On Echo Chambers and Challenging Assumptions: Responsible Data in Fragile and Conflict Settings
There has been a growing realisation that responsible data practices are important, need improvement, and can be especially critical in fragile and conflict settings. Decision-makers at international organisations in Brussels or New York or Geneva seem to understand that there is a systemic problem with responsible data in humanitarian settings. This is excellent, and is in part due to the diligent work of many members of the Responsible Data community.
But although we now know that technology and data are not neutral, we must also recognise that what is meant by ‘responsible’ data is not neutral either.
Most of the discourse around responsible data in humanitarian contexts focuses on the relationship between aid providers and direct users or recipients of aid. Local NGOs, civil society and communities are not usually part of the conversation. And unfortunately, for all of the immense value of this community, they are not often part of our conversations, either. This reflects and exacerbates deeply-rooted power imbalances and top-down engagement strategies, reinforcing existing silos and echo chambers.
Take, for example, the need for additional data in this field. We recognise that to ‘do no harm’ we need to learn what the harms actually are. And we readily admit that so far, we have tended to act based on what we think the risks are.
As Ben Parker rightly argues, this is cause for data professionals to ask themselves, “Have we even thought of all the possible ways this could go wrong and, if we haven’t, who could help us think it through?” There is a plethora of helpful guidelines on data in humanitarian contexts. They include the Principles for Digital Development, the Harvard Signal Code, UN OCHA’s report on Humanitarian Data Ethics, and the ICRC’s Handbook on Data Protection in Humanitarian Action. But, as Parker argues, guidelines are not enough, and data professionals need to be personally dedicated to a responsible data approach.
And they must also take concrete steps to make sure that their responsible data approach reflects the values of affected communities. To be ‘responsible’, data practitioners must meaningfully engage with civil society or community leaders in humanitarian contexts – especially those who are most impacted by humanitarian data practices – to help them understand all of the possible ways they could go wrong.
So while it is true that there urgently needs to be more research and evidence in this area, these must be grounded in participatory and inclusive methods that center the perspectives of local communities. Otherwise, we cannot arrive at an understanding of what responsible data means in these contexts.
How can we do this in practice? Taking a justice-centered approach, as reflected by innovative work on design justice, applied data justice, or data feminism is a useful starting point for improving responsible data practice. These diverse approaches have a common base of good practices, including:
- Designing collective solutions
- Accounting for structural inequities and power relations
- Focusing on marginalised people and communities whose knowledge and data often get ignored
- Considering the political economy of knowledge production
- Ensuring meaningful participation in decisions, and
- Recognising community-based traditions, knowledge, and practices.
It is imperative to be continuously challenging our own assumptions about what ‘responsible data’ means in fragile and conflict settings by listening to and learning from affected communities. We need to start mainstreaming these concepts into our work, internal and external advocacy, and day-to-day decision-making.
This likely requires a community re-think, and an opportunity to streamline our definition of what a justice-centered approach to responsible data looks like. It will also require a concerted effort to reach beyond our networks, set aside time and budget for inclusive consultations, and to become comfortable with shifting our programmatic plans – sometimes perhaps radically – to incorporate diverse views into responsible data work.