Hi CHI, I am really excited to give you all a brief overview of our paper, Getting Ourselves Together: Epistemic Burden and Data-centered Participatory Design Research.
I'm Jennifer Pierre and I'm a researcher at Google and I’m presenting today on behalf of myself and my co-authors: Dr. Roderic Crooks from the University of California, Irvine, Dr. Morgan Currie from the University of Edinburgh, Dr. Britt Paris from Rutgers University and Dr. Irene Pasquetto from University of Michigan.
So, in our paper, we use the concept of epistemic burden as a tool for highlighting the types of research harm that can take place in participatory design research, especially when this work engages with minoritized communities and activists using a data-centered approach. We really see this as contributing to the intersection of HCI and data justice.
We see this also as building on a lineage of recent excellent work in the HCI space highlighting some of the different harms around extraction and potential challenges in really serving and equitably partnering with the groups that we're intending to serve and collaborate with.
Epistemic burden is a concept that really pulls together multiple related ideas around the way in which privileged groups further injustice and potential exploitation through the relationships with oppressed and marginalized community groups.
There are three major outcomes that come out of epistemic burden.The first is diverting energy away from ongoing activities among community groups and minoritized communities to educate the privileged.
The second is responding to stories of the oppressed with hostility so this is not just outright hostility or explicit hostility, but also things like undue skepticism or distrust. And lastly receiving social recognition from dedication to dismantling oppression over the channeling and funneling of that recognition to the affected groups.
Our research questions for this work ask how we as academics and design researchers can avoid perpetuating the cycle of burdening community organizations through our approaches to data-centered design research.
So this really entails how we can use the concept of epistemic burden to proactively help us identify the different types of harms that can take place in participatory design research agendas and how we can analyze epistemic burden to steer our approach to the least-harmful, most appropriate methods.
We use two case studies to answer these questions and they serve as empirical evidence of epistemic burden at work and the potential of epistemic exploitation as well as an opportunity for experiential reflection and interpretation.
The first case study we investigate in our work is the UCLA POIH Project or “Police Officer Involved Homicide” Project, which is a Hackathon held at UCLA to investigate gaps in official records of police data.
Some of the key tensions that we saw here were that we, as researchers, set the agenda and assumed that data was not the center and in retrospect, this really clashed with the distrust that we saw in community groups around the official data records and the use of data for different means. So we see this is really encapsulating the first two types of epistemic burden.
So driving attention and energy and resources away for some of the ongoing work that really didn't have to do with data-centered approaches or data-centered work and a minimizing of the actual existing distrust among those community groups around even the work of completing or helping to resolve gaps in official data records. So considering that and reflecting on that, what could we have done differently?
We could have let the organizations set and lead the project agenda which would have included aligning altogether on definitions and values of data particularly and crucially including a grappling of data non-use and the appropriateness of that in different ways as well as working more concretely towards ongoing goals and existing work within those communities.
The UCI Database Project was an NSF-funded undergraduate project that was towards the goal of building up a database of data justice grassroots organizations.
And some of the key tensions that we saw here were not enough credit explicitly for sharing and producing data and the work entailed within that and that paired really with insufficient follow-up after initial interviews to really provide a substantive way of balancing the types of gains, especially material gains that were being experienced by both members of this partnership so both community organizations and the researchers engaging in this work.
And this really speaks to and symbolizes the third type of epistemic burden, which really explains the type of unbalanced recognition that can be brought forth through epistemic burden in this type of work.
So what could we have done differently here is add organizations and organizers as co-authors and presenters and really citing their work and data as they desire and with appropriate consent as well as ensuring that there is a substantive way to bring forth material gains and balance the type of recognition that different players within this partnership are gaining through things like writing, organizational funding directly into proposals, and offering things like free grant writing training to groups.
So, to summarize, we see epistemic burden as a theoretical tool for identifying work that's needed before engaging in data-centered participatory design research to take a proactive stance to avoid perpetuating cycles of extraction and harm and focus instead on minimizing burden. Thank you.