Artificial Intelligence (AI) can benefit society, but it is also fraught with risks. Societal adoption of AI is recognized to depend on stakeholder trust in AI, yet the literature on trust in AI is fragmented, and little is known about the vulnerabilities faced by different stakeholders, making it is difficult to draw on this evidence-base to inform practice and policy. We undertake a literature review to take stock of what is known about the antecedents of trust in AI, and organize our findings around five trust challenges unique to or exacerbated by AI. Further, we develop a concept matrix identifying the key vulnerabilities to stakeholders raised by each of the challenges, and propose a multi-stakeholder approach to future research.
How can we use synergy to explain the value created by business analytics systems? In this article, we conceptualize and operationalize two important aspects of synergy: namely, the synergistic relationship and the synergistic outcome. We explore the enablers and mechanisms that are involved in a synergistic relationship between business analytics systems and customer relationship management systems and define it as the ability of systems to work together, span their boundaries and complement each other. Synergistic outcomes are the new business analytics–enabled customer relationship management systems that emerge from the synergistic relationship between business analytics systems and customer relationship management systems. Taking a whole system perspective, business analytics–enabled customer relationship management systems comprise the components and the emergent properties that arise from their interaction (e.g. the ability to cross-sell and up-sell based on advanced computational methods), in which the emergent properties are new because they do not exist in the individual components. We develop a research model that uses Synergistic Relationship and Synergistic Outcomes to explain the business value created by business analytics systems and customer relationship management systems, and we test this model using a survey of 201 managers in Australia and the United States. We find that the synergistic relationship plays a significant role in the creation of business analytics–enabled customer relationship management systems and subsequently business value. Business analytics–enabled customer relationship management systems—comprising business analytics systems, customer relationship management systems and their emergent properties—contribute to transactional, informational and strategic value. This goes beyond the value created by the business analytics and customer relationship management systems individually, as measured through statistical interaction.
Governments are increasingly relying on algorithmic decision-making (ADM) to deliver public services. Recent information systems literature has raised concerns regarding ADM's negative unintended consequences, such as widespread discrimination, which in extreme cases can be destructive to society. The extant empirical literature, however, has not sufficiently examined the destructive effects of governmental ADM. In this paper, we report on a case study of the Australian government's "Robodebt" programme that was designed to automatically calculate and collect welfare overpayment debts from citizens but ended up causing severe distress to citizens and welfare agency staff. Employing perspectives from systems thinking and organisational limits, we develop a research model that explains how a socially destructive government ADM programme was initiated, sustained, and delegitimized. The model offers a set of generalisable mechanisms that can benefit investigations of ADM's consequences. Our findings contribute to the literature of unintended consequences of ADM and demonstrate to practitioners the importance of setting up robust governance infrastructures for ADM programmes.
Although Big Data generates many benefits for individuals, organizations and society, significant ethical issues are forcing governments to review their regulations so that citizens' rights are protected. Given these ethical issues and a gradual increase of awareness about them, individuals are in need of new technical solutions to engage with organizations that extract value from Big Data. Currently, available solutions do not adequately accommodate the conflicting interests of individuals and organizations. In this paper, we propose a conceptual design for an artifact that will raise awareness amongst individuals about Big Data ethical issues and help to restore the power balance between individuals and organizations. Furthermore, we set forward a design agenda outlining future activities towards building and evaluating our proposed artifact. Our work is grounded in discourse ethics and stakeholder theory and intertwined with the European General Data Protection Regulation (GDPR).
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