David De Cremer’s research while affiliated with Northeastern University and other places

For this commentary we explore the how and why of a looming responsibility shift to autonomous AI in medicine and the potential unintended consequence of reducing doctors’ sense of obligation to preserve ethics. In exploring this proposition, we address the difficulties of relying upon intelligent machines to uphold ethics and we offer suggestions on how policymakers and medical educators might prevent ethics degradation caused by the alignment of doctors with autonomous AI.

As firms increasingly depend on artificial intelligence to evaluate people across various contexts (e.g., job interviews, performance reviews), research has explored the specific impact of algorithmic evaluations in the workplace. In particular, the extant body of work focuses on the possibility that employees may perceive biases from algorithmic evaluations. We show that although perceptions of biases are indeed a notable outcome of AI-driven assessments (vs. those performed by humans), a crucial risk inherent in algorithmic evaluations is that individuals perceive them as lacking respect and dignity. Specifically, we find that the effect of algorithmic (vs. human) evaluations on perceptions of disrespectful treatment (a) remains significant while controlling for perceived biases (but not vice versa), (b) is significant even when the effect on perceived biases is not, and (c) is larger in size than the effect on perceived biases. The effect of algorithmic evaluations on disrespectful treatment is explained by perceptions that individuals’ detailed characteristics are not properly considered during the evaluation process conducted by AI.

Because of a fear of missing out, organizations today rush out to adopt AI while not understanding what the technology stands for and how to deploy it most effectively. Trusting blindly the promises of AI as the ultimate value-creator, business leaders are unclear about their roles in making AI work for the organization and therefore delegate responsibility of the adoption process entirely to tech experts. In this opinion paper, I argue that this situation breeds fertile ground for a pending AI crisis as organizations will fail to align AI deployment with organizational purpose and in doing so fail to put AI to use in socially responsible and ethical ways. As a result, no real gains are achieved when adopting AI while threats and potential harm to society and humanity in general are fostered.

The emergence of generative AI technologies has led to an increasing number of people collaborating with AI to produce creative works. Across two experimental studies, in which we carefully designed and programmed state-of-the-art human–AI interfaces, we examine how the design of generative AI systems influences human creativity (poetry writing). First, we find that people were most creative when writing a poem on their own, compared to first receiving a poem generated by an AI system and using sophisticated tools to edit it (Study 1). Following this, we demonstrate that this creativity deficit dissipates when people co-create with—not edit—AI and establish creative self-efficacy as an important mechanism in this process (Study 2). Thus, our findings indicate that people must occupy the role of a co-creator, not an editor, to reap the benefits of generative AI in the production of creative works.

The use of chatbots is becoming widespread as they offer significant economic opportunities. At the same time, however, customers seem to prefer interacting with human operators when making inquiries and as a result are not as cooperative with chatbots when their use is known. This specific situation creates an incentive for organizations to use chatbots without disclosing this to customers. Will this deceptive practice harm the reputation of the organization, and the employees who work for them? Across four experimental studies, we demonstrate that prospective customers, who interact with an organization using chatbots, perceive the organization to be less ethical if the organization does not disclose the information about the chatbot to their customers (Study 1). Moreover, employees that work for an organization which requires them to facilitate the deceptive use of a chatbot exhibit greater turnover intentions (Study 2) and receive worse job opportunities from recruiters in both a hypothetical experimental setting (Study 3) and from professional job recruiters in the field (Study 4). These results highlight that using chatbots deceptively has far reaching negative effects, which begin with the organization and ultimately impact their customers and the employees that work for them.

Given the increasing use of intelligent technologies and the resulting requirement for leaders to be tech-savvy, an important question that emerges is whether perceptions of leadership effectiveness are changing too? Drawing from gender role theory and expectation violation theory, we propose and test a model of whether and how females can benefit from being tech-savvy. To explore whether women may stand to gain from the changing nature of the workforce or not, we ran a series of studies. Our data sources included a cross-sectional time-lagged field study and experimental studies with students and online adults. We found that a definitive skill of the current technological era, tech-savviness, changes perceptions and evaluations of female leaders in a much more positive way than it does for male leaders, as such allowing female leaders to be more likely to be perceived as effective leaders. The reason for the effect that tech-savvy women were more likely to be perceived as effective leaders was driven by the fact that tech-savvy women are seen as more competent and hence, attributed more social status. Our research goes beyond merely focusing on the negative effects of gender stereotypes by providing an actionable solution—that comes from promoting tech knowledge and savviness—for women to counter gender stereotypes that undermine them from being viewed as effective leaders.