Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Structural Graph Matching Using the EM Algorithm and Singular Value Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
The role of transparency in recommender systems
CHI '02 Extended Abstracts on Human Factors in Computing Systems
Interactive Case-Based Reasoning in Sequential Diagnosis
Applied Intelligence
Explanations in Knowledge Systems: Design for Explainable Expert Systems
IEEE Expert: Intelligent Systems and Their Applications
A covenant with transparency: opening the black box of models
Communications of the ACM - Adaptive complex enterprises
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Journal of Management Information Systems
Effective explanations of recommendations: user-centered design
Proceedings of the 2007 ACM conference on Recommender systems
Toward establishing trust in adaptive agents
Proceedings of the 13th international conference on Intelligent user interfaces
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Ensuring transparency in computational modeling
Communications of the ACM - Being Human in the Digital Age
Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs
Journal of Management Information Systems
Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
An evaluation of the usefulness of case-based explanation
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Nimble cybersecurity incident management through visualization and defensible recommendations
Proceedings of the Seventh International Symposium on Visualization for Cyber Security
Are explanations always important?: a study of deployed, low-cost intelligent interactive systems
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Intelligent pairing assistant for air operation centers
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
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Research on intelligent systems has emphasized the benefits of providing explanations along with recommendations. But can explanations lead users to make incorrect decisions? We explored this question in a controlled experimental study with 18 professional network security analysts doing an incident classification task using a prototype cybersecurity system. The system provided three recommendations on each trial. The recommendations were displayed with explanations (called "justifications") or without. On half the trials, one of the recommendations was correct; in the other half none of the recommendations was correct. Users were more accurate with correct recommendations. Although there was no benefit overall of explanation, we found that a segment of the analysts were more accurate with explanations when a correct choice was available but were less accurate with explanations in the absence of a correct choice. We discuss implications of these results for the design of intelligent systems.