Tracing the Legitimacy of Artificial Intelligence – A Media Analysis, 1980-2020

Artificial Intelligence (AI) has received ambivalent evaluations, ranging from AI as a great opportunity and solution to crucial problems of our time to AI as a threat to humanity. For AI technologies to diffuse, they need to gain legitimacy. We trace the legitimacy of AI in society from 1980 to 2020. For our analysis, we rely on 2,543 newspaper articles from The New York Times as a reflection of societal discourse over the legitimacy of AI. Using computer-assisted content analysis, we find a sharp increase in media coverage around the mid-2010s. We find the language used in the articles to be predominantly positive and to show little changes over time. Our analysis also uncovers six highly discussed industries in the context of AI

Weaponizing the GDPR: How Flawed Implementations Turn the Gold Standard for Privacy Laws into Fool\u27s Gold

Despite its ambitious goals of protecting personal data and generally being well-received, the General Data Protection Regulation (GDPR) can be exploited for identity theft by weaponizing subject access requests (SARs). To understand this threat and investigate the impact of victims’ privacy awareness and public exposure on its effectiveness, we selected three victims – highly privacy aware person, average user, and semipublic figure – and tasked six realistic attackers with stealing their personal data. Based on 718 submitted SARs, we provide novel insights from a realistic case study of a law being weaponized and advance the understanding of GDPR-based identity theft by demonstrating its practical viability. Further, we derive patterns from common flaws observed in SAR handling processes, and explore threat mitigation options for individuals, organizations, and lawmakers. Generalizing our findings, we uncover approaches for cybersecurity researchers to probe further laws for flaws

Ethical AI Research Untangled: Mapping Interdisciplinary Perspectives for Information Systems Research

We provide a systematic overview of the interdisciplinary discourse on ethical AI by combining bibliometric and text mining approaches on a corpus of 23,870 ethical AI publications from journals and conference proceedings. In our research in progress, we offer three contributions of interest to IS scholars: First, in our term analyses, we empirically delineate ethical AI and related terms such as responsible or trustworthy AI. Second, we unearth the intellectual structure of the field and identify five thematic clusters, some of which are directly relevant to IS scholars. Third, we identify that IS research on ethical AI should more intensely consider fairness and transparency as well as the link to explainability. Additionally, we suggest that IS scholars contribute towards policymakers’ ethical AI guidelines by contributing their strong expertise in practical applications

Conceptual contributions in marketing scholarship: patterns, mechanisms, and rebalancing options

This article analyzes the nature and temporal change of conceptual contributions in marketing scholarship with two complementary studies. First, based on a computer-aided text analysis of 5,922 articles published in the four major marketing journals between 1990 and 2021, the authors analyze how conceptual contributions have changed over time using the MacInnis (2011) framework. Results indicate that over the past three decades, theorizing efforts have strongly favored “envisioning” and “explicating” at the expense of “relating” and “debating,” with this imbalance increasing over time. Second, the authors draw on 48 in-depth interviews with editors, department heads, and authors to validate these patterns and uncover the underlying mechanisms. The findings indicate that a prevalent thought style has developed in the field—defined by the research ideals of novelty, clarity, and quantification—that shapes the collective view of how marketing scholars, in their roles as authors, reviewers, and mentors, can make a valuable contribution to marketing scholarship. This thought style favors envisioning and explicating contributions and disfavors relating and debating contributions. Jointly, the two studies point to several rebalancing options that can reinvigorate relating and debating contributions while preserving the current strengths of the marketing field

Privacy Risk Perceptions in the Connected Car Context

Connected car services are rapidly diffusing as they promise to significantly enhance the overall driving experience. Because they rely on the collection and exploitation of car data, however, such services are associated with significant privacy risks. Following guidelines on contextualized theorizing, this paper examines how individuals perceive these risks and how their privacy risk perceptions in turn influence their decision-making, i.e., their willingness to share car data with the car manufacturer or other service providers. We conducted a multi-method study, including interviews and a survey in Germany. We found that individuals’ level of perceived privacy risk is determined by their evaluation of the general likelihood of IS-specific threats and the belief of personal exposure to such threats. Two cognitive factors, need for cognition and institutional trust, are found to moderate the effect that perceived privacy risk has on individuals’ willingness to share car data in exchange for connected car services

