Search

Social care robots are increasingly used in health care. As the autonomy of these machines continues to increase, they will likely require some capacity for ethical reasoning. Current efforts to imbue robots with ethical decision-making capabilities tend to assume that people perceive robots and human actors as ethically equivalent entities. That is, it is assumed that the ethical behavior of a robot can be modeled based on what is considered ethical human behavior. Despite its ubiquity, this assumption is a hypothesis that warrants empirical investigation. In this article, we partially replicate earlier work that tested this hypothesis. We run two online experiments assessing the ethical equivalence of robots and humans. In addition, we present a new analysis of the data of as reported by Hidalgo et al. (How Humans Judge Machines, MIT Press, 2021). We compare the current (and previous) data with these authors’ work to summarize the tenability of the ethical equivalence hypothesis. Generally, respondents tend to rate robot actions as slightly less ethical than those performed by humans. However, the differences are minor and depend on the scenario. Therefore, we conclude that people treat humans and robots as nearly ethically equivalent. This implies that ethical rules for robots can be derived from existing frameworks and guidelines to regulate human behaviors.

This research note illuminates the ascent of so-called “learning pods”, a concept and phenomenon with close connections to civil society that rose to prominence during the COVID-19 pandemic. We begin by characterizing and positioning learning pods in the diverse U.S. K-12 educational landscape. Next, participation in, and intent to form/join, learning pods are depicted by reporting on secondary data from a population poll among a national sample of U.S. adults since the start of the 2020 academic school year to December 2021. The second half of the research note discusses how learning pods can help garner useful insights to existing nonprofit research and theory.

In the context of the INSERT project, we have been developing a clinical single photon emission computed tomography (SPECT) insert for a magnetic resonance imaging (MRI) system, in order to perform simultaneous SPECT/MRI of the human brain. This system will consist of 20 CsI:Tl scintillation detectors, 5-cm wide and 10-cm long, with a 72-channel silicon photomultiplier (SiPM) readout per detector, and a multimini slit-slat (MSS) collimator set up in a stationary partial ring. Additionally the system has a custom-built transmit/receive MR coil to ensure compatibility with the SPECT system. Due to the novel design of the system/collimator, existing geometric calibration methods are not suitable. Therefore we propose a novel and practical calibration procedure that consists of a set of specific independent measurements to determine the geometric parameters of the collimator. This procedure was developed utilizing a prototype system that consists of a reduced-size single detector with a 36-channel SiPM-based readout and a single MSS collimator module. Validation was performed by reconstructing different imaging phantoms, using a rotating stage to simulate a tomographic acquisition. Regarding uniformity, the coefficient of variation (COV) for the cylinder phantom reconstructed with correct calibration parameters is 6.7%, whereas the COV using incorrect parameters is 9.4%. The quality of the phantom reconstructions provide evidence of the applicability of the proposed method to the calibration of the prototype system. This procedure can be easily adapted for the final INSERT system.