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

GDPR Privacy Type Clustering: Motivational Factors for Consumer Data Sharing

The GDPR introduced restrictive privacy-preserving measures, affecting the daily life of (online) consumers. Moreover, literature shows that privacy preferences are constantly evolving. This is the first study introducing a GDPR exercising-oriented approach to identify consumer privacy types. Based on a representative sample of the German online population, we cluster consumers according to their privacy importance (“intention to act”) and GDPR knowledge (“ability to act”) and derive four consumer privacy type clusters: fundamentalists, amateurs, pragmatists, and unconcerned. We investigate motivational factors for changing privacy settings and find significant differences between consumers’ intentions and actions for selected factors. This provides evidence for the privacy paradox. Contrarily, intentions and actions align for other factors, which supports the hypothesis that action-based consent might lower the privacy paradox. Finally, we suggest the development of standardized scales and corresponding clustering methodologies for consumer privacy type clustering to increase comparability over time and across populations

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

Consumer Preferences for Privacy Management Systems

This work presents insights into consumer preferences regarding Privacy Management Systems in the context of the General Data Protection Regulation (GDPR). The authors perform a Choice-Based Conjoint experiment with consumers (n = 589) to elicit preferences over four attributes and compute usage likelihoods for all product configurations. Results show that data sharing for marketing purposes and discounts are the most important attributes for consumers. Furthermore, consumers prefer digital access to privacy-related information, detailed rights management for data sharing and no data sharing for marketing purposes. Moreover, a cluster analysis reveals differing importance weights across clusters. The study concludes that incorporating consumer preferences into the design and development process of Privacy Management Systems could increase their use and effectiveness, ultimately strengthening consumers’ privacy rights and companies’ legal compliance. The authors suggest researching legal, business, and consumer requirements more holistically to converge these perspectives to improve Privacy Management Systems adoptions

Evidence for a Peierls phase-transition in a three-dimensional multiple charge-density waves solid

The effect of dimensionality on materials properties has become strikingly evident with the recent discovery of graphene. Charge ordering phenomena can be induced in one dimension by periodic distortions of a material's crystal structure, termed Peierls ordering transition. Charge-density waves can also be induced in solids by strong Coulomb repulsion between carriers, and at the extreme limit, Wigner predicted that crystallization itself can be induced in an electrons gas in free space close to the absolute zero of temperature. Similar phenomena are observed also in higher dimensions, but the microscopic description of the corresponding phase transition is often controversial, and remains an open field of research for fundamental physics. Here, we photoinduce the melting of the charge ordering in a complex three-dimensional solid and monitor the consequent charge redistribution by probing the optical response over a broad spectral range with ultrashort laser pulses. Although the photoinduced electronic temperature far exceeds the critical value, the charge-density wave is preserved until the lattice is sufficiently distorted to induce the phase transition. Combining this result with it ab initio} electronic structure calculations, we identified the Peierls origin of multiple charge-density waves in a three-dimensional system for the first time.Comment: Accepted for publication in Proc. Natl. Acad. Sci. US

Estimating model evidence using data assimilation

We review the field of data assimilation (DA) from a Bayesian perspective and show that, in addition to its by now common application to state estimation, DA may be used for model selection. An important special case of the latter is the discrimination between a factual model–which corresponds, to the best of the modeller's knowledge, to the situation in the actual world in which a sequence of events has occurred–and a counterfactual model, in which a particular forcing or process might be absent or just quantitatively different from the actual world. Three different ensemble‐DA methods are reviewed for this purpose: the ensemble Kalman filter (EnKF), the ensemble four‐dimensional variational smoother (En‐4D‐Var), and the iterative ensemble Kalman smoother (IEnKS). An original contextual formulation of model evidence (CME) is introduced. It is shown how to apply these three methods to compute CME, using the approximated time‐dependent probability distribution functions (pdfs) each of them provide in the process of state estimation. The theoretical formulae so derived are applied to two simplified nonlinear and chaotic models: (i) the Lorenz three‐variable convection model (L63), and (ii) the Lorenz 40‐variable midlatitude atmospheric dynamics model (L95). The numerical results of these three DA‐based methods and those of an integration based on importance sampling are compared. It is found that better CME estimates are obtained by using DA, and the IEnKS method appears to be best among the DA methods. Differences among the performance of the three DA‐based methods are discussed as a function of model properties. Finally, the methodology is implemented for parameter estimation and for event attribution

