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1. Computational Tools for Human-Robot Interaction Design

   https://doi.org/10.1109/HRI.2019.8673221  

   Porfirio, David; Sauppe, Allison; Albarghouthi, Aws; Mutlu, Bilge (March 2019, Proceedings of the 2019 14th ACM/IEEE International Conference on Human-Robot Interaction)

   Robots must exercise socially appropriate behavior when interacting with humans. How can we assist interaction designers to embed socially appropriate and avoid socially inappropriate behavior within human-robot interactions? We propose a multi-faceted interaction-design approach that intersects human-robot interaction and formal methods to help us achieve this goal. At the lowest level, designers create interactions from scratch and receive feedback from formal verification, while higher levels involve automated synthesis and repair of designs. In this extended abstract, we discuss past, present, and future work within each level of our design approach. 

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2. Human Inductive Biases in Design Decision Making

   https://doi.org/10.1115/DETC2020-22252  

   Shergadwala, Murtuza N.; Panchal, Jitesh H. (August 2020, ASME 2020 International Design Engineering Technical Conferences and Computers and Information Science in Engineering)

   null (Ed.)

   Abstract Designers make information acquisition decisions, such as where to search and when to stop the search. Such decisions are typically made sequentially, such that at every search step designers gain information by learning about the design space. However, when designers begin acquiring information, their decisions are primarily based on their prior knowledge. Prior knowledge influences the initial set of assumptions that designers use to learn about the design space. These assumptions are collectively termed as inductive biases. Identifying such biases can help us better understand how designers use their prior knowledge to solve problems in the light of uncertainty. Thus, in this study, we identify inductive biases in humans in sequential information acquisition tasks. To do so, we analyze experimental data from a set of behavioral experiments conducted in the past [1–5]. All of these experiments were designed to study various factors that influence sequential information acquisition behaviors. Across these studies, we identify similar decision making behaviors in the participants in their very first decision to “choose x”. We find that their choices of “x” are not uniformly distributed in the design space. Since such experiments are abstractions of real design scenarios, it implies that further contextualization of such experiments would only increase the influence of these biases. Thus, we highlight the need to study the influence of such biases to better understand designer behaviors. We conclude that in the context of Bayesian modeling of designers’ behaviors, utilizing the identified inductive biases would enable us to better model designer’s priors for design search contexts as compared to using non-informative priors. 

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3. Microstructure design using a human computation game

   https://doi.org/10.1016/j.mtla.2022.101544  

   Adair, Christopher W.; Evans, Hayley; Beatty, Emily; Hansen, Derek L.; Holladay, Seth; Johnson, Oliver K. (September 2022, Materialia)
4. Decentering Through Design: Bridging Posthuman Theory with More-than-Human Design Practices

   https://doi.org/10.1080/07370024.2023.2283535  

   Nicenboim, Iohanna; Oogjes, Doenja; Biggs, Heidi; Nam, Seowoo (July 2025, Human–Computer Interaction)
5. Pitfalls of Embodiment in Human-Agent Experiment Design

   https://doi.org/10.1145/3652988.3673958  

   Hale, James; Schweitzer, Lindsey; Gratch, Jonathan (September 2024, ACM)

   The intelligent virtual agent community often works from the assumption that embodiment confers clear benefits to human-machine interaction. However, embodiment has potential drawbacks in highlighting the salience of social stereotypes such as those around race and gender. Indeed, theories of computer-mediated communication highlight that visual anonymity can sometimes enhance team outcomes. Negotiation is one domain where social perceptions can impact outcomes. For example, research suggests women perform worse in negotiations and find them more aversive, particularly when interacting with men opponents. Research with human participants makes it challenging to unpack whether these negative consequences stem from women’s perceptions of their partner or greater toughness on the part of these men opponents. We use a socially intelligent AI negotiation agent to begin to unpack these processes. We manipulate the perceived toughness of the AI by whether or not it expresses anger — a common tactic to extract concessions. Independently, we manipulate the activation of stereotypes by randomly setting whether the interaction has embodiment (as a male opponent) or has only text (where we obscure gender cues). We find a clear interaction between gender and embodiment. Specifically, women perform worse, and men perform better against an apparently male opponent compared to a disembodied agent – as measured by the subjective value they assign to their outcome. This highlights the potential disadvantages of embodiment in negotiation, though future research must rule out alternative mechanisms that might explain these results. 

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