Citation:
D’Ignazio, C., & Klein, L. (2020). Data Feminism. MIT Press.
Personal Reflection and Application
Data Feminism astutely and powerfully demonstrates the way that intersection feminist thought and theory can empower efforts to question (and overthrow) deeply ingrained social and political hierarchies. This activist framework provides a relevant lens to my own research which advocates for access and equity in museum practice. The book's seven chapters are centered on seven tenets or principles that act as a guide for solidarity and social action. The are examine power, challenge power, elevate emotion and embodiment, rethink binaries and hierarchies, embrace pluralism, consider context, and make labor visible.
Chapters 1-4: Notes and Key Terms
Chapter 1: Examine Power
Data feminism begins by analyzing how power operates in the world.
Examining power means naming and explaining the forces of oppression that are so baked into our daily lives—and into our datasets, our databases, and our algorithm
Power - describes the current configuration of structural privilege and structural oppression, in which some groups experience unearned advantages—because various systems have been designed by people like them and work for people them—and other groups experience systematic disadvantages—because those same systems were not designed by them or with people like them in mind.
Four domains in the "matrix of domination":
· Structural domain - Organizes oppression: laws and policies.
· Disciplinary domain - Administers and manages oppression. Implements and enforces laws and policies
· Hegemonic domain - Circulates oppressive ideas: culture and media.
· Interpersonal domain - Individual experiences of oppression.
The problems of gender and racial bias in our information systems are complex, but some of their key causes are plain as day: the data that shape them, and the models designed to put those data to use, are created by small groups of people and then scaled up to users around the globe.
While equitable representation—in datasets and data science workforces—is important, it remains window dressing if we don’t also transform the institutions that produce and reproduce those biased outcomes in the first place
The most grave and urgent manifestation of the matrix of domination is within the interpersonal domain. The phenomenon of missing data is a regular and expected outcome in all societies characterized by unequal power relations, in which a gendered, racialized order is maintained through willful disregard, deferral of responsibility, and organized neglect for data and statistics about those minoritized bodies who do not hold power.
Chapter's key questions: who questions about data science: Who does the work (and who is pushed out)? Who benefits (and who is neglected or harmed)? Whose priorities get turned into products (and whose are overlooked)?
Chapter 2: Challenge Power
Data feminism commits to challenging unequal power structures and working toward justice.
Forms of action: collecting (counter data), analyzing (biased algorithms), imagining (new starting points), and teaching (the next generation).
The theory of change that motivates efforts to use data as evidence, or “proof,” is that by being made aware of the extent of the problem, those in power will be prompted to take action. However, Proof can just as easily become part of an endless loop if not accompanied by other tools of community engagement, political organizing, and protest. Proof can also unwittingly compound the harmful narratives—whether sexist or racist or ableist or otherwise oppressive—that are already circulating in the culture, inadvertently contributing to what are known as deficit narratives.
· Deficit narratives reduce a group or culture to its “problems,” rather than portraying it with the strengths, creativity, and agency that people from those cultures possess
Ideal process - from community problem to gathering proof to informed reporting to policy change—represents the best aspirations of speaking truth to power.
Data ethics - represents a growing interdisciplinary effort—both critical and computational—to ensure that the ethical issues brought about by our increasing reliance on data-driven systems are identified and addressed.
We must look to understand and design systems that address the source of the bias: structural oppression.
The key to co-liberation, which the authors offer as a goal for research projects, is that it requires a commitment to and a belief in mutual benefit, from members of both dominant groups and minoritized groups.
· Starting from the assumption that oppression is the problem, equity is the path, and co-liberation is the desired goal leads to fundamentally different projects that challenge power at their source.
Chapter 3: On Rational, Scientific, Objective Viewpoints from Mythical, Imaginary, Impossible Standpoints
Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world. The third principle of data feminism, and the theme of this chapter, is to elevate emotion and embodiment.
False binaries work to benefit the group already at the top—elite white men.
Questions to ask: How can we let go of this binary logic? First, is visual minimalism really more neutral? [NO!] And second, how might activating emotion—leveraging, rather than resisting, emotion in data visualization—help us learn, remember, and communicate with data?
"Rhetorical dimension is present in every design,” - Jessica Hullman
Rather than valorizing the neutrality ideal and trying to expunge all human traces from a data product because of their bias, feminist philosophers have proposed a goal of more complete knowledge.
Standpoint theory (Sandra Harding) - works toward more inclusive knowledge production by centering the perspectives—or standpoints—of groups that are otherwise excluded from knowledge-making processes. Other key theorists: Donna Haraway (feminist objectivity) and Linda Alcoff (positionality).
· Disclosing your subject position(s) is an important feminist strategy for being transparent about the limits of your—or anyone’s—knowledge claims
· The belief that universal objectivity should be our goal is harmful because it’s always only partially put into practice
Deliberately embracing emotions like wonder, confusion, humor, and solidarity enables a valuable form of data maximalism, one that allows for multisensory entry points, greater accessibility, and a range of learning types.
If there is any single rule in design, it’s that context is queen. A design choice made in one context or for one audience does not translate to other contexts or audiences.
Rebalancing emotion and reason opens up the data communication toolbox and allows us to focus on what truly matters in a design process: honoring context, architecting attention, and taking action to defy stereotypes and reimagine the world.
Chapter 4: What Gets Counted Counts
Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression. Principle: Rethink Binaries and Hierarchies
What is counted—like being a man or a woman—often becomes the basis for policymaking and resource allocation. By contrast, what is not counted—like being nonbinary—becomes invisible.
Before collective oppression can be identified through analyses the data must exist in the first place.
Historical fact worth noting: Before the eighteenth century, Western societies understood race as a concept tied to religious affiliation, geographic origin, or some combination of both. Race had very little to do with skin color until the rise of the transatlantic slave trade, in the seventeenth century.
Classification systems are essential to any working infrastructure but have their limitations. Flawed classification systems are not only significant problems in themselves, but also symptoms of a more global condition of inequality
Term: paradox of exposure: the double bind that places those who stand to significantly gain from being counted in the most danger from that same counting (or classifying) act. (for example, illegal immigrants).
Questions about counting must be accompanied by questions about consent, as well as of personal safety, cultural dignity, and historical context.
Acts of counting and classification must always balance harms and benefits. They are powerful tools in the creation of knowledge but are also tools of power in themselves.
When a community is counting for itself, about itself, there is the potential that data collection can be not only be empowering but also healing.
An intersectional feminist approach to counting insists that we examine and, if necessary, rethink the assumptions and beliefs behind our classification infrastructure, as well as consistently probe who is doing the counting and whose interests are served.