Choose your words wisely! Understanding the strategic communication of differential privacy

As a possible solution addressing the growing tension for companies on wanting to collect data and not upset their customers through adverse events simultaneously, differential privacy (DP), an approach that allows the collection of data while ensuring privacy, is gaining in popularity. As many companies increasingly engage in deploying DP, they consequently try to communicate such efforts to their consumers. However, compared to traditional measures, DP has unique characteristics which pose special challenges in its communication. Despite this, prior research did not sufficiently address the user-perspective on DP. Consequently, we adopt an elaboration likelihood lens to investigate how two prevalent descriptions of DP are perceived. By conducting a between-subjects experiment (n=264) we identify powerful mediating effects in the perception of DP, not known before. We contribute to literature by demonstrating the full-mediation of these effects, and to practice by depicting how these can be incorporated in a successful communication strategy

Doctors’ Dilemma – Understanding the Perspective of Medical Experts on AI Explanations

As a solution for the pressing issue in medicine of “black-box” artificial intelligence (AI), models that are hard to understand, explainable AI (XAI) is gaining in popularity. XAI aims at making AI more understandable by explaining its working, e.g., through human understandable explanations. However, while prior research found that such explanations must be adapted for the given expert group being addressed, we find limited work on explanations and their effect on medical experts. To address this gap, we conducted an online experiment with such medical experts (e.g., doctors, nurses) (n=204), to investigate how explanations can be utilized to achieve a causal understanding and respective usage of AI. Our results demonstrate and contribute to literature by identifying transparency and usefulness as powerful mediators, which were not known before. Additionally, we contribute to practice by depicting how these can be used by managers to improve the adoption of AI systems in medicine

At What Price? Exploring the Potential and Challenges of Differentially Private Machine Learning for Healthcare

The increased generation of data has become one of the main drivers of technological innovation in healthcare. This applies in particular to the adoption of Machine Learning models that are used to generate value from the growing available healthcare data. However, the increased processing of sensitive healthcare data comes with challenges in terms of data privacy. Differential privacy, the method of adding randomness to the data to increase privacy, has gained popularity in the last few years as a possible solution. However, while the addition of randomness increases privacy, it also reduces overall model performance, generating a privacy-utility trade-off. Examining this trade-off, we contribute to the literature by providing an empirical paper that experimentally evaluates two prominent and innovative methods of differentially private Machine Learning on medical image and text data to deepen the understanding of the existing potential and challenges of such methods for the healthcare domain

Can Our Health Data Stay Private? A Review and Future Directions for IS Research on Privacy-Preserving AI in Healthcare

The generation of data has become one of the main drivers of modern healthcare. Like other industries, we see that the total amount of healthcare data is growing and in diversity. Thus, Artificial Intelligence (AI) is being used increasingly as a tool to turn this body of healthcare data into real value. But with AI and big data comes big risk, especially in terms of data privacy. Privacy-preserving AI techniques are gaining in popularity to prevent patient privacy compromises while utilizing the potentials offered by AI. However, there is no clear understanding of the current research space of applying such privacy-preserving techniques in healthcare. This paper aims to provide an understanding of these techniques and investigates the emerging research field of privacy-preserving AI and its use in healthcare by reviewing the current multidisciplinary research to synthesize knowledge and derive future research directions in this regard

Nobody Said IT Was Easy - Managing Government-Initiated Information Systems in Addressing and Preparing for Health Crises

COVID-19 served to teach governments many painful lessons about their pitfalls and challenges in managing public health crises. Although both practitioners and academics have been aware that crisis information systems (CIS) constitute a valuable tool for crisis prevention and management, their implementation to counteract COVID-19 lagged by months. To analyze this crisis management mismatch, in this paper, we examine and identify the structural challenges and shortcomings of government-initiated crisis management through CIS. This paper analyzes two CIS projects tackling the COVID-19 crisis, funded by the German government. Drawing on a complexity-lens and the NASSS-framework, key shortcomings are identified. We derive propositions for future CIS projects to enable crisis preparedness. Our outcomes suggest that adopting a complexity perspective in planning, initiating, and developing governmental CIS provides a promising avenue for achieving successful crisis management. We contribute to literature by highlighting the suitability of the complexity-lens in health crises

