My goal for this course is to give an overview of the diverse range of areas and subtasks that fall under the interdisciplinary field of data science. While much of our time is focused on technical details underlying exploratory analysis, in this second unit of the course we will also integrate other aspects that are important to consider when working with data. If you are interested in exploring these further, there are many other UR courses that further expand on these questions, such as the inferential modeling covered in DSST189 and predictive modeling in DSST389. The latter course also spends much more time focused on the presentation and distribution of data.
Another important part of data science is understanding the bigger picture of how to determine a research question in the first place and what to do with the result of an analysis once is has taken place. Many of these details are domain specific and you’ll probably learn more about them in the methods classes offered such as digital humanities, econometrics, and quantitative psychology. In addition to these domain-specific elements, there are also larger questions about data ethics, data justice, and the role of data in our larger society that should be a part of any data science work we partake in. These issues are studied at length in the course RHCS345 (part of the Data Science minor). Here, I want to spend some time discussing how these larger questions should influence the kinds of work we perform in DSST289.
My notes for today are synthesis of two important pieces or scholarship which establish, respectively, the concepts of “Data Feminism” and “Data Sheets”. Both are freely available online:
You do not need to read the original book and article for this class (though both are well worth a read when you can find the time). For the purpose of this semester, we can rely on the notes I have prepared below.
The concept of Data Feminism was introduced in a recently published book with the same name by Catherine D’Ignazio and Lauren Klein. They describe the idea as follows:
A way of thinking about data, both their uses and their limits, that is informed by direct experience, by a commitment to action, and by intersectional feminist thought. The starting point for data feminism is something that goes mostly unacknowledged in data science: power is not distributed equally in the world.
The book is filled with thought-provoking examples and hundreds of citations to scholarship across many fields. While very clear and well-written, I find it be a difficult text to read if you are not already somewhat familiar with the basic principals of intersectional feminist thought that the authors are drawing on. Even more difficult is the practice of taking the concepts in the text and turning them into actionable things that we can put into our own data science practice. My goal in the notes here is to try to summarize their ideas and turn them into concepts we can draw on in our work throughout the rest of the semester.
The book defines a set of seven principles. I have grouped these into the three larger categories that are described below.
If you have no prior background in intersectional feminist thought, you mighy presume that it has something to do with the study of gender and sex- or gender-based inequalities. And, it is the case that these questions are a core part of the field. At the same time, D’Ignazio and Klein’s use of the term feminism is intended to be even broader, which they explain as follows:
[W]e employ the term feminism as a shorthand for the diverse and wide-ranging projects that name and challenge sexism and other forces of oppression, as well as those which seek to create more just, equitable, and livable futures.
As such, the principles are not so much a question specifically about gender but rather concerns about power. What structures create and reinforce power? How is power being used? How can we challenge unequal power and work toward reflixive and equitable systems of justice?
The connection to our work comes from the fact that data is an ever-increasing structure of power in our society. Data affects our lives in many ways, from major life events such as college admissions and hiring practices, to day-to-day occurances such as the availability, pricing, and marketing of practically every good and service that we spend money on. Insurance prices, home mortgage rates, and airline prices have long used complex algorithms built on large datasets; these practices are now becoming the norm across almost every industry.
While data has become an important source of power, it is not a source of power equally available to everyone. A lot of data is expensive to produce and store and therefore is only available to large, rich institutions such as certain government agencies, large companies, and research universities. Some forms of data, likewise, cannot be collected equally by everyone due to legal and legistical barriers. As an important example in our current moment, Google became what it is today by building a better search engine by crawling across the internet to analyze to content of individual websites. Today, it would be impossible for another company to start a competing service because of barries Google has put up to re-collecting that kind of data.
Even when it possible for everyone to collect data, not all entities have the same level of power that they can wield with their datasets. Datasets created and published by institutions such as the United Nations instantly gain a level of credibility that is difficult or impossible for other groups to establish for their own datasets. Similarly, when corporations and governments build algorithms with their own curated datasets, those data gain a special level of power that can rarely be questioned or easily challenged.
So how can this principal lead us to better science practices? There is no simple answer to this question and it is something that we will return to throughout the rest of the semester. Two things we can try to do, however, are to identify ways that the data and analysis that we are using and creating could be used as a form of power while also doing our best to avoid creating and reinforcing unequal power structures. As an alternative, we should privilege projects, data sets, and analyses that are guided by principles that challenge these existing structures while working towards a more equal society.
