States have responded to COVID-19's ongoing workplace disruptions with myriad workers' compensation policy changes. While some states have extended presumptions of coverage to a large swath of workers who are at risk of contracting COVID-19 on the job, others have been far more measured or have declined to meaningfully change their policies. In contemplating state responses to COVID-19, this Comment makes two novel contributions to legal scholarship: First, it categorizes states into one of four groupings along a spectrum of coverage, from most likely to extend workers' compensation benefits to at-risk employees to least likely. Second, it contends that both historical and economic principles counsel states to adopt a selective coverage approach to pandemic workers' compensation. Under a selective coverage approach, states adopt a presumption of coverage for certain workers, such as first responders and grocery store clerks, who are most at risk of contracting pandemic diseases through the course of their work.

Modern vehicles are using AI and increasingly sophisticated sensor suites to improve Advanced Driving Assistance Systems (ADAS) and support automated driving capabilities. Heads-Up-Displays (HUDs) provide an opportunity to visually inform drivers about vehicle perception and interpretation of the driving environment. One approach to HUD design may be to reveal to drivers the vehicle’s full contextual understanding, though it is not clear if the benefits of additional information outweigh the drawbacks of added complexity, or if this balance holds across drivers. We designed and tested an Augmented Reality (AR) HUD in an online study (N = 298), focusing on the influence of HUD visualizations on drivers’ situation awareness and perceptions. Participants viewed two driving scenes with one of three HUD conditions. Results were nuanced: situation awareness declined with increasing driving context complexity, and contrary to expectation, also declined with the presence of a HUD compared to no HUD. Significant differences were found by varying HUD complexity, which led us to explore different characterizations of complexity, including counts of scene items, item categories, and illuminated pixels. Our analysis finds that driving style interacts with driving context and HUD complexity, warranting further study.

We develop an optimal tax framework that combines two recent extensions of tax analysis: a tax-systems emphasis on non-rate policy instruments, and a recognition of the role of behavioral biases. Although the implications of taxpayers' biases for optimal tax rates have received considerable attention, a complete analysis of this aspect of optimal tax theory must account for the fact that such biases are often endogenous to the non-rate aspects of a tax system. We first generalize and extend the analysis of optimal tax systems to incorporate endogenous behavioral biases. We then develop a novel and important application of this issue, showing how misperception of the tax rate affects the optimal breadth of the tax base.

The sound a robot or automated system makes and the sounds it listens for in our shared acoustic environment can greatly expand its contextual understanding and to shape its behaviors to the interactions it is trying to perform. People convey significant information with sound in interpersonal communication in social contexts. Para-linguistic information about where we are, how loud we're speaking, or if we sound happy, sad or upset are relevant to understand for a robot that looks to adapt its interactions to be socially appropriate. Similarly, the qualities of the sound an object makes can change how people perceive that object and can alter whether or not it attracts attention, interrupts other interactions, reinforces or contradicts an emotional expression, and as such should be aligned with the designer's intention for the object. In this tutorial, we will introduce the participants to software and design methods to help robots recognize and generate sound for human-robot interaction (HRI). Using open-source tools and methods designers can apply to their own robots, we seek to increase the application of sound to robot design and stimulate HRI research in robot sound.

As autonomous vehicles (AVs) become a reality on public roads, researchers and designers are beginning to see unexpected reactions from the public ranging from curiosity to vandalism. These behaviors are concerning, as AV platforms will need to know how to deal with people behaving unexpectedly or aggressively. We call this griefing of AVs, adopting the term from harassment in online gaming. We discuss several examples of griefing observed in on-road field studies using a Wizard-of-Oz driverless car. While Uber and Waymo have anecdotally mentioned vandalism towards AVs, we believe this to be the first public video available of AV griefing ranging from playful to aggressive. To stimulate discussion, we propose speculative design principles to address griefing.

Autonomous vehicle (AV) systems are developing at a rapid pace, not only in technological capabilities, but also in human-centered directions. Despite this development, we lack a nuanced understanding of driver preference in decision scenarios that semi-AVs will face, and of possible misalignment between semi-AV decisions and user preference. Using an online survey, we explore how participants would like semi-AVs to act and alert them of the vehicles' decisions in various scenarios. Participants reported varying levels of comfort with autonomy, desire to takeover control, and desire for AV informing. Individual differences, including level of experience with autonomy and situation awareness, affected perceptions of the vehicle. Our results highlight the importance of considering driver preference in AV decision-making, and we present an influence diagram that situates this factor among others. We also derive five design principles, including that a previous positive AV experience can lead to more harmful consequences for AVs when not aligned with driver preference.

