Data Bloom’s Stance on Equity
At Data Bloom, equity is always front of mind. We actively work to understand the limits of our perspective and uncover personal biases through workshops, education, and conversations.
Equity plays a major role within our data work. We are invigorated by the potential of data, statistics, and algorithms to eliminate bias and empower disadvantaged communities. We recognize that the improper use of data and data tools can be oppressive and perpetuate biases (for example, as done with redlining, predictive policing, and risk-based sentencing). At Data Bloom, we continue to learn and think deeply about how data and data tools can be inequitable, and actively avoid these unjust data practices. We know that:
- Collected data is never completely representative. We always seek to understand who is represented in our data and how they are being represented.
- Data analysis requires decisions, and these decisions can lead to biased results. For example, one may need to choose which demographic information should/shouldn’t be included in the analysis. We focus our analysis decisions on what is fair and just.
- Interpreting results is never impartial. As an example, one person may interpret a result as evidence of problems among individuals/communities while another may interpret the result as evidence of a systemic issue. We seek interpretations which are holistic and impartial, while acknowledging potential limits of our own interpretations.