Can You Trust the Black Box? The Effect of Personality Traits on Trust in AI-Enabled User Interfaces

Machine learning algorithms for link prediction can be valuable tools for hypothesis generation. However, many current algorithms are black boxes or lack good user interfaces that could facilitate insight into why predictions are made. We present LinkExplorer, a software suite for predicting, explaining and exploring links in large biomedical knowledge graphs. LinkExplorer integrates our novel, rule-based link prediction engine SAFRAN, which was recently shown to outcompete other explainable algorithms and established black box algorithms. Here, we demonstrate highly competitive evaluation results of our algorithm on multiple large biomedical knowledge graphs, and release a web interface that allows for interactive and intuitive exploration of predicted links and their explanations.

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Towards Explainable Artificial Intelligence and Explanation User Interfaces to Open the ‘Black Box’ of Automated ECG Interpretation

Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-030-68007-7_6 ◽

2021 ◽

pp. 96-108

Author(s):

Khaled Rjoob ◽

Raymond Bond ◽

Dewar Finlay ◽

Victoria McGilligan ◽

Stephen J. Leslie ◽

...

Keyword(s):

Artificial Intelligence ◽

User Interfaces ◽

Black Box ◽

Explainable Artificial Intelligence ◽

Ecg Interpretation

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What’s Driving Me? Exploration and Validation of a Hierarchical Personality Model for Trust in Automated Driving

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720820922653 ◽

2020 ◽

pp. 001872082092265

Author(s):

Johannes Kraus ◽

David Scholz ◽

Martin Baumann

Keyword(s):

Personality Traits ◽

User Interfaces ◽

Structural Equation ◽

Interpersonal Trust ◽

A Priori ◽

Self Esteem ◽

Equation Modeling ◽

Automated Driving ◽

Final Model ◽

Trust In Automation

Objective This paper presents a comprehensive investigation of personality traits related to trust in automated vehicles. A hierarchical personality model based on Mowen’s (2000) 3M model is explored in a first and replicated in a second study. Background Trust in automation is established in a complex psychological process involving user-, system- and situation-related variables. In this process, personality traits have been viewed as an important source of variance. Method Dispositional variables on three levels were included in an exploratory, hierarchical personality model (full model) of dynamic learned trust in automation, which was refined on the basis of structural equation modeling carried out in Study 1 (final model). Study 2 replicated the final model in an independent sample. Results In both studies, the personality model showed a good fit and explained a large proportion of variance in trust in automation. The combined evidence supports the role of extraversion, neuroticism, and self-esteem at the elemental level; affinity for technology and dispositional interpersonal trust at the situational level; and propensity to trust in automation and a priori acceptability of automated driving at the surface level in the prediction of trust in automation. Conclusion Findings confirm that personality plays a substantial role in trust formation and provide evidence of the involvement of user dispositions not previously investigated in relation to trust in automation: self-esteem, dispositional interpersonal trust, and affinity for technology. Application Implications for personalization of information campaigns, driver training, and user interfaces for trust calibration in automated driving are discussed.

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