Toward an Objective Measurement of AI Literacy

Humans multitudinously interact with Artificial Intelligence (AI) as it permeates every aspect of contemporary professional and private life. The socio-technical competencies of humans, i.e., their AI literacy, shape human-AI interactions. While academia does explore AI literacy measurement, current literature exclusively approaches the topic from a subjective perspective. This study draws on a well-established scale development procedure employing ten expert interviews, two card-sorting rounds, and a between-subject comparison study with 88 participants in two groups to define, conceptualize, and empirically validate an objective measurement instrument for AI literacy. With 16 items, our developed instrument discriminates between an AI-literate test and a control group. Furthermore, the structure of our instrument allows us to distinctly assess AI literacy aspects. We contribute to IS education research by providing a new instrument and conceptualizing AI literacy, incorporating critical themes from the literature. Practitioners may employ our instrument to assess AI literacy in their organizations

The Explanation Matters: Enhancing AI Adoption in Human Resource Management

Artificial intelligence (AI) has ubiquitous applications in companies, permeating multiple business divisions like human resource management (HRM). Yet, in these high-stakes domains where transparency and interpretability of results are of utmost importance, the black-box characteristic of AI is even more of a threat to AI adoption. Hence, explainable AI (XAI), which is regular AI equipped with or complemented by techniques to explain it, comes in. We present a systematic literature review of n=62 XAI in HRM papers. Further, we conducted an experiment among a German sample (n=108) of HRM personnel regarding a turnover prediction task with or without (X)AI-support. We find that AI-support leads to better task performance, self-assessment accuracy and response characteristics toward the AI, and XAI, i.e., transparent models allow for more accurate self-assessment of one’s performance. Future studies could enhance our research by employing local explanation techniques on real-world data with a larger and international sample

Digging for Dark Matter: Spectral Analysis and Discovery Potential of Paleo-Detectors

Paleo-detectors are a recently proposed method for the direct detection of Dark Matter (DM). In such detectors, one would search for the persistent damage features left by DM--nucleus interactions in ancient minerals. Initial sensitivity projections have shown that paleo-detectors could probe much of the remaining Weakly Interacting Massive Particle (WIMP) parameter space. In this paper, we improve upon the cut-and-count approach previously used to estimate the sensitivity by performing a full spectral analysis of the background- and DM-induced signal spectra. We consider two scenarios for the systematic errors on the background spectra: i) systematic errors on the normalization only, and ii) systematic errors on the shape of the backgrounds. We find that the projected sensitivity is rather robust to imperfect knowledge of the backgrounds. Finally, we study how well the parameters of the true WIMP model could be reconstructed in the hypothetical case of a WIMP discovery.Comment: 14 pages, 5 figures, code available at https://github.com/tedwards2412/paleo_detectors/ . v2: Added additional analysis theory details, matches version published in PR

New Projections for Dark Matter Searches with Paleo-Detectors

Paleo-detectors are a proposed experimental technique to search for dark matter (DM). In lieu of the conventional approach of operating a tonne-scale real-time detector to search for DM-induced nuclear recoils, paleo-detectors take advantage of small samples of naturally occurring rocks on Earth that have been deep underground ($\gtrsim 5$ km), accumulating nuclear damage tracks from recoiling nuclei for $\mathcal{O}(1)$ Gyr. Modern microscopy techniques promise the capability to read out nuclear damage tracks with nanometer resolution in macroscopic samples. Thanks to their $\mathcal{O}(1)$ Gyr integration times, paleo-detectors could constitute nuclear recoil detectors with keV recoil energy thresholds and 100 kilotonne-yr exposures. This combination would allow paleo-detectors to probe DM-nucleon cross sections orders of magnitude below existing upper limits from conventional direct detection experiments. In this article, we use improved background modeling and a new spectral analysis technique to update the sensitivity forecast for paleo-detectors. We demonstrate the robustness of the sensitivity forecast to the (lack of) ancillary measurements of the age of the samples and the parameters controlling the backgrounds, systematic mismodeling of the spectral shape of the backgrounds, and the radiopurity of the mineral samples. Specifically, we demonstrate that even if the uranium concentration in paleo-detector samples is $10^{-8}$ (per weight), many orders of magnitude larger than what we expect in the most radiopure samples obtained from ultra basic rock or marine evaporite deposits, paleo-detectors could still probe DM-nucleon cross sections below current limits. For DM masses $\lesssim 10$ GeV/$c^2$, the sensitivity of paleo-detectors could still reach down all the way to the conventional neutrino floor in a Xe-based direct detection experiment.Comment: Invited contribution to Instruments "Special Issue Innovative Experimental Techniques for Direct Dark Matter Detection)". 30 pages, 5 figures, 1 table. Code available at https://github.com/sbaum90/paleoSpec and https://github.com/sbaum90/paleoSen

Increasing Referral of LBJ Patients to the Active Living After Cancer (ALAC) Program

https://openworks.mdanderson.org/sumexp21/1032/thumbnail.jp