May I Ask a Follow-up Question? Understanding the Benefits of Conversations in Neural Network Explainability

Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-form conversations can enhance users' comprehension of static explanations, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration. Participants are presented with static explanations, followed by a conversation with a human expert regarding the explanations. We measure the effect of the conversation on participants' ability to choose, from three machine learning models, the most accurate one based on explanations and their self-reported comprehension, acceptance, and trust. Empirical results show that conversations significantly improve comprehension, acceptance, trust, and collaboration. Our findings highlight the importance of customized model explanations in the format of free-form conversations and provide insights for the future design of conversational explanations

Performance of a Large-area GEM Detector Read Out with Wide Radial Zigzag Strips

A 1-meter-long trapezoidal Triple-GEM detector with wide readout strips was tested in hadron beams at the Fermilab Test Beam Facility in October 2013. The readout strips have a special zigzag geometry and run along the radial direction with an azimuthal pitch of 1.37 mrad to measure the azimuthal phi-coordinate of incident particles. The zigzag geometry of the readout reduces the required number of electronic channels by a factor of three compared to conventional straight readout strips while preserving good angular resolution. The average crosstalk between zigzag strips is measured to be an acceptable 5.5%. The detection efficiency of the detector is (98.4+-0.2)%. When the non-linearity of the zigzag-strip response is corrected with track information, the angular resolution is measured to be (193+-3) urad, which corresponds to 14% of the angular strip pitch. Multiple Coulomb scattering effects are fully taken into account in the data analysis with the help of a stand-alone Geant4 simulation that estimates interpolated track errors.Comment: 30 pages, 28 figures, submitted to NIM

Look Who's Talking Now: Implications of AV's Explanations on Driver's Trust, AV Preference, Anxiety and Mental Workload

Explanations given by automation are often used to promote automation adoption. However, it remains unclear whether explanations promote acceptance of automated vehicles (AVs). In this study, we conducted a within-subject experiment in a driving simulator with 32 participants, using four different conditions. The four conditions included: (1) no explanation, (2) explanation given before or (3) after the AV acted and (4) the option for the driver to approve or disapprove the AV's action after hearing the explanation. We examined four AV outcomes: trust, preference for AV, anxiety and mental workload. Results suggest that explanations provided before an AV acted were associated with higher trust in and preference for the AV, but there was no difference in anxiety and workload. These results have important implications for the adoption of AVs.Comment: 42 pages, 5 figures, 3 Table

Expectations and Trust in Automated Vehicles

A lack of trust is a major barrier to the adoptions of Automated Vehicles (AVs). Given the ties between expectation and trust, this study employs the expectation-confirmation theory to investigate in trust in AVs. An online survey was used to collect data including expectation, perceived performance, and trust in AVs from 443 participants which represent U.S. driver population. Using the polynomial regression and response surface methodology, we found that higher trust is engendered when perceived performance is higher than expectation, and perceived risk can moderate the relationship between expectation confirmation and trust in AVs. Results have important theoretical and practical implicationsUniversity of Michigan McityPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153795/1/Zhang et al. 2020.pdfDescription of Zhang et al. 2020.pdf : Main fil