Together We Are Stronger - Paving the Way for Value Co-Creation in Data Breach Responses

Data breaches pose severe risks to companies. In fact, those incidents generate adverse effects on the customer relationship and companies’ financial performance. To this end, prior research has demonstrated that a dedicated response strategy to a data breach can mitigate these consequences. Nevertheless, contemporary research focuses on one-way response communication with the affected customer. Customers receive notification of the incident and are offered a pre-determined solution but are not actively integrated into the data breach response process. In turn, informed through service failure literature, we argue that a value co-creation perspective of data breach response strategies holds merit. We identify six distinct research avenues for future data breach research through a hermeneutic literature review of salient co-recovery literature. Our research represents a novelty to the field of data breach response strategies. We synthesize the service failure, data breach, and co-creation streams of literature and highlight research shortcomings and opportunities

Discordance of low density lipoprotein cholesterol and non-high density lipoprotein cholesterol and coronary artery disease severity

87th Congress of the European-Atherosclerosis-Society (EAS)European Atherosclerosis So

The Role of Uncertainty in Data Breach Response Processes - A Reactance Theory Perspective

Data breaches lead to inherent uncertainty among customers due to the compromise of information and its potential consequences for customers, e.g., identity theft or credit card misuse. Previous research has focused on outcome-based strategies to address these negative impacts. However, informed by reactance theory, we argue that customers feel a loss of control due to the induced uncertainty and that companies need to tackle these impacts. We test our hypotheses in two empirical studies. The results of Study 1 suggest that data breaches indeed lead to an increased perception of uncertainty among customers. Study 2 examines to what extent the establishment of control can mitigate the negative uncertainty effects. We highlight that by providing customers with control, companies can reduce the degree of uncertainty and increase satisfaction with the response. By conceptualizing choice as a catalyst for perceived control, we offer practitioners a novel strategy for responding to data breaches

Nutritional status and severity of coronary artery disease

Objective The aim of this study is to evaluate the association between Nutritional Risk Index (NRI), a simple tool to assess nutritional status, and coronary artery disease severity and complexity in patients undergoing coronary angiography. Methods This study is a retrospective analysis of 822 patients undergoing coronary angiography. Patients with previous revascularization were excluded. Gensini and SYNTAX scores were calculated according to the angiographic images to determine atherosclerosis severity. NRI was calculated as follows: NRI = [15.19 × serum albumin (g/dl)] + [41.7 × (body weight/ideal body weight)]. In patients ≥65 years of age, Geriatric NRI (GNRI) was used instead of NRI. GNRI was calculated as follows: GNRI = [14.89 × serum albumin (g/dl)] + [41.7 × (body weight/ideal body weight)]. Patients were then divided into three groups as previously reported: NRI < 92, NRI 92–98 and NRI > 98. Gensini and SYNTAX scores were compared between three groups. Results The mean age of study population was 61.9 ± 11.1 years. NRI 98 was measured in 212, 321 and 289 patients, respectively. There was no difference regarding to sex, BMI, smoking, hypertension and diabetes mellitus between three groups. Patients with NRI < 92 had the highest mean Gensini score than the patients with NRI 92–98 and NRI > 98 (38.0 ± 40.6 vs. 31.17 ± 42.4 vs. 25.8 ± 38.4, P = 0.005). Also patients with NRI < 92 had the highest mean SYNTAX score than the patients with NRI 92–98 and NRI > 98 (11.8 ± 12.9 vs. 9.3 ± 12.4 vs. 7.7 ± 11.8, P = 0.001). Also, Gensini score of ≥20 and high SYNTAX score of ≥33 were associated with lower NRI (P < 0.001 and P < 0.001, respectively). Conclusion In our study, nutritional status evaluated by the NRI was associated with more extensive and complex coronary atherosclerosis in patients undergoing coronary angiography

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E. Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported by a French National Research Agency ‘Investissements d’Avenir’ programme grant (ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research Training Program Scholarship. The Raipur Group is thankful to: (1) the University Grants Commission, New Delhi, India for the research grants received through its SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science; and (2) the Center for Translational Chronobiology at the School of Studies in Life Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was supported by grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported by the National Science Foundation Graduate Research Fellowship (R010138018). We acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E. Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova); S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R. C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T. Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera); J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B. Todorova (University of Vienna, Austria). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog