Information on the psychological mechanisms behind people’s perceptions of robot judges in the courtroom is limited. We aimed to determine whether perceptions of empathy increase people’s trust in robot judges and whether this trust influences people’s evaluation of judgments by a robot judge and their acceptance of such judges in the courtroom. We conducted a web-based randomized experiment on December 27, 2022 with 738 Japanese participants aged 18 years or older. Participants viewed one of four short clips and completed a questionnaire. Data from 531 individuals were included in the analysis. Results showed that perception of the judge’s empathy increased trust in that judge and impacted the judgment evaluation. Overall, participants perceived the human judge as significantly more empathetic than the robot judge. A perception of empathy from the robot judge was associated with a higher rate of accepting a robot judge in the courtroom via trust in that judge.

Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the single-domain data only. In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for LAtent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results. The model enriches the causal representation for multi-domain data. We propose an integrated two-phase algorithm to estimate the model. In particular, we first locate the latent factors and estimate the factor loading matrix. Then to uncover the causal structure among shared latent factors of interest, we derive a score function based on the characterization of independence relations between external influences and the dependence relations between multi-domain latent factors and latent factors of interest. We show that the proposed method provides locally consistent estimators. Experimental results on both synthetic and real-world data demonstrate the efficacy and robustness of our approach.

In this paper, we study the theoretical properties of clustering based on the kernel normalized cut. Our first contribution is to derive a nonasymptotic upper bound on the expected distortion rate of the kernel normalized cut. From this result, we show that the solution of the kernel normalized cut converges to that of the population-level weighted k-means clustering on a certain reproducing kernel Hilbert space (RKHS). Our second contribution is the discover of the interesting fact that the population-level weighted k-means clustering in the RKHS is equivalent to the population-level normalized cut. Combining these results, we can see that the kernel normalized cut converges to the population-level normalized cut. The criterion of the population-level normalized cut can be considered as an indivisibility of the population distribution, and this criterion plays an important role in the theoretical analysis of spectral clustering in Schiebinger et al. (2015). We believe that our results will provide deep insights into the behavior of both normalized cut and spectral clustering.