Drivers and pedestrians use various culturally-based nonverbal cues such as head movements, hand gestures, and eye contact when crossing roads. With the absence of a human driver, this communication becomes challenging in autonomous vehicle (AV)- pedestrian interaction. External human-machine interfaces (eHMIs) for AV-pedestrian interaction are being developed based on the research conducted mainly in North America and Europe, where the traffic and pedestrian behavior are very structured and follow the rules. In other cultures (e.g., South Asia), this can be very unstructured (e.g., pedestrians spontaneously crossing the road at non-cross walks is not very uncommon). However, research on investigating cross-cultural differences in AV-Pedestrian interaction is scarce. This research focuses on investigating cross-cultural differences in AV-Pedestrian interaction to gain insights useful for designing better eHMIs. This paper details three cross-cultural studies designed for this purpose, and that will be deployed in two different cultural settings: Sri Lanka and Germany.

Abstract Through a strategic learning process, prototypes unveil design directions. We provide a review of prototyping methods for novice designers to study and pedagogical practice for capstone design course faculty to juxtapose. Stanford University's ME310 graduate-level project-based learning course introduces students to various prototyping design techniques, such as Needfinding and Benchmarking, and prototyping methods, such as the Critical Experience Prototype, Critical Function Prototype, Dark Horse Prototype, Part-X is Finished, Funky System Prototype, and Functional System Prototype.

Electric vehicles’ (EVs) nearly silent operation has proved to be dangerous for bicyclists and pedestrians, who often use an internal combustion engine’s sound as one of many signals to locate nearby vehicles and predict their behavior. Inspired by regulations currently being implemented that will require EVs and hybrid vehicles (HVs) to play synthetic sound, we used a Wizard-of-Oz AV setup to explore how adding synthetic engine sound to a hybrid autonomous vehicle (AV) will influence how pedestrians interact with the AV in a naturalistic field study. Pedestrians reported increased interaction quality and clarity of intent of the vehicle to yield compared to a baseline condition without any added sound. These findings suggest that synthetic engine sound will not only be effective at helping pedestrians to hear EVs, but also may help AV developers implicitly signal to pedestrians when the vehicle will yield.

As autonomous vehicles (AVs) become a reality on public roads, researchers and designers are beginning to see unexpected behaviors from the public. Ranging from curiosity to vandalism, these behaviors are concerning as AV platforms will need to know how to deal with people behaving unexpectedly or aggressively. We call these antagonistic behaviors griefing of AVs, adopting the term from online gaming, which Warner and Raiter define as "Intentional harassment of other players...which utilizes aspects of the game structure or physics in unintended ways to cause distress''. We used the term griefing (rather than bullying), as not all behavior was intended to be violent or demeaning. However, any behavior that delays an AV's journey could be problematic for AV developers and consumers. We observed ten griefing instances over four years and five studies of pedestrian-AV behavior in three countries. For each study, we modified a conventional vehicle to appear autonomous through fake LiDAR and decals saying "Driverless Vehicle''. The driver hid beneath a costume that looked like a car seat, allowing them to remain in control of the vehicle at all times while the vehicle appeared fully autonomous from the outside. Pedestrians were generally convinced of the illusion, as confirmed through interviews with consenting pedestrians and video recordings of all interactions. Full detail on the study, as well as proposed design principles to counter this behavior, will be published at HRI 2020 as a full paper. These observations build on accounts of bullying towards robots that have been previously reported in the HRI community. While AV developers such as Uber and Waymo have shared anecdotes of past vandalism, we believe this to be the first public video made available that captures the range of griefing from playful to aggressive. We hope this video stimulates conversation regarding appropriate design principles to counter griefing towards AVs. Several researchers study motivations behind this behavior, and it remains unclear how long it will take for it to naturally subside. In the meantime, AVs should be designed with this behavior in mind.

