Data Feminism Flashcards
Why is it important to involve women and collect gender-disaggregated data to avoid gender disparities in data?
Gender disparities in data can be harmful and perpetuate existing gender inequalities, making it important to involve women in data collection and to collect and maintain gender-disaggregated data.
The unique experiences and perspectives of women can be better represented and accounted for, helping to reduce gender biases in data and ensuring that policies and decisions are informed by accurate and inclusive information.
Flashcard 1:
Q: What is Data Feminism?
A: Data Feminism is a diverse and wide-ranging set of projects that expose and challenge sexism in data.
Example: Data Feminism might involve analyzing gender disparities in data collections and advocating for the collection and maintenance of gender-disaggregated data.
Flashcard 2:
Q: What is the “data gap” or “data divide”?
A: The “data gap” or “data divide” refers to inequalities in data, including availability, ownership, technical and human processing capabilities.
Example: Within countries, the digital divide might refer to uneven access to information and communications technologies based on factors like age, income, or race.
Flashcard 3:
Q: What is gender bias?
A: Gender bias is present in data collections,
Example: A study might find that a voice recognition system is more accurate for male voices than for female voices, indicating the presence of gender bias.
Flashcard 4:
Q: What are the principles of Data Feminism?
A: The principles of Data Feminism include examining and challenging power,
elevating emotion and embodiment,
rethinking binaries and hierarchies,
embracing pluralism,
considering context,
making labor visible, and collecting and
maintaining gender-disaggregated data
Flashcard 5:
Q: Why are gender disparities in data harmful?
A: Gender disparities in data are harmful and can be addressed by involving women and asking for their input, collecting and maintaining gender-disaggregated data, and embracing pluralism in data projects.
Example: A gender disparity in data might result in a medical treatment that is less effective for women than for men, leading to negative health outcomes for women.
Flashcard 6:
Q: What is the “Yentl syndrome”?
A: The “Yentl syndrome” refers to the misdiagnosis and poor treatment of women unless their symptoms conform to those of men.
Example: The Yentl syndrome might be observed in the misdiagnosis of a woman’s heart attack symptoms, which can be different from those of men and may lead to delayed or incorrect treatment.
Flashcard 7:
Q: What is the “representation of the world”?
A: The “representation of the world” refers to how data represents reality, and the fact that it is often created and interpreted from a male perspective.
Example: The representation of the world might be observed in the fact that professions for women are often derivative of men’s professions, and in the use of masculine language as “neutral” in some languages.
Flashcard 8:
Q: What is the digital divide?
A: The digital divide is the uneven access, use, or impact of information and communications technologies among and within countries.
Example: The digital divide might be observed in urban/rural, young/old, affluent/poor, or white/non-white disparities in access to the internet or other technologies.
Flashcard 9:
Q: What is the importance of examining power in data projects?
A: Examining power in data projects can help to identify whose priorities are being prioritized, who benefits and who suffers, and whose voices are being left out.
Example: Examining power in a data project might involve investigating who is funding the project and who has decision-making power over its implementation.
Flashcard 10:
Q: What is the importance of challenging power in data projects?
A: Challenging power in data projects can help to address systemic inequalities and promote more equitable outcomes.
Example: Challenging power in a data project might involve compiling counterdata to challenge dominant narratives or working to empower marginalized communities to own and control their own data.
Flashcard 11:
Q: What is the importance of considering context in data projects?
A: Considering context in data projects can help to ensure that data is collected, analyzed, and interpreted in a way that is appropriate to its specific context and does not reinforce existing power imbalances.
Example: Considering context in a data project might involve taking into account the cultural, social, and historical factors that shape how people interact with and understand data.
Flashcard 12:
Q: What is the importance of making labor visible in data projects?
A: Making labor visible in data projects can help to recognize and value the contributions of those who contribute to data collection and analysis.
Example: Making labor visible in a data project might involve crediting individual researchers, acknowledging the contributions of research assistants or data entry clerks, and highlighting the complex process of creating data.
Flashcard 13:
Q: What is the importance of embracing pluralism in data projects?
A: Embracing pluralism in data projects can help to ensure that data reflects the diversity of perspectives and experiences among users and stakeholders.
Example: Embracing pluralism in a data project might involve soliciting input from a wide range of stakeholders, including those who are typically underrepresented in data projects, and using multiple methods and sources to collect data.
Flashcard 14:
Q: What is the importance of rethinking binaries and hierarchies in data projects?
A: Rethinking binaries and hierarchies in data projects can help to recognize the complexity and diversity of human experiences and to avoid reproducing dominant power structures.
Example: Rethinking binaries and hierarchies in a data project might involve recognizing the diversity of gender identities beyond the binary of male/female and using intersectional approaches to understand how different identities intersect and interact with each other.
