Anti-Racist Results-Based Accountability (AR-RBA)

Ibram X. Kendi defines anti-racism as a powerful collection of policies that produce or sustain racial equity between racial groups. Anti-racism is based in the truth that racial groups are equals in all the ways they are different.

In Anti-Racist Results-Based Accountability, we center race because it is foundational to this country’s history and how individuals experience systems. We also focus a lot on race because it is the single greatest predictor for a person’s health, wealth, and overall well-being. This loving focus on race isn’t at the exclusion of other identities, for each and every one of us are an intersection of many beautiful identities.

The Three Core Principles of AR-RBA

1. Creating a Data Culture
   Applying AR-RBA transforms a punitive data culture focused on errors to transparent, non-punitive data design, analysis and use culture. This means:
   
   
   • Shifting to consideration of data for its value for learning about impact
   • Acknowledging that data isn’t neutral and neither are those of us interpreting it
   • Accepting that data can come in many forms and valuing multiple kinds of data
   • Moving from extracting data to sharing it with the community, regardless of outcome - data and information are power, and need to be shared to build trust even when it’s inconvenient
   • Using data to inform practice (data-driven decision making), with the understanding that not knowing continues harm

2. Analyzing the Root Cause
   To find a root cause we have to look at our data and ask the question “why?” And then ask it again and again and again. This is a narrative practice that doesn’t “prove” or blame communities of color for our institutional failures and structural designs. Instead, the focus is inward and we own the work.

3. Participatory Strategy Development
   Ensuring that power is accounted for and all parts of the process are designed and implemented with BIPOC decision-making at the center - “nothing for us without us is for us”. Those closest to the problems are closest to the solutions and usually farthest from the resources.

Two Kinds of Accountability

Anti-Racist Results-Based Accountability provides accountability at both the population and program level by measuring outcomes rather than just outputs. AR-RBA is used by communities to improve quality of life AND by organizations to achieve the best possible outcomes for our clients; it’s a framework that unites big-picture strategy with people-driven solutions.

AR-RBA is used by entire communities to improve quality of life by focusing on areas of deep inequity. Communities use big-picture indicators to reveal inequities on the population level. The only way to change inequities at the population level is to work in deep relationship and partnership with and across the communities that are experiencing the greatest inequities.

Data-Driven Decision-Making

Data-driven decision-making is part of the data culture created by Anti-Racist Results-Based Accountability. Data is more than numbers on a page. Data is stories. Data comes from all five of our senses. We see it, we taste it, we hear it, we touch it, smell it. At its best, data offer a holistic picture to inform and direct our decisions.

We don’t collect data just for a funder or a boss, or to sit in an unread report. We are collecting and using data to inform our ongoing work and choices with a through line to impact. 

To that end, AR-RBA pulls in and embraces all of our voices, but especially the voices of the people closest to the problem, who are most impacted by inequities. Those closest to the problem are often closest to the solution.

Since we know that our systems and institutions will default to benefiting white people, we have to assume that if we are unaware of our impact we’re causing or perpetuating harm in Black, Indigenous, Asian and local communities of color. A critical part of living into a data culture that has integrity is to see what impact our work is having for Black, Indigenous, Asian and local communities of color. No data point or even set of data points will be perfect, but data can reveal whether or not our strategies are making a difference in our community. To that end, AR-RBA demands that we stay open to the idea that the programs and strategies we’re using might not be the right ones, and might, in fact, exclude the very communities we’re trying to help.