Equitable Data Practice

The Elevate Data for Equity project aims to help change norms and practices of data use to advance equity and prevent harm to communities of color and people with low incomes. The resources below provide principles, guidance, and templates for equitable data practice.
 

Principles for Advancing Equitable Data Practice
Urban Institute, 2020

This brief presents principles for equitable data practice across the data life cycle based on the principles of beneficence, respect for persons, and justice. 

A Toolkit for Centering Racial Equity throughout Data Integration
Actionable Intelligence for Social Policy, 2020

This toolkit, focused on data sharing and integration, describes positive and problematic practices for centering racial equity across the data life cycle. In addition to examples of promising practices, it offers three activity templates for deciding who should be at the table, mapping assets and engaging community, and identifying root causes through factor analysis.

How Philanthropy Can Help Lead on Data Justice
Louise Lief, 2020

This article argues that philanthropy can advance “data justice” by incorporating new principles into its practice, leading to greater community control and results that better reflect the lived experience of communities being studied. 

Four Principles to Guide Civil Society’s Use of Digital Data        
Digital Impact, 2019

This toolkit aims to help nonprofit organizations and foundations manage data in alignment with their missions. It presents questions around technology, governance, and management across the data life cycle and offers four principles to guide use of data: permission, privacy, openness, and pluralism.       

A Guide to Incorporating a Racial and Ethnic Equity Perspective throughout the Research Process
Child Trends, 2019

This guide offers five guiding principles and discusses how researchers can apply a racial equity lens to the stages of the research process: landscape assessment, design and data collection, data analysis, and dissemination. 

Why Am I Always Being Researched?
Chicago Beyond, 2018

This publication presents an equity-based approach to research to shift the power dynamic and restore communities as authors and owners. It presents the inequities in current research practices and then offers sections for community organizations, researchers, and funders. For each group, it recommends areas of consideration before starting research: knowing the role, community and voice in setting up the study, community and voice during the study, equitable numbers for impact, and sharing results.

A Path to Social License: Guidelines for Trusted Data Use
Data Futures Partnership, 2017

Although created for New Zealand organizations, this report is relevant to all data analysts who use personal data. It recommends practices to foster trust among the people providing data and among the wider community. It discusses eight questions around value, protection, and choice that people will have before sharing their data, as well as guidance on what transparent and reasonable answers look like. 

Ten Simple Rules for Big Data Research
Council for Big Data, Ethics, and Society, 2017

This article, funded by the National Science Foundation, presents 10 principles for ethical use of big data research methods and infrastructures to reduce the chance of harm and build best practices based on the council’s consultations.

Powering Health Equity Action with Online Data Tools: 10 Design Principles
PolicyLink and Ecotrust, 2017

This guide presents 10 principles for displaying data about race. Although the guide focuses on health equity, its lessons apply to a range of social policy topics. For organizations displaying data online, the guide provides principles for framing data with a racial equity lens, including steps to applying best practices. 

Advancing Better Outcomes for All Children: Reporting Data Using a Racial Equity Lens
Annie E. Casey Foundation, 2008

This guide outlines how to produce written material that uses a racial equity lens effectively and includes tips on the overall organization and presentation of data as well as on the narratives that accompany indicators. 


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