Recent research suggests that a robot's motors make sounds that can influence users' perception of the robot's characteristics. To more deeply understand users' associations with specific sonic characteristics, we adapted methods from sensory science including Check All That Apply (CATA) questions and Polarized Sensory Positioning (PSP) to tease out small differences in motor sounds in an online survey. These methods are straightforward for untrained people to do in an online setting, mathematically rigorous, and can explore a variety of subtle auditory and perceptual stimuli. We describe how to use these methods, interpret the results with several intuitive visual representations, and show that the results align with a previous study of the same dataset. We close by discussing benefits and limitations of applying these methods to study subtle phenomena in the HCI community.

Autonomous vehicles' (AVs) interactions with pedestrians remain an ongoing uncertainty. Several studies have claimed the need for explicit external human-machine interfaces (eHMI) such as lights or displays to replace the lack of eye contact with and explicit gestures from drivers, however this need is not thoroughly understood. We review literature on explicit and implicit eHMI, and discuss results from a field study with a Wizard-of-Oz driverless vehicle that tested pedestrians' reactions in everyday traffic without explicit eHMI. While some pedestrians were surprised by the vehicle, others did not notice its autonomous nature, and all crossed in front without explicit signaling, suggesting that pedestrians may not need explicit eHMI in routine interactions---the car's implicit eHMI (its motion) may suffice.

Interactions between autonomous vehicles (AV) and pedestrians remain an ongoing area of research within the AutoUI community and beyond. Given the challenge of conducting studies to understand and prototype these interactions, we propose a combined full-day workshop and tutorial on how to conduct field experiments and controlled experiments using Wizard-of-Oz (WoZ) protocols. We will discuss strengths and weaknesses of these approaches based on practical experiences and describe challenges we have faced. After diving into the intricacies of different experiment designs, we will encourage participants to engage in hands-on exercises that will explore new ways to answer future research questions.

Autonomous vehicles' (AVs) interactions with pedestrians remain an ongoing uncertainty. Studies claim the need for explicit external human-machine interfaces (eHMI) such as lights to replace the lack of eye contact with and explicit gestures from drivers. To further explore this area, we conducted a naturalistic field study using the Ghostdriver protocol to explore how pedestrians react to a simulated driverless vehicle stopping at a crosswalk in real traffic on real roads. All pedestrians crossed in front of the vehicle with little hesitation, even though we did not signal anything beyond the vehicle's stopping motion. A few were surprised at the vehicle's novelty, however most paid little attention to its autonomous appearance. The video includes demonstrative examples of the kinds of reactions we observed, which we hope will further a dialogue on the role of eHMI in AV-pedestrian interactions.

We investigate the impact of the US drone program in Pakistan on insurgent violence. Using details about US-Pakistan counterterrorism cooperation and geocoded violence data, we show that the program was associated with monthly reductions of around nine to thirteen insurgent attacks and fifty-one to eighty-six casualties in the area affected by the program. This change was sizable, as in the year before the program, the affected area experienced around twenty-one attacks and one hundred casualties per month. Additional quantitative and qualitative evidence suggests that this drop is attributable to the drone program. However, the damage caused in strikes during the program cannot fully account for the reduction. Instead, anticipatory effects induced by the program played a prominent role in subduing violence. These effects stemmed from the insurgents’ perception of the risk of being targeted in drone strikes; their efforts to avoid targeting severely compromised their movement and communication abilities, in addition to eroding within-group trust. These findings contrast with prominent perspectives on air-power, counterinsurgency, and US counterterrorism, suggesting select drone deployments can be an effective tool of counterinsurgency and counterterrorism.

Autonomous robots in the home and on the road are fundamentally changing the way we live and interact. The visual expressions and interactions of these devices are well studied; however, more could be done to learn how sound could be a deliberate (or sometimes accidental) channel of communication from autonomous systems to humans. Combining engineering design, music, acoustics, and psychology, my thesis aims first to identify how sound colors human-robot interactions, and second to design acoustic guidelines that can improve trust of autonomous systems. As case studies, I plan to evaluate real-life interactions at two scales: sidewalk robot-pedestrian interactions and autonomous vehicle-pedestrian interactions---seeing autonomous cars as large robots that we sit inside of. This work will produce a generalizable methodology that refines interfaces between humans and technology.