DADA: data assimilation for the detection and attribution of weather and climate-related events

A new nudging method for data assimilation, delay‐coordinate nudging, is presented. Delay‐coordinate nudging makes explicit use of present and past observations in the formulation of the forcing driving the model evolution at each time step. Numerical experiments with a low‐order chaotic system show that the new method systematically outperforms standard nudging in different model and observational scenarios, also when using an unoptimized formulation of the delay‐nudging coefficients. A connection between the optimal delay and the dominant Lyapunov exponent of the dynamics is found based on heuristic arguments and is confirmed by the numerical results, providing a guideline for the practical implementation of the algorithm. Delay‐coordinate nudging preserves the easiness of implementation, the intuitive functioning and the reduced computational cost of the standard nudging, making it a potential alternative especially in the field of seasonal‐to‐decadal predictions with large Earth system models that limit the use of more sophisticated data assimilation procedures

Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures.

Our study proposed an automatic pipeline for opportunistic osteoporosis screening using 3D texture features and regional vBMD using multi-detector CT images. A combination of different local and global texture features outperformed the global vBMD and showed high discriminative power to identify patients with vertebral fractures. INTRODUCTION Many patients at risk for osteoporosis undergo computed tomography (CT) scans, usable for opportunistic (non-dedicated) screening. We compared the performance of global volumetric bone mineral density (vBMD) with a random forest classifier based on regional vBMD and 3D texture features to separate patients with and without osteoporotic fractures. METHODS In total, 154 patients (mean age 64 ± 8.5, male; n = 103) were included in this retrospective single-center analysis, who underwent contrast-enhanced CT for other reasons than osteoporosis screening. Patients were dichotomized regarding prevalent vertebral osteoporotic fractures (noFX, n = 101; FX, n = 53). Vertebral bodies were automatically segmented, and trabecular vBMD was calculated with a dedicated phantom. For 3D texture analysis, we extracted gray-level co-occurrence matrix Haralick features (HAR), histogram of gradients (HoG), local binary patterns (LBP), and wavelets (WL). Fractured vertebrae were excluded for texture-feature and vBMD data extraction. The performance to identify patients with prevalent osteoporotic vertebral fractures was evaluated in a fourfold cross-validation. RESULTS The random forest classifier showed a high discriminatory power (AUC = 0.88). Parameters of all vertebral levels significantly contributed to this classification. Importantly, the AUC of the proposed algorithm was significantly higher than that of volumetric global BMD alone (AUC = 0.64). CONCLUSION The presented classifier combining 3D texture features and regional vBMD including the complete thoracolumbar spine showed high discriminatory power to identify patients with vertebral fractures and had a better diagnostic performance than vBMD alone

Can host reaction animal models be used to predict and modulate skin regeneration?

The study of host reactions in the biomedical and tissue engineering (TE) fields is a key issue but somehow set aside where TE constructs are concerned. Every day new biomaterials and TE constructs are being developed and presented to the scientific community. The combination of cells and biomolecules with scaffolding materials, as TE constructs, make the isolation and the understanding of the effect of each one those elements over the overall host reaction difficult. Eventually, all variables influence the host reaction and the performance of the constructs. For this reason, current assessment of the in vivo performance of TE constructs follows individual approaches, using specific animal models to independently provide insights regarding the contribution of the biomaterials/scaffolds towards the host reaction, and of all the constructs regarding their functionality. Skin wound healing progress into tissue regeneration or repair is highly dependent on the specificities of the inflammatory stage, as demonstrated by comparison between fetal and adult mechanisms. Thus, it would be expected that insights acquired from host tissue reaction evaluation to biomaterials/scaffolds would be explored to predict healing progression and improve the functionality of skin TE constructs. The rational of this review is to make a comprehensive analysis of to what extent the knowledge obtained from the evaluation of in vivo host reactions to implantable biomaterials/scaffolds has been used in the design of skin TE strategies, by promoting tissue regeneration rather than repair.T.C.S. acknowledges Grant No. RL3-TECT-NORTE-01-0124-FEDER-000020, co-financed by the North Portugal Regional Operational Programme (ON.2-O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund