Search for: All records

This article argues that privacy protection should move beyond protecting the “real me” as the holder of personal data and toward protecting the “statistical me”: the inferred, model-generated version of myself that AI systems construct, remember, and act upon. 

This commentary introduces theShared Values for Digitalization, developed by a task force of the Association for Information Systems (AIS) to guide IT professionals, educators, and students in addressing the ethical and professional challenges emerging from digital transformation. Recognizing the limitations of rigid codes of conduct in a dynamic and globally diverse digital landscape, the commentary discusses seven shared values–professional competence, trustworthiness, respect for intellectual property, privacy and confidentiality, inclusiveness, social good, and environmental sensitivity. Rather than prescribing rules, these values serve as reflective guides that encourage discernment, dialogue, and responsible action across contexts. While many students encounter ethics coursework, theShared Valuesare aimed at a distinct need: professional judgment in socio-technical design and governance decisions where technical choices reshape what is measurable, contestable, and consequential. The commentary further outlines a structured process for navigating conflicts among values and illustrates its application through two cases involving artificial intelligence (AI)-driven, content moderation and data anonymization. Finally, it discusses strategies for embedding these shared values within the broader information systems community–through teaching, research, and professional practice–to cultivate ethical awareness and moral judgment in the age of digitalization. 

Abstract Two potential obstacles stand between the observation of a statistical correlation and the design (and deployment) of an effective intervention,omitted variable biasandreverse causality. Whereas the former has received ample attention, comparably scant focus has been devoted to the latter in the methodological literature. Many existing methods for reverse causality testing commence by postulating a structural model that may suffer from widely recognized issues such as the difficulty of properly setting temporal lags, which are critical to model validity. In this article, we draw upon advances in machine learning, specifically the recently established link between causal direction and the effectiveness of semi-supervised learning algorithms, to develop a novel method for reverse causality testing that circumvents many of the assumptions required by traditional methods. Mathematical analysis and simulation studies were carried out to demonstrate the effectiveness of our method. We also performed tests over a real-world dataset to show how our method may be used to identify causal relationships in practice. 

ABSTRACT The Earth's ionosphere plays a critical role in radio wave transmission, reflection, and scattering, directly affecting communication, navigation, and positioning systems. However, the comprehensive impacts of space weather remain to be fully established in cases where the ionosphere experiences strong disturbances during geomagnetic storms. We reported unprecedented observational evidence of extreme ionospheric electron density depletion and its hemispheric asymmetry during the May 10–12, 2024 super geomagnetic storm, utilizing multi-instrument ground-based and spaceborne in-situ observations. The ionospheric electron density significantly decreased, with a maximum reduction of 98% over the whole northern hemisphere for more than 2 days, causing backscatter echo failures in multiple ionosondes within the Chinese Meridian Project (CMP) monitoring network. In contrast, mid-to-low latitude regions in the southern hemisphere exhibited electron density enhancements. Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) simulations demonstrated strong consistency with northern hemispheric observations. The vertical drift and the column integrated ratio of O and N2 (ΣO/N2) from observations and simulations indicated the deep reduction of total electron content (TEC) mainly generated by severe ion recombination associated with neutral composition changes that interacted with the disturbed electric field. The summer to winter neutral wind and asymmetry of O/N₂ were possibly responsible for the asymmetry in electron density between the northern and southern hemispheres. These results advance understanding of ionospheric storm physics by establishing causal links between magnetosphere-thermosphere coupling processes and extreme electron density variations, while providing critical observational constraints for space weather model refinement. 

A key challenge facing the use of machine learning (ML) in organizational selection settings (e.g., the processing of loan or job applications) is the potential bias against (racial and gender) minorities. To address this challenge, a rich literature of Fairness-Aware ML (FAML) algorithms has emerged, attempting to ameliorate biases while maintaining the predictive accuracy of ML algorithms. Almost all existing FAML algorithms define their optimization goals according to a selection task, meaning that ML outputs are assumed to be the final selection outcome. In practice, though, ML outputs are rarely used as-is. In personnel selection, for example, ML often serves a support role to human resource managers, allowing them to more easily exclude unqualified applicants. This effectively assigns to ML a screening rather than a selection task. It might be tempting to treat selection and screening as two variations of the same task that differ only quantitatively on the admission rate. This paper, however, reveals a qualitative difference between the two in terms of fairness. Specifically, we demonstrate through conceptual development and mathematical analysis that miscategorizing a screening task as a selection one could not only degrade final selection quality but also result in fairness problems such as selection biases within the minority group. After validating our findings with experimental studies on simulated and real-world data, we discuss several business and policy implications, highlighting the need for firms and policymakers to properly categorize the task assigned to ML in assessing and correcting algorithmic biases. 

Over the past two decades, behavioral research in privacy has made considerable progress transitioning from acontextual studies to using contextualization as a powerful sensitizing device for illuminating the boundary conditions of privacy theories. Significant challenges and opportunities wait, however, on elevating and converging individually contextualized studies to a context-contingent theory that explicates the mechanisms through which contexts influence consumers’ privacy concerns and their behavioral reactions. This paper identifies the important barriers occasioned by this lack of context theorizing on the generalizability of privacy research findings and argues for accelerating the transition from the contextualization of individual research studies to an integrative understanding of context effects on privacy concerns. It also takes a first step toward this goal by providing a conceptual framework and the associated methodological instantiation for assessing how context-oriented nuances influence privacy concerns. Empirical evidence demonstrates the value of the framework as a diagnostic device guiding the selection of contextual contingencies in future research, so as to advance the pace of convergence toward context-contingent theories in information privacy. This paper was accepted by Anindya Ghose, information systems. 

Abstract Researchers have investigated whether machine learning (ML) may be able to resolve one of the most fundamental concerns in personnel selection, which is by helping reduce the subgroup differences (and resulting adverse impact) by race and gender in selection procedure scores. This article presents three such investigations. The findings show that the growing practice of making statistical adjustments to (nonlinear) ML algorithms to reduce subgroup differences must create predictive bias (differential prediction) as a mathematical certainty. This may reduce validity and inadvertently penalize high‐scoring racial minorities. Similarly, one approach that adjusts the ML input data only slightly reduces the subgroup differences but at the cost of slightly reduced model accuracy. Other emerging tactics involve weighting predictors to balance or find a compromise between the competing goals of reducing subgroup differences while maintaining validity, but they have been limited to two outcomes. The third investigation extends this to three outcomes (e.g., validity, subgroup differences, and cost) and presents an online tool. Collectively, the studies in this article illustrate that ML is unlikely to be able to resolve the issue of adverse impact, but it may assist in finding incremental improvements. 

Research and practical development of data-anonymization techniques have proliferated in recent years. Yet, limited attention has been paid to examine the potentially disparate impact of privacy protection on underprivileged subpopulations. This study is one of the first attempts to examine the extent to which data anonymization could mask the gross statistical disparities between subpopulations in the data. We first describe two common mechanisms of data anonymization and two prevalent types of statistical evidence for disparity. Then, we develop conceptual foundation and mathematical formalism demonstrating that the two data-anonymization mechanisms have distinctive impacts on the identifiability of disparity, which also varies based on its statistical operationalization. After validating our findings with empirical evidence, we discuss the business and policy implications, highlighting the need for firms and policy makers to balance between the protection of privacy and the recognition/rectification of disparate impact. This paper was accepted by Chris Forman, information systems.