K Morrison: Researcher — Prolific Citations Library

Explore 2 peer-reviewed studies by K Morrison in Human-AI Collaboration and Explainable AI (XAI) (2025). Discover research powered by Prolific's participant panel.

This page lists 2 peer-reviewed papers authored or co-authored by K Morrison in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Don't Be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration

Authors: P Spitzer, J Holstein, K Morrison

Year: 2025

Published in: ... Journal of Human ..., 2025 - Taylor & Francis

Institution: Karlsruhe Institute of Technology, Carnegie Mellon University, University of Bayreuth

Research Area: Human-AI Collaboration, Explainable AI (XAI)

Discipline: Human-Computer Interaction (HCI)

Incorrect explanations in AI-assisted decision-making lead to a misinformation effect, negatively impacting human reasoning, procedural knowledge, and collaboration performance.

Methods: A study on human-AI collaboration involving AI-supported decision-making paired with explainable AI (XAI) to assess the effects of incorrect explanations.

Key Findings: Impact of incorrect explanations on human reasoning strategies, procedural knowledge, and team performance in human-AI collaboration.

Citations: 13

Sample Size: 160
Imperfections of XAI: phenomena influencing AI-assisted decision-making

Authors: P Spitzer, K Morrison, V Turri, M Feng, A Perer

Year: 2025

Published in: ACM Transactions on ..., 2025 - dl.acm.org

Institution: Carnegie Mellon University, Karlsruhe Institute of Technology, University of Bayreuth

Research Area: Explainable AI (XAI), AI-Assisted Decision-Making, Human-AI Collaboration

Discipline: Artificial Intelligence

The study highlights how imperfect explainable AI (XAI), along with human cognitive styles, affects reliance on AI and the performance of human–AI teams, providing design guidelines for better collaboration systems.

Methods: The researchers conducted a study with 136 participants, analyzing the effects of explanation imperfections and cognitive styles on AI-assisted decision-making and human–AI collaboration.

Key Findings: The impact of incorrect explanations and explanation modalities on human reliance, decision-making, and human–AI team performance, as well as the role of cognitive styles.

Citations: 2

Sample Size: 136