This article studies whether people want to control what information on their own past pro-social behavior is revealed to others. Participants are assigned a color that depends on their past pro-social behavior. They can spend money to manipulate the probability with which their color is revealed to another participant. The data show that participants are more likely to reveal colors with more favorable informational content. This pattern is not found in a control treatment in which colors are randomly assigned, thus revealing nothing about past pro-social behavior. Regression analysis confirms these findings, also when controlling for past pro-social behavior. These results complement the existing empirical evidence, confirming that people strategically and, therefore, consciously manipulate their social image.

Discussions of the development and governance of data-driven systems have, of late, come to revolve around questions of trust and trustworthiness. However, the connections between them remain relatively understudied and, more importantly, the conditions under which the latter quality of trustworthiness might reliably lead to the placing of ‘well-directed’ trust. In this paper, we argue that this challenge for the creation of ‘rich’ trustworthiness, which we term the Trustworthiness Recognition Problem, can usefully be approached as a problem of effective signalling, and suggest that its resolution can be informed by a multidisciplinary approach that relies on insights from economics and behavioural ecology. We suggest, overall, that the domain specificity inherent to the signalling theory paradigm offers an effective solution to the TRP, which we believe will be foundational to whether and how rapidly improving technologies are integrated in the healthcare space. We suggest that solving the TRP will not be possible without taking an interdisciplinary approach and suggest further avenues of inquiry that we believe will be fruitful.

There has been an erosion of trust in medical care and clinical research, and this has raised issues about whether institutions and investigators conducting clinical research are worthy of trust. We review recent literature on research on trust and trustworthiness in the clinical research enterprise and identify opportunities to enhance trustworthiness, which will likely increase participant trust in clinical research. In addition, we review materials reporting the results of national polls related to the public’s trust in different occupations. The literature on trustworthiness and trust is complex and suffers from a lack of agreement on definitions of trust and trustworthiness and actions to enhance trustworthiness. Nonetheless, institutions need to take action to address the many elements that contribute to being perceived as trustworthy. As a complementary approach, since nurses have consistently ranked highest on trust by the public for twenty-two straight years, we analyze the features that likely account for the public’s uniform high regard for nurses. We propose specific actions to enhance the role of research nurses in the research enterprise, without compromising their primary role as participant advocates, that we have adopted at Rockefeller University to gain the benefits of the public’s trust in nurses in building trustworthiness.

The robots of tomorrow should be endowed with the ability to adapt to drastic and unpredicted changes in their environment and interactions with humans. Such adaptations, however, cannot be boundless: the robot must stay trustworthy. So, the adaptations should not be just a recovery into a degraded functionality. Instead, they must be true adaptations: the robot must change its behaviour while maintaining or even increasing its expected performance and staying at least as safe and robust as before. The RoboSAPIENS project will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that they are carried out in an intrinsically trustworthy, safe and efficient manner, thereby reconciling open-ended self-adaptation with safety by design. RoboSAPIENS will transform these foundations into ‘first time right’-design tools and platforms and will validate and demonstrate them.

There are epistemic manipulators in the world. These people are actively attempting to sacrifice epistemic goods for personal gain. In doing so, manipulators have led many competent epistemic agents into believing contrarian theories that go against well-established knowledge. In this paper, I explore one mechanism by which manipulators get epistemic agents to believe contrarian theories. I do so by looking at a prominent empirical model of trustworthiness. This model identifies three major factors that epistemic agents look for when trying to determine who is trustworthy. These are (i) ability, (ii) benevolence, and (iii) moral integrity. I then show how manipulators can manufacture the illusion that they possess these factors. This leads epistemic agents to view manipulators as trustworthy sources of information. Additionally, I argue that fact-checking will be an ineffective – or even harmful – practice when correcting the beliefs of epistemic agents who have been tricked by this illusion of epistemic trustworthiness. I suggest that in such cases we should use an alternative correction, which I call trust undercutting.

Free riding involves benefiting from common resources or services while avoiding contributing to their production and maintenance. Few studies have adequately investigated the propensity to overestimate the prevalence of free riding. This is a significant omission, as exaggeration of the phenomenon is often used to justify control and coercion systems. To address this gap, we investigate how the common good approach may mitigate the flaws of a system excessively focused on free-riding risk. In this conceptual paper featuring illustrative vignettes, we argue that the common good perspective is realistic and effective in preventing this excessive attention by promoting trust as an unconditional gift and a response to vulnerability. We discuss the common good perspective’s originality over the dominant approaches and propose a set of ethical and managerial recommendations that may be the best protection against this excessive focus and maybe even against free riding itself.

Usage of large language models and chat bots will almost surely continue to grow, since they are so easy to use, and so (incredibly) credible. I would be more comfortable with this reality if we encouraged more evaluations with humans-in-the-loop to come up with a better characterization of when the machine can be trusted and when humans should intervene. This article will describe a homework assignment, where I asked my students to use tools such as chat bots and web search to write a number of essays. Even after considerable discussion in class on hallucinations, many of the essays were full of misinformation that should have been fact-checked. Apparently, it is easier to believe ChatGPT than to be skeptical. Fact-checking and web search are too much trouble.

Large-scale societal issues such as public health crises highlight the need to communicate scientific information, which is often uncertain, accurately to the public and policy makers. The challenge is to communicate the inherent scientific uncertainty — especially about the underlying quality of the evidence — whilst supporting informed decision making. Little is known about the effects that such scientific uncertainty has on people’s judgments of the information. In three experimental studies (total N=6,489), we investigate the influence of scientific uncertainty about the quality of the evidence on people’s perceived trustworthiness of the information and decision making. We compare the provision of high, low, and ambiguous quality-of-evidence indicators against providing no such cues. Results show an asymmetric relationship: people react more strongly to cues of low quality of evidence than they do to high quality of evidence compared to no cue. While responses to a cue of high quality of evidence are not significantly different from no cue; a cue of low or uncertain quality of evidence is accompanied by lower perceived trustworthiness and lower use of the information in decision making. Cues of uncertain quality of evidence have a similar effect to those of low quality. These effects do not change with the addition of a reason for the indicated quality level. Our findings shed light on the effects of the communication of scientific uncertainty on judgment and decision making, and provide insights for evidence-based communications and informed decision making for policy makers and the public.

This study compares Chinese people’s trust and trustworthiness, risk attitude, and time preference before and after the onset of the COVID-19 pandemic in China. We compare the preferences of subjects in two online experiments with samples drawn from 31 provinces across mainland China before and after the onset of the pandemic. We test two competing hypotheses regarding trust and trustworthiness. On the one hand, the outbreak as a collective threat could enhance in-group cohesion and cooperation and thus increase trust and trustworthiness. On the other hand, to the extent that people expect their future income to decline, they may become more self-protective and self-controlled, and thus less trusting and trustworthy and more risk averse and patient. Comparing before and after the onset, we found that the subjects increased in trustworthiness. After the onset, trust and trustworthiness (and risk aversion and present bias too) were positively correlated with the COVID-19 prevalence rate in the provinces. Subjects with more pessimistic expectations about income change showed more risk aversion and lower discount rates, supporting the speculation concerning self-control.