Our Core Values
Commitment to the Protection of Individuals and their Data
OADS will uphold the responsible stewardship of any Georgetown data assets and systems in our purview or in our own operations.
A Focus on Diversity, Equity and Inclusion
Every function of OADS—from reporting to survey analysis, from data governance to strategic project selection—can benefit from an inclusive approach that integrates a diverse array of perspectives. To align its priorities and processes with Georgetown’s overall goals of inclusivity, OADS will:
- Proactively ask for and consider feedback from varied stakeholders—an approach that applies to all phases of the data life cycle, including project planning, data collection, data access, research design, algorithms/use of statistical tools, and dissemination of findings.
- Ensure that the process of choosing and prioritizing the large-scale research/analytics projects in OADS’s portfolio is transparent and inclusive.
- Encourage participation in data collection efforts (surveys, focus groups, etc.); when identifying samples, focus on including a sufficient number of individuals from subgroups of interest.
- Develop data collection tools and processes that are respectful and responsive to the needs of different groups; ensure that groups see themselves represented when asking for demographic and individual background information (e.g., having non-binary gender options and multiple racial and ethnic categories).
- Analyze and report data that can help understand differences in educational outcomes and other metrics; assess whether programs, policies, or practices impact groups differently.
- Develop predictive models for the Georgetown population in a way that ensures a diverse sample, recognizing that factors predictive for one population may differ from the factors that are predictive for another.
- When collecting and analyzing data on student diversity, focus not only on composition, but on engagement, inclusion, and achievement.
- Examine data and analysis for any evidence of errors or biases.
- Build organizational capacity and practices for continuous dialogue and reflection, including ongoing training on potential sources and implications of bias in statistical modeling, machine learning, and data management.
Integrity of our Work
Collection, analysis, and representation of data are guided by ethical principles of honesty, accuracy, confidentiality, integrity, and replicability.
Clarity of Communication
OADS will strive for clarity in explanation of data, processes, outcomes of analysis, and parameters for use.