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Technologies such as voice assistants can aid older adults aging in place by assisting with basic home and health tasks in daily routines. However, currently available voice assistants have a common design-they are vastly represented as young and female. Humans may apply stereotypes to human-computer interactions similarly to human-human interactions. When stereotypes are activated, users may lose trust or confidence in the abilities of the device, or even stop using the device all together. The two purposes of this study are to 1) investigate if users can detect the age and gender of voice assistants, and 2) understand the extent to which a voice assistant’s perceived gender, age, and reliability elicit stereotypic responses. A series of health-related vignettes will be utilized to assess perceptions of and stereotypic responses toward voice assistants in younger and older adults. In line with previous research examining healthcare agents (Pak et al., 2014), we hypothesize that voice assistants with younger male voices will be rated as more trustworthy and that high reliability will have a positive impact on ratings of trust.

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Consumers are increasingly interacting with automated systems for various purposes (e.g., smart speaker, vehicle control). One of the most popular ways they communicate with these systems is via the spoken auditory channel (listening and speaking). However, most of these systems tend to default to a female voice. In addition, many specifically use a White-sounding female voice. While prior work has investigated how attributes of age and gender influence perceptions of voiced automation, an additional vocal attribute of humanness has not been well researched -race. The specific goal of this project is to examine how more inclusively designed AI voices (e.g. exhibiting attributes of race) affect Black users’ attitudes toward autonomous technology. Results are expected to show more preferential attitudes when automation exhibits similar vocal attributes to the user. Findings will add to our understanding of how a deliberately more inclusive design affects an underrepresented population’s attitudes toward technology.

Complexity can be characterized at numerous levels; physical, perceptual, and cognitive features all influence the overall complexity of an informational display. The Human Performance Test Facility (HPTF) at the U.S. Nuclear Regulatory Commission (NRC) develops lightweight simulator studies to examine the workload induced by various control room-related tasks in expert and non-expert populations. During the initial development of the lightweight simulator, cognitive complexity was defined based on the number of elements in each control panel. While the number of items roughly maps onto information density, it is only one of several features contributing to display complexity. This study is a follow-up to the original complexity evaluation and includes an initial characterization of the perceptual complexity of a set of control panels in their original (i.e., unmodified) and modified (for cognitive complexity reduction) forms. To assess perceptual complexity, a 3-dimensional approach was developed. The control panel displays were assessed using common measures of physical complexity (e.g., edge congestion, clutter, symmetry), performance-based measures (reaction time and accuracy for target identification), and subjective impressions using a survey adapted from a similar FAA assessment of air traffic controller workstation display complexity. Overall, the results suggested that clutter and symmetry were associated with target identification performance; participants interacting with high symmetry-low clutter displays identified target controls faster than those interacting with low symmetry-high clutter displays. Survey results tended to follow the same pattern as the physical and performance-based results; however, these patterns were not statistically significant, likely due to the small sample size. These initial results are a promising indication that the physical and performance-based measures were valid for assessing display complexity and that they are sensitive to differences in complexity, even with smaller samples. The physical and performance-based measures may be good candidates for human factors validation of future system designs - they are quick and easy to administer while providing a holistic sense of display perceptual complexity. Like other types of surveys, surveys for display complexity often require large samples to detect meaningful differences between groups. System designers and other stakeholders may want to consider alternative strategies, such as physical system measurement and characterization using performance-based methods if the user base is small or designs are in the early stages of development, requiring quick answers and an iterative approach to evaluation.