Our second summarized principle of Data Feminism is the concept that data is never neutral of objective. Depending our academic background, this concept may range from either surprising or cliché. Let’s try to break this idea down a bit to see how it can lead us to better data scientists.
Where the first principle focused on the unequal power structures created by the use of data, here we want to focus on the more specific (but closely related) role of the data itself. Here is a non-exhaustive list of some of the ways that we can understand data as never being neutral of objective:
Let’s take one example that helps illustrate some of these points from two different angles. In 2018, a team from MIT led by Joy Buolamwini produced a dataset of photographs from 1270 unique individuals from a diverse set of skin types and racial backgrounds. The team applied face detection and recognition algorithms from a variety of open-source and commercial providers and demonstrated that existing algorithms performed significantly worse when applied to this set than when used to label a collection of individuals they perceived to be White. The implication was that the dataset used to train these algorithms consisted of overwhelmingly White faces and individuals living in the United States and Europe.
The example from Joy Buolamwini is a great example of the first principle in action. A data scientist collected and analyzed data to identify and challenge a problematic power structure. Likewise, from her work it is not too difficulty to see that the previously existing datasets for face detection had some problematic biases and were far from neutral or objective. At the same time, what about Buolamwini’s dataset? It is not entirely neutral or objective either. It focuses primarily on the Black/White binary, with no representation of faces from Asian or Indigenous communities. Also, the research group elected not to make their dataset freely available. On one hand this helps maintain it as a set that can check biases in other algorithms and avoids potentially problematic issues of distributing human images, such as the potential tokenization of the individuals involved in the study. On the other hand, holding back the data prevents others from using it to do the kind of diversification that their paper called for. Clearly there is no easy answer and I would not say that either option is truly neural or objective.
So how can we put this principle into action in our data science practice? Because there is no way to make our data and analysis neutral or objective, it is important for us to reflect and document all of the choices we make in constructing and analyzing data. These choices should be done by taking into account the goals of our work, following field-specific best practice guidelines, the weighing the pros and cons of different communities that might be affected by our actions. Secondly, because no dataset of method will capture all perspectives, we should use a variety of approaches when addressing our research questions. Options include using data from different sources, analyzing data collected in different ways, and integrating insights from quantitative and qualitative analyses to gain a broader set of perspectives.
The final principle of Data Feminism highlighted by D’Ignazio and Klein involves the importance of recognizing and that good data science involves the work and collaboration of a large number of people. As they concisely explain:
When designing data products from a feminist perspective, we must similarly aspire to show the work involved in the entire lifecycle of the project. This remains true even as it can be difficult to name each individual involved or when the work may be collective in nature and not able to be attributed to a single source.
Some diverse types of labor involved in data science work include participants who partake in human-based research studies, librarians and archivists who curate and catalog vast collections of human knowledge, and students who participate in the collection and analysis of large datasets. In Data Feminism, the authors interrogate an even larger set of possible roles involves in working with data and explore several particularly difficult examples of crediting work and possible solutions.
How do we integrate this into our work? The short answer is that we should try to surface all of the people that contribute to our work as data scientists. In terms of the more specific way that we should do this, we turn to the final section of these notes.
Many, though certainly not all, of the actionable ideas from Data Feminism involve doing a better job of reflecting and documenting the data science process. To consider a way of doing this, let’s turn to the second reference mentioned above. The goal of the “Datasheets for Datasets” article can be summarized from the following quote:
Despite the importance of data to machine learning, there is currently no standardized process for documenting machine learning datasets. To address this gap, we propose datasheets for datasets… [W]e propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on. Datasheets for datasets have the potential to increase transparency and accountability within the machine learning community, mitigate unwanted societal biases in machine learning models, facilitate greater reproducibility of machine learning results, and help researchers and practitioners to select more appropriate datasets for their chosen tasks.
The rest of the article describes the kinds of elements the authors suggest should be included in a datasheet. Some of these are very technical and only applicable to the sub-field of machine learning. The core ideas, though, can be adapted to provide a framework for any dataset.
I have adapted and simplified the framework from “Datasheets for Datasets” to better align with the Data Feminism principles above. Here are the eight sections that I propose should be a part of our concept of a Feminist Datasheet:
We will talk more in class about these concepts and how they might be put into action with a specific dataset. This will become the basis for the class final projects.
This is a long reading. Rather than a specific piece of homework, make sure that you really read the whole thing at a good level of detail. These concepts will permeate our work throughout the rest of the semester.