Those who work with data and engage in research practices are in a powerful position to heal the communities we work in, but we are also at risk of causing harm. Data is powerful and should be treated with immense care—it shapes resource allocations and perceptions of truth and reality. Data Violence occurs when the processes for conducting research and research practices cause harm (violence) in the individuals, communities or systems we intend to benefit.
Why is it so Hard to Identify Data Violence?
Data Violence is complex and sometimes seemingly invisible. Too often, the applied research field does not address whether our projects cause harm. Even though many applied researchers support social justice initiatives and aim to advance equity. Acknowledging we are causing harm doesn’t feel good, so we tend to avoid it as a field. The causes of data violence include systemic factors like colonialism, racism, white supremacy and homophobia. Power structures that perpetuate data violence and harm are built into the processes, structures and systems. In which the project takes place. e.g. academia, funding institutions, traditional research methodologies. So, conventional research practices and traditions of data rigor and professional practice, funder and institutional agendas and privileging the status quo cause data violence.
We often think of harm reduction related to data at the individual level: individual consent forms and institutional review boards that explore personal harm but not data violence and systemic harm.
Traditionally, the accountability for harm lies with Institutional Review Boards (IRBs). The IRB process is too top-down when assessing harm. Cahill (2007) captures that researchers are suggesting IRBs’ focus on institutional from liability and risk. IRBs are grounded in the medical model, not in applied research. IRBs are ill-equipped to assess Data Violence for non-medical, community-based researchers. As a result, harm and exploitation of communities continue despite best intentions.
To reduce data violence, we need a paradigm shift wherein practitioners acknowledge that data collection is an extraction. A withdrawal and a cost to the community. It takes community data, insights, and perspective from community ownership to a place of researcher ownership.
1. Harm can Exist at all Stages of a Project
Harm occurs early in our projects when the focus and scope centers funder agendas and timelines over the community whose needs are seen as fixed, which is unrealistic. RFPs communicate a problem to be fixed instead of strength-based, and the focus and scope center funder/institutional agendas. Practitioners often ignore past research harm that may have occurred within the community. Quantitative-centric notions of research rigor are valued over qualitative descriptions and too rigid definitions of “evidence” are inconsistent with community definition of evidence via experience of Data Violence.
2. Challenges During Data Collection
During Data Collection, communities often don’t have opportunities to speak to the complexity of their experience, and inquiries can be insensitive, triggering, or retraumatizing. Requesting data from community members whose basic needs are not met is problematic, and sometimes, researchers mandate data collection to receive service or benefit, which causes harm. Exiting the community after data extraction, dehumanizes and reinforces the paradigm of “researcher” and “subject.”
3. Analysis and Reporting: Minimizing Complexity Data Violence
As we analyse date and write our reports, too often, we minimize the complexity of community experience, don’t recognize bias in our analysis, and don’t give enough attention to root causes and systemic factors of Data Violence. Government/official standards limit definitions/categories used in data collection and analysis (for example, definitions of poverty, race, etc.). We often decontextualize and aggregate data across disparate programs or communities so that it loses important contextual nuance and meaning. We uphold traditional notions of research rigor wherein “expert” can only perform data analysis. Researchers control narrative reporting and funders/organizations ascribe the report format to suit their agendas over the needs of community-based agencies or communities.
4. Ethical Considerations and Language Use Data Violence
Exiting the community after data extraction can dehumanize and reinforce the paradigm of researcher and subject. The researcher owns data despite coming from the community, and researchers control the production of collateral materials with little community input. Researchers do not discuss if/when/how research findings will be available to the community, and published reports are not made accessible. Projects that define completion and success by report or publication completion and the project is closed out without consideration of how researchers can sustain the impacts achieved. Researchers need to assess if the project has caused harm to the community.
5. Addressing Language and Compensation Disparities
Using harmful language within the research process. Using monolingual/English-only materials throughout the project. Not compensating communities perpetrates power dynamics and dictates that researcher time is greater than community time.
We Can all do Better
Luckily, to work against Data Violence, there is a concept of harm reduction. Wherein applied researchers can take great care to reduce harm (violence) in our work. We can reduce harm by incorporating Diversity, Equity, and Inclusion principles into the “DNA” of research practices. We can center the goals/needs of the people in communities over our project’s goals and still have successful project outcomes. Acknowledging and identifying harm is a crucial first step toward making our practices less harmful and more equitable, followed by engaging in practical research harm reduction strategies. Ideally, we will also engage in healing, restorative work, and harm accountability. Engaging in harm reduction practices in our work does take extra effort. Still, the positive impacts can be far-reaching toward building trust-based, reciprocal relationships and genuine care in communities.