehicles — giving them names, voices, and described intentions — increases trust and willingness to rely on them. This suggests that...
Machines and Mindlessness: Social Responses to Computers
A foundational paper demonstrating that humans apply social rules and expectations to computers, even when they know the machines are not human. People are polite to computers, app...
On Seeing Human: A Three-Factor Theory of Anthropomorphism
Proposes a comprehensive theory explaining when and why people anthropomorphize. Three factors drive the tendency: elicited agent knowledge (using human schemas as defaults), effec...
"It's Reducing a Human Being to a Percentage": Perceptions of Justice in Algorithmic Decisions
Examines how people perceive fairness when algorithms make consequential decisions about them. Participants expressed deep discomfort with being "reduced to a number," revealing ps...
Fear of Autonomous Robots and Artificial Intelligence: Evidence from National Representative Data with Probability Sampling
Using nationally representative survey data, this study maps the prevalence and predictors of fear toward autonomous robots and AI. The findings reveal that AI anxiety is widesprea...
AI Anxiety
A conceptual analysis of AI anxiety as a societal phenomenon. The authors argue that much of the fear surrounding AI is based on misunderstandings of how AI works, inflated by medi...
Algorithm Appreciation: People Prefer Algorithmic to Human Judgment
Challenging the algorithm aversion narrative, this study shows that in many contexts people actually prefer algorithmic advice over human judgment. The preference reverses only whe...
The Principles and Limits of Algorithm-in-the-Loop Decision Making
Investigates what happens when humans use AI as a tool rather than a replacement. The study finds that algorithm-in-the-loop decision making does not automatically improve outcomes...
Making Sense of Recommendations
Studies how people interpret and use algorithmic recommendations for interpersonal predictions. The research shows that algorithms outperform human intuition at predicting others' ...
Putting the Art in Artificial: Aesthetic Responses to Computer-Generated Art
A systematic study of how people perceive and evaluate art created by computers compared to art created by humans. The findings reveal that authorship information significantly mod...
Artificial Intelligence, Artists, and Art: Attitudes Toward Artwork Produced by Humans vs. Artificial Intelligence
Examines how disclosure of AI involvement in art creation affects perceived value, creativity, and emotional depth. The study reveals a robust "human premium" in art evaluation tha...
Computational Creativity: The Final Frontier?
A position paper arguing that computational creativity represents the ultimate challenge for AI — and that public perception of creative AI is shaped more by psychological biases a...
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Tests whether AI explanations help human-AI teams exceed the performance of either alone. Surprisingly, explanations that increase understanding do not always improve team performa...
On Human Predictions with Explanations and Predictions of Machine Learning Models
Examines how different types of AI explanations affect human prediction accuracy. The study finds that not all explanations are created equal — some improve human performance while...
Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems
Reveals that domain experts may be worse at collaborating with AI than novices, because their confidence in their own knowledge leads them to dismiss correct AI recommendations. Th...
Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature
A comprehensive review of how people perceive fairness in algorithmic decisions across different domains. The review reveals that fairness perceptions are highly context-dependent ...
Understanding Perception of Algorithmic Decisions: Fairness, Trust, and Emotion in Response to Algorithmic Management
Studies how workers perceive algorithmic management compared to human management. Workers view algorithms as more fair for mechanical tasks but less fair for tasks requiring human ...
The Moral Machine Experiment
The largest study of moral preferences in AI decision-making, gathering 40 million decisions from people in 233 countries on how autonomous vehicles should resolve ethical dilemmas...
Alone Together: Why We Expect More from Technology and Less from Each Other
Sherry Turkle's landmark work documenting how digital companions and social robots reshape human expectations of intimacy and connection. Through extensive interviews and observati...
Affective Computing
The foundational text that launched the field of affective computing, arguing that emotions are not opposed to rational thought but essential to it — and that for AI to be truly in...
The Uncanny Valley of Mind: An Empirical Investigation of the Uncanny Valley of Mind Hypothesis
Tests whether there is a psychological "uncanny valley" for AI minds — a point where machines that seem almost-but-not-quite human in their mental abilities provoke discomfort. The...