The Imperative: Assess & Address Barriers to Benefits & Opportunities
President Biden’s executive order (EO 13985) on racial equity instructs the head of each agency to review “policies and programs to assess whether underserved communities and their members face systemic barriers in accessing benefits and opportunities.” Each agency must complete this review “within 200 days [and file] a report with the Assistant to the President for Domestic Policy (APDP)”.
The goal when undertaking these kinds of analyses is to ensure relevant and actionable findings; however, in Data Society’s experience with work of this nature, tackling underlying forces that sneak bias into data, programs and analyses will be challenging without expert guidance. Bias can be introduced at many points of data collection, and expert understanding of the data collection process, as well as analysis techniques, is necessary to ensure that insights are presented to decision makers accurately, free from bias or, at minimum, with appropriate context.
The goal of this white paper is to share Data Society’s experience and perspectives to help agency leaders comply with this EO faithfully, objectively, and with a mind towards improving the mission’s effectiveness.
The First Step: Reimagining Success
At the outset of any data-driven project, best practice would be to define, even reimagine, what success looks like. To the program manager, success can be finding no bias and returning to mission-critical activities. But to the President, department executives, and the public, success looks different. To them, success relies on accurate data collection and analysis that unequivocally demonstrates where bias does exist, and in what form. As a result, the government can implement appropriate and (often simple) corrective action to ensure fairness and equity across the board.
Considering the Chief Data Officer (CDO) of the United States is among the key stakeholders receiving this report, this objective, data-centric approach is the only credible approach.
Human Psychology Predisposes Past, Non-Data-Driven Practices to Bias.
With respect to the executive order, success lies in looking beyond the apparent symptoms of bias, and into the heart of the data story, to illuminate underlying structural issues. For example:
- Historical data and legacy processes can contain stereotypes and representations of convention rather than be reflective of reality (e.g., predicting criminal recidivism based on historical demographics of incarcerated populations assumes that everyone is justly incarcerated and therefore policing reforms are not necessary).
- The existence of prior policies or practices, perhaps in generations past and long overwritten by new ones, that still act as real barriers to entry and opportunity (e.g., urban housing and development trends reveal the scars of historical redlining 50 years later).
- Terms, approaches, and tools that may work perfectly in one cultural context may communicate disincentives to others (e.g., assessing vaccination efforts across demographics without cultural, local or historical context will bias resulting conclusions from that data).
Bias can be measured and addressed quickly, dispassionately, and accurately — with the right support:
Reporting should be clear, objective, and action-oriented.
Partner with Data Society.
- Food scarcity emergency management through our work with the World Central Kitchen
- Oversight of global financial funds at scale, monitoring real-time data flows to mitigate risk and better address development needs, for the Inter-American Development Bank
- Global sustainability efforts, identifying greater efficiencies, reduced cost and waste, and reduced energy consumption for various large-scale manufacturers and federal agencies.
Data-Driven Mission Effectiveness
Jaime De La Ree
Jaime De La Ree is the acting VP of Business Development at Data Society with seven years of experience in technology consulting. Before joining Data Society, Jaime worked in mobile technology consulting, supply chain process transformation, and fundraising technology implementation. When he’s not helping Data Society build partnerships to improve data capabilities, Jaime spends his time with his wife, son, and daughter and in the remaining time refines his skills as a carpenter, car enthusiast, and outdoorsman.
Nisha Iyer is the VP of Technology at Data Society. Her roots are in Data Science and AI where she has 8 years of experience within the industry. Over the last five years she has been charged with growing and managing diverse tech teams that include: data scientists, engineers and UI/UX designers. Nisha has a passion for innovation and is always thrilled to learn and grow from her team members. When she is not innovating at Data Society, Nisha spends her time with her partner Alli and their two dogs Rex and Picci. She enjoys eating amazing food, being outside and building community.