Developments in medical big data analytics may bring societal benefits but are also challenging privacy and other ethical values. At the same time, an overly restrictive data protection regime can form a serious threat to valuable observational studies. Discussions about whether data privacy or data solidarity should be the foundational value of research policies, have remained unresolved. We add to this debate with an empirically informed ethical analysis. First, experiences with the implementation of the General Data Protection Regulation (GDPR) within (...) a European research consortium demonstrate a gap between the aims of the regulation and its effects in practice. Namely, strictly formalised data protection requirements may cause routinisation among researchers instead of substantive ethical reflection, and may crowd out trust between actors in the health data research ecosystem; while harmonisation across Europe and data sharing between countries is hampered by different interpretations of the law, which partly stem from different views about ethical values. Then, building on these observations, we use theory to argue that the concept of trust provides an escape from the privacy-solidarity debate. Lastly, the paper details three aspects of trust that can help to create a responsible research environment and to mitigate the encountered challenges: trust as multi-agent concept; trust as a rational and democratic value; and trust as method for priority setting. Mutual cooperation in research—among researchers and with data subjects—is grounded in trust, which should be more explicitly recognised in the governance of health data research. (shrink)

In this 60th anniversary of Business & Society essay, we seek to make three main contributions at the intersection of governance and artificial intelligence. First, we aim to illuminate some of the deeper social, legal, organizational, and democratic challenges of rising AI adoption and resulting algorithmic power by reviewing AI research through a governance lens. Second, we propose an AI governance framework that aims to better assess AI challenges as well as how different governance modalities can support AI. At the (...) heart of our framework lies the governance forces that apply to institutions, organizations, and individuals, who ultimately provide, regulate, and use AI decision-making. We discuss how businesses may harness AI’s economic power through governance solutions without creating or amplifying societal biases and inequalities. Third, as part of our section on future research, we identify a set of governance trade-offs in AI adoption, suggest future research avenues to conceptually strengthen research on the governance of AI, and lay out key policy recommendations. (shrink)

Consumer-based datasets are the products of data brokerage firms that agglomerate millions of personal records on the adult US population. This big data commodity is purchased by both companies and individual clients for purposes such as marketing, risk prevention, and identity searches. The sheer magnitude and population coverage of available consumer-based datasets and the opacity of the business practices that create these datasets pose emergent ethical challenges within the computational social sciences that have begun to incorporate consumer-based datasets into empirical (...) research. To directly engage with the core ethical debates around the use of consumer-based datasets within social science research, I first consider two case study applications of consumer-based dataset-based scholarship. I then focus on three primary ethical dilemmas within consumer-based datasets regarding human subject research, participant privacy, and informed consent in conversation with the principles of the seminal Belmont Report. (shrink)

This paper introduces the concept of business-related conspiracy theories. Drawing on Aristotelian virtue ethics and undertaking a narratological and ethical analysis of 28 BrCTs found online, I emphasize that BrCTs are narratives with structures rooted in other latent macro- and meta-narratives, including centuries-old myths. In particular, I reconstruct the fictional world of BrCTs – one in which CSR and social contracts have failed – before identifying eight different types of actors as which people can morally situate themselves in their relationships (...) with business. Finally, I elaborate on the actors’ performances and their use of external and legitimate forces to end the story. The paper concludes with a discussion of potential future research to help combat BrCTs, as well as a call for the critical study of political CSR. (shrink)

Artificial intelligence is shaping strategy, activities, interactions, and relationships in business and specifically in marketing. The drawback of the substantial opportunities AI systems and applications provide in marketing are ethical controversies. Building on the literature on AI ethics, the authors systematically scrutinize the ethical challenges of deploying AI in marketing from a multi-stakeholder perspective. By revealing interdependencies and tensions between ethical principles, the authors shed light on the applicability of a purely principled, deontological approach to AI ethics in marketing. To (...) reconcile some of these tensions and account for the AI-for-social-good perspective, the authors make suggestions of how AI in marketing can be leveraged to promote societal and environmental well-being. (shrink)