Navigating Uncertain Waters: How Organizations Respond to Institutional Pressure in Times of the Looming EU AI Act

The looming AI Act puts significant pressure on organizations to develop safe and reliable AI. First, we analyze 975 AI incidents to provide an overview of the actors involved in identifying primary AI failures. Second, using institutional theory and based on over two years of observation of more than 400 actors, we identify four strategies (circumvent, comply, compromise, and control) and exemplary response practices (relabel, redefine, balance, and predefine) for how firms respond to institutional pressures from AI regulation. We thereby extend the institutional theory perspective on AI regulation. Using our longitudinal multi-actor perspective, we develop the 4C-Framework for how organizations actively respond to AI regulation. Moreover, we demonstrate how this framework can be applied to impending AI regulation, including strategies for engaging with regulators, exploring opportunities for collaboration, and dealing with the spread of misinformation. Overall, this paper provides a comprehensive approach for companies to proactively shape AI regulation

Perceived privacy risk in the Internet of Things: determinants, consequences, and contingencies in the case of connected cars

The Internet of Things (IoT) is permeating all areas of life. However, connected devices are associated with substantial risks to users’ privacy, as they rely on the collection and exploitation of personal data. The case of connected cars demonstrates that these risks may be more profound in the IoT than in extant contexts, as both a user's informational and physical space are intruded. We leverage this unique setting to collect rich context-immersive interview (n = 33) and large-scale survey data (n = 791). Our work extends prior theory by providing a better understanding of the formation of users’ privacy risk perceptions, the effect such perceptions have on users’ willingness to share data, and how these relationships in turn are affected by inter-individual differences in individuals’ regulatory focus, thinking style, and institutional trust

Exploring Minecraft Settlement Generators with Generative Shift Analysis

© 2023 The Author(s). This is an open access conference paper distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/With growing interest in Procedural Content Generation (PCG) it becomes increasingly important to develop methods and tools for evaluating and comparing alternative systems. There is a particular lack regarding the evaluation of generative pipelines, where a set of generative systems work in series to make iterative changes to an artifact. We introduce a novel method called Generative Shift for evaluating the impact of individual stages in a PCG pipeline by quantifying the impact that a generative process has when it is applied to a pre-existing artifact. We explore this technique by applying it to a very rich dataset of Minecraft game maps produced by a set of alternative settlement generators developed as part of the Generative Design in Minecraft Competition (GDMC), all of which are designed to produce appropriate settlements for a pre-existing map. While this is an early exploration of this technique we find it to be a promising lens to apply to PCG evaluation, and we are optimistic about the potential of Generative Shift to be a domain-agnostic method for evaluating generative pipelines

Exploring Minecraft Settlement Generators with Generative Shift Analysis

With growing interest in Procedural Content Generation (PCG) it becomes increasingly important to develop methods and tools for evaluating and comparing alternative systems. There is a particular lack regarding the evaluation of generative pipelines, where a set of generative systems work in series to make iterative changes to an artifact. We introduce a novel method called Generative Shift for evaluating the impact of individual stages in a PCG pipeline by quantifying the impact that a generative process has when it is applied to a pre-existing artifact. We explore this technique by applying it to a very rich dataset of Minecraft game maps produced by a set of alternative settlement generators developed as part of the Generative Design in Minecraft Competition (GDMC), all of which are designed to produce appropriate settlements for a pre-existing map. While this is an early exploration of this technique we find it to be a promising lens to apply to PCG evaluation, and we are optimistic about the potential of Generative Shift to be a domain-agnostic method for evaluating generative pipelines