Data donation has received attention as a more consensual means of collecting personal data for scientific inquiry and AI technology. Yet the nature of data often donated–such as harmful online messages and menstrual tracking logs–carries risk of retraumatization (the forced reliving of traumatic experience). While the well-being of data donors is considered in prior work, approaches to retraumatization remain ad hoc. We present Trauma-Informed Data Donation (TIDD): a context-specific, exploratory design framework for adapting the Trauma-Informed Approach (TIA) from the Public Health domain to data donation. TIDD was the product of a 2-year research through design process with experts on sexual violence and trauma, and observational interviews of data donors. We use a case study applying TIDD to our custom data donation platform, Ube, as an invitation for designers to consider how TIDD could be used as a malleable foundation for donation of data associated with other forms of trauma.

Data donation is a growing form of personal data collection that foregrounds consent and conscious participation of the data donor. There remains little guidance on who must consent to data donation, particularly when the data represents multiple people. We provide empirical perspectives on this question through in-situ observation and interviews (N=18) with online daters who chose to donate messaging interactions with potential sexual partners for sexual violence research. Findings elucidate two diverging perspectives. Participants advocating for ''unilateral consent'' argued that consent of their messaging partner is not necessary, in part, because the anticipated benefit of data donation superseded consent. Participants advocating for ''collective consent'' wanted both messaging partners to consent to its donation, citing concerns for privacy of, and personal relationships with, the other person. Findings suggest that collective consent interfaces should be incorporated in data donation platforms, even if not strictly required by legal regulation, to improve donation of multi-person data.

A majority of people experience trauma, spurring calls to incorporate trauma-informed approaches (TIA) from public health and social work into technology design. While technologies touted as trauma-informed are starting to propagate the literature, there persists a gap in knowledge around how design teams apply TIA and qualify their technology as adhering to trauma-informed principles. We address this through a 12-month development project with trauma and sexual violence experts to produce Ube, a data donation platform for collecting online dating sexual consent data to improve sexual risk detection AI. Through analysis of design documentation we retrospectively articulate a trauma-informed design process that evolved through the course of Ube’s development, comprising three elements for integrating trauma-informed principles: design goals that adapt the definition of TIA to the application domain, design activities that map to trauma-informed principles, and consequent design choices. We conclude with methodological recommendations to improve trauma-informed design processes.

Social computing platforms facilitate interpersonal harms that manifest across online and physical realms such as sexual violence between online daters and sexual grooming through social media. Risk detection AI has emerged as an approach to preventing such harms, however a myopic focus on computational performance has been criticized in HCI literature for failing to consider how users should interact with risk detection AI to stay safe. In this paper we report an interview study with woman-identifying online daters (n=20) about how they envision interacting with risk detection AI and how risk detection models can be designed pursuant to such interactions. In accordance with this goal, we engaged women in risk detection model building exercises to build their own risk detection models. Findings show that women anticipate interacting with risk detection AI to augment - not replace - their personal risk assessment strategies. They likewise designed risk detection models to amplify their subjective and admittedly biased indicators of risk. Design implications involve the notion of personalizable risk detection models, but also ethical concerns around perpetuating problematic stereotypes associated with risk.

Ethics have become a core consideration in human-robot interaction (HRI) due to ample opportunity for both positive and negative impact on humans. HRI literature has expounded on ways to produce ethical social robots, especially participatory design (PD) that integrates anticipated users and other stakeholders as designers themselves to ensure their values are integrated into robot design. We draw attention to the ethics of participation in robot design, distinct from the ethics of the robot ultimately designed. We propose an approach to foregrounding ethics in PD processes through co-construction of robot PD protocols with stakeholders. We call this ”pre-PD” because it entails expanding the boundaries of PD beyond the product of design (the robot) to also include the participatory activities that enable design. Contributions of the paper include: (1) a case study of pre-PD for sexual violence mitigation robots to demonstrate feasibility of stakeholders co-constructing robot PD protocols, and (2) an actionable framework for HRI researchers to use when constructing their own PD protocols with stakeholders, informed by reflection on the case study.