How does DPA promote gender data literacy in each dimension?
1. Context & Concepts:
This is the foundational dimension towards becoming gender data literate. The goal of this dimension is to (1) decode key terms and buzzwords in the gender data landscape, (2) discuss gender data within the political context of the post-2015 SDGs framework and data for social good, and (3) translate gender issues into specific data-driven objectives and actionable plans.
Decoding gender related terms requires the understanding of gender-related fundamental concepts (e.g., gender, gender identity, gender equality, sex, and sexual orientation). These terms and concepts are explained in the DPA Gender 201 Course, which is a free online course by DPA with support from Meta and hosted by the TechChange platform.
2. Methods & Tools:
The key goals of the second dimension towards gender data literacy are to (1) understand existing methods and tools used to leverage gender data, (2) assess data representativeness, biases and insights within data-driven approaches and methods, and (3) identify applicable tools by assessing the value added of gender data for specific development challenges.
DPA uses a mixed-methods methodology in their gender data projects while applying the feminist principles defined by Catherine D’Ignazio and Lauren F. Klein. Such methodology could integrate both quantitative and qualitative research methods, as well as traditional and non-traditional data sources collected or accessed at different stages of the project, with a participatory approach involving stakeholders that provided feedback and input throughout the entire project. The following projects were highlighted by DPA:
3. Design & Strategy:
The third dimension aims to (1) identify individual and organisational objectives towards a gender data strategy, (2) understand how to operationalize gender data in projects, partnerships, and policies, and (3) recognize individual and organizational next steps towards the best use and application of gender data.
An illustrative example towards achieving gender data literacy is a DPA project in collaboration with GIZ’s “FAIR Forward - Artificial Intelligence for All” initiative, financed by the BMZ. The DPA carried out an assessment that examined how the initiative and its partners have embedded the gender perspectives into their projects, into the AI training datasets created by the partner organizations and into the design, scope and outputs of the project. The DPA analysed:
- Integration of gender perspectives and indicators into the design and M&E of projects
- Potential gender bias in the AI training datasets created by partner organizations
- Inclusion of gender differentiated impacts indicators in capacity building activities and trainings
4. Ethics & Engagement
According to DPA, this aspect – though critical - is often overlooked in the usage of data. The goals of this dimension are to (1) identify models for prioritizing inclusivity, transparency, and accountability in data-driven public-private-people partnerships, (2) articulate and assess ethical, privacy and legal implications of gender data applications, and (3) understand key principles for effective data communication and storytelling.
When ethical aspects are not considered, there is a risk to cause more harm to the already vulnerable communities. Therefore, ethical standards should be applied based on previous field work and with the aim of reinforcing the key five principles of working with vulnerable populations: justice, non-maleficence, no-stigmatization, confidentiality and transparency.
One possibility to promote ethics and the necessary engagement is to implement a Council for the Orientation of Development and Ethics (CODE), an innovative participatory methodology developed by DPA, applied in more than ten projects in 2020. It is a group of independent stakeholders from diverse sectors (civil society, government, academia, etc.) who voluntarily share their expertise throughout the project. As an advisory group, it provides oversight to ensure a project abides by key ethical principles. The key goals of CODE are to (1) adequately understand the context of analysis, (2) make ethical use of (gender) data and findings and (3) allow for the inclusion and participation of the communities involved in the projects.