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Recommender systems, while a powerful decision making tool, are often operationalized as black box models, such that their AI algorithms are not accessible or interpretable by human operators. This in turn can cause confusion and frustration for the operator and result in unsatisfactory outcomes. While the field of explainable AI has made remarkable strides in addressing this challenge by focusing on interpreting and explaining the algorithms to human operators, there are remaining gaps in the human's understanding of the recommender system. This paper investigates the relative impact of using context, properties of the decision making task and environment, to align human and AI algorithm understanding of the state of the world, i.e. judgment, to improve joint human-recommender performance as compared to utilizing post-hoc algorithmic explanations. We conducted an empirical, between-subjects experiment in which participants were asked to work with an automated recommender system to complete a decision making task. We manipulated the method of transparency (shared contextual information to support shared judgment vs algorithmic explanations) and record the human's understanding of the task, the recommender system, and their overall performance. We found that both techniques yielded equivalent agreement on final decisions. However, those who saw task context had less tendency to over-rely on the recommender system and were able to better pinpoint in what conditions the AI erred. Both methods improved participants' confidence in their own decision making, and increased mental demand equally and frustration negligibly. These results present an alternative approach to improving team performance to post-hoc explanations and illustrate the impact of judgment on human cognition in working with recommender systems., Comment: arXiv admin note: text overlap with arXiv:2310.11370

AI recommender systems are sought for decision support by providing suggestions to operators responsible for making final decisions. However, these systems are typically considered black boxes, and are often presented without any context or insight into the underlying algorithm. As a result, recommender systems can lead to miscalibrated user reliance and decreased situation awareness. Recent work has focused on improving the transparency of recommender systems in various ways such as improving the recommender's analysis and visualization of the figures of merit, providing explanations for the recommender's decision, as well as improving user training or calibrating user trust. In this paper, we introduce an alternative transparency technique of structuring the order in which contextual information and the recommender's decision are shown to the human operator. This technique is designed to improve the operator's situation awareness and therefore the shared situation awareness between the operator and the recommender system. This paper presents the results of a two-phase between-subjects study in which participants and a recommender system jointly make a high-stakes decision. We varied the amount of contextual information the participant had, the assessment technique of the figures of merit, and the reliability of the recommender system. We found that providing contextual information upfront improves the team's shared situation awareness by improving the human decision maker's initial and final judgment, as well as their ability to discern the recommender's error boundary. Additionally, this technique accurately calibrated the human operator's trust in the recommender. This work proposes and validates a way to provide model-agnostic transparency into AI systems that can support the human decision maker and lead to improved team performance., Comment: 26 pages, 12 figures, submitted to Springer's "Cognition, Technology, and Work" journal

Half-metallic ferromagnets show 100% spin-polarization at the Fermi level and are ideal candidates for spintronic applications. Despite the extensive research in the field, very few materials have been discovered so far. Here we present results of electronic band structure calculations based on density functional theory and extensive physical-property measurements for Fe3Se4 revealing signatures of half-metallicity. The spin-polarized electronic band structure calculations predict half-metallic ferrimagnetism for Fe3Se4. The electrical resistivity follows exponentially suppressed electron-magnon scattering mechanism in the low-temperature regime and show a magnetoresistance effect that changes the sign from negative to positive with decreasing temperature around 100 K. Other intriguing observations include the anomalous behavior of Hall resistance below 100 K and an anomalous Hall coefficient that roughly follows the \r{ho}2 behavior., Comment: Accepted

We have investigated the structural properties, vibrational spectra, and electronic band structures of crystalline cellulose allomorphs and chemically modified cellulose with quantum chemical methods. The electronic band gaps of cellulose allomorphs I$_\alpha$, I$_\beta$, II, and III$_1$ lie in the range of 5.0 to 5.6 eV. We show that extra states can be created in the band gap of cellulose by chemical modification. Experimentally feasible amidation of cellulose I$_\beta$ with aniline or 4,4'- diaminoazobenzene creates narrow bands in the cellulose band gap, reducing the difference between the occupied and empty states to 4.0 or 1.8 eV, respectively. The predicted states 4,4'-diaminoazobenzene-modified cellulose I$_\beta$ fall in the visible spectrum, suggesting uses in optical applications., Comment: 15 pages + 11 pages of supporting information

We report a detailed magnetotransport study on single crystals of PrBi. The presence of $f$-electrons in this material raises the prospect of realizing a strongly correlated version of topological semimetals. PrBi shows a magnetic field induced metal insulator transition below $T \sim 20$ K and a very large magnetoresistance ($\approx 4.4 \times 10^4~$) at low temperatures ($T= 2$ K). We have also probed the Fermi surface topology by de Haas van Alphen (dHvA) and Shubnikov de Haas (SdH) quantum oscillation measurements complimented with density functional theory (DFT) calculations of the band structure and the Fermi surface. Angle dependence of the SdH oscillations have been carried out to probe the possible signature of surface Dirac fermions. We find three frequencies corresponding to one electron ($\alpha$) and two hole ($\beta$ and $\gamma$) pockets in experiments, consistent with DFT calculations. The angular dependence of these frequencies is not consistent with a two dimensional Fermi surface suggesting that the transport is dominated by bulk bands. Although the transport properties of this material originate from the bulk bands, the high mobility and small effective mass are comparable to other compounds in this series proposed as topologically nontrivial., Comment: 10 pages, 6 figures, 4 tables