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This paper explores the impact of value similarity between humans and AI on human reliance in the context of AI-assisted ethical decision-making. Using kidney allocation as a case study, we conducted a randomized human-subject experiment where workers were presented with ethical dilemmas in various conditions, including no AI recommendations, recommendations from a similar AI, and recommendations from a dissimilar AI. We found that recommendations provided by a dissimilar AI had a higher overall effect on human decisions than recommendations from a similar AI. However, when humans and AI disagreed, participants were more likely to change their decisions when provided with recommendations from a similar AI. The effect was not due to humans’ perceptions of the AI being similar, but rather due to the AI displaying similar ethical values through its recommendations. We also conduct a preliminary analysis on the relationship between value similarity and trust, and potential shifts in ethical preferences at the population-level.

We initiate the study of $\textit{behavioral information design}$ through deep learning. In information design, a $\textit{sender}$ aims to persuade a $\textit{receiver}$ to take certain actions by strategically revealing information. We address scenarios in which the receiver might exhibit different behavior patterns other than the standard Bayesian rational assumption. We propose HAIDNet, a neural-network-based optimization framework for information design that can adapt to multiple representations of human behavior. Through extensive simulation, we show that HAIDNet can not only recover information policies that are near-optimal compared with known analytical solutions, but also can extend to designing information policies for settings that are computationally challenging (e.g., when there are multiple receivers) or for settings where there are no known solutions in general (e.g., when the receiver behavior does not follow the Bayesian rational assumption). We also conduct real-world human-subject experiments and demonstrate that our framework can capture human behavior from data and lead to more effective information policy for real-world human receivers.

Artificial intelligence (AI) has been increasingly involved in decision making in high-stakes domains, including loan applications, employment screening, and assistive clinical decision making. Meanwhile, involving AI in these high-stake decisions has created ethical concerns on how to balance different trade-offs to respect human values. One approach for aligning AIs with human values is to elicit human ethical preferences and incorporate this information in the design of computer systems. In this work, we explore how human ethical preferences are impacted by the information shown to humans during elicitation. In particular, we aim to provide a contrast between verifiable information (e.g., patient demographics or blood test results) and predictive information (e.g., the probability of organ transplant success). Using kidney transplant allocation as a case study, we conduct a randomized experiment to elicit human ethical preferences on scarce resource allocation to understand how human ethical preferences are impacted by the verifiable and predictive information. We find that the presence of predictive information significantly changes how humans take into account other verifiable information in their ethical preferences. We also find that the source of the predictive information (e.g., whether the predictions are made by AI or human doctors) plays a key role in how humans incorporate the predictive information into their own ethical judgements.

Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a user's role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community---CaringBridge.org---where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users' peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships---patterns of repeated interactions---are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems.

Expressive writing has been shown to improve physical, mental, and social health outcomes for patients struggling with difficult diagnoses. In many online health communities, writing comprises a substantial portion of the user experience, yet little work has explored how writing itself affects user engagement. This paper explores user engagement on CaringBridge, a prominent online community for writing about personal health journeys. We build a survival analysis model, defining a new set of variables that operationalize expressive writing, and comparing these effects to those of social support, which are well-known to benefit user engagement. Furthermore, we use machine learning methods to estimate that approximately one third of community members who self-identify with a cancer condition cease engagement due to literal death. Finally, we provide quantitative evidence that: (1) receiving support, expressive writing, and giving support, in decreasing magnitude of relative impact, are associated with user engagement on CaringBridge, and (2) that considering deceased sites separately in our analysis significantly shifts our interpretations of user behavior.