By Erin Wolcott, Assistant Professor of Economics, Middlebury College

Earlier this month, I invited a long-time friend, Quoctrung Bui, to campus. Bui and I know each from working our first job after college crunching data as research assistants at the Federal Reserve Board in Washington, DC. These days Bui is a graphics editor at the New York Times, where he covers social science and policy for The Upshot. He specializes in writing stories accompanied with data visualizations that help readers process the numbers.

At a workshop sponsored by the CTLR and DLA, Bui discussed with faculty what it’s like to be a graphics editor, how faculty can get their research picked up by the media, and what Middlebury students can do to prepare themselves for potential careers in data journalism. The conversation also turned to helpful coding and publication tools (see below for Bui’s recommendations).

To publish an Upshot article, Bui generally works with a team. He used the word “barnacle-ing” to describe how these teams are formed.  If one editor comes up with an idea, other editors “barnacle” on to the project because they find it interesting, and together they can publish better journalism than working against each other. Initial ideas often come from sifting through academic research (often on Twitter feeds of scholars, policy makers, and other journalists) in search of new data and compelling stories. From there, editors interview experts in the field and work with researchers to create The Upshot’s data visualizations. Success of a story is partly measured by the number of reader “clicks” and other online analytics.

One data graphic that sparked much conversation during Bui’s workshop was the New York Times‘s election forecast “needles,” which both catch some of the excitement of elections and compress all sorts of factors in ways that might be hiding the data feeding these meters, particular around margins of error (also the needle has inspired a number of Internet memes and commentary).

For students interested in a career in data journalism, the New York Times sponsors fall and spring semester internships in graphics. Bui recommends proficiency in R and other programming languages, but most of all a keen sense of journalistic curiosity about how data can suggest stories relevant to the Times’s readership.

Bui’s Quick Guide to Helpful Data Visualization Tools

• In my opinion, the best model out for how publications should look like is Distill.
• A chart/mapmaking tool I like for the web is Datawrapper.
• I find Mapshaper does most of what you might want to do in QGIS or ArcGIS, but is free, web-based, and much faster.

DLA’s Digital Fluencies Series investigates what it means to develop more critical facility and engagement with digital technologies. Meetings usually combine 1-3 readings and a case study for hands-on exploration. Faculty, students, and staff at all levels of digital skill are welcome to attend.

Our meeting on algorithmic racism explored how we increasingly live in an algorithmic society, our everyday lives shaped by interactions with Google searches, social media platforms, artificial intelligence software, and myriad devices and programs that rely on the execution of computational algorithms. At the broadest level, We wanted to ask, updating Robert Staughton Lynd’s famous book-title phrase Knowledge For What?, algorithms for what? More specifically, we hoped to explore what it would mean to become “algorithmically fluent” and more critically aware of the ways in which algorithms reinforce or extend larger structures of racism, oppression, injustice, and misrepresentation. And how might we harness the power of algorithms for better ends in scholarship, teaching, inclusivity, freedom, and citizenship in the contemporary world?

We turned to the following readings and case study:

READINGS

• Safiya Umoja Noble, “Introduction: The Power of Algorithms,” in Algorithms of Oppression: How Search Engines Reinforce Racism (New York: New York University Press, 2018)
• Navneet Alang, “Turns Out Algorithms Are Racist,” New Republic,31 August 2017
• Zeynep Tufekci, “YouTube, the Great Radicalizer,” New York Times, 10 March 2018
• Virginia Eubanks, “The Digital Poorhouse,” Harper’s, January 2018

CASE STUDY

• Zeynep Tufekci, “What Happens to #Ferguson Affects Ferguson: Net Neutrality, Algorithmic Filtering and Ferguson,” The Message, 14 August 2014

ADDITIONAL READINGS

• Benjamin Schmidt, “Do Digital Humanists Need to Understand Algorithms?,” Debates in the Digital Humanities 2016
• Benjamin Schmidt, “Why Digital Humanists don’t need to understand algorithms, but do need to understand transformations,” Sapping Attention, 20 July 2016
• Yeshi Milner, “An Open Letter to Facebook from the Data for Black Lives Movement,” Medium, 4 April 2018
• Li Zhou, “Is Your Software Racist,” Politico, 7 February 2018

Much of our conversation pivoted on two issues: how do we become aware of the effects of algorithms in our lives as citizens, and what kind of curricular interventions at Middlebury might best prepare students for navigating a world of algorithms?

On the former question, we returned repeatedly to the need for awareness, while on the latter we pondered how to enhance this awareness in a liberal arts college curriculum. Our overarching sense seemed to be that not everyone must become proficient in designing algorithms as coders or programmers to develop more contextual understanding of how they function in the Internet and other digital technologies as currently designed. We can learn basic underlying histories and guiding principles for algorithmic construction that help us all better identify when algorithms are causing harm, when they turn into what Cathy O’Neil calls Weapons of Math Destruction. 

We can also, fascinatingly, do the reverse: we can use algorithms as a way to glimpse deeper issues of structural racism (not to mention sexism and other isms that name systemic modes of injustice, violence, and suffering). Algorithms, we learned from our readings, get designed and implemented within social conditions that are already systemically racist; is it no wonder that they then, as computational processors of data, information, and knowledge, reproduce racism? What has been most striking, as Safiya Umoja Noble and Zeynep Tufekci show, is how the particular contexts in which algorithms now dominate our lives, amplify these underlying and persistent historical forces? The problem is not algorithmic thinking per se, but rather the frameworks in which algorithms are employed.

And what are these frameworks? We noticed a few from our readings and discussion:

1. Advertising as the business model for Silicon Valley. Our authors repeatedly pointed to the ways in which Google, Facebook, Twitter, and other dominant forces on the Internet are all driven by attracting attention to sell advertising. This, as Tufekci contends in the case of YouTube, seems to create algorithmic designs that intensify extremist views and controversy while hollowing out a common middle ground of cultural experience and exchange (although perhaps cat videos might sustain that common space, which is to say perhaps there are certain kinds of kitsch that create commonality!?).
2. The bubble effect of social media. Because social media carves up the distinction between private and public spheres in new ways, it undercuts previous assumptions and models about shared culture. Tufekci’s work on Twitter, Facebook, and Fergusoncatches the ways in which the idea of the public sphere has fragmented into a multitude of semi-public spaces. The network models that social media algorithmically generate pose new challenges for giving the public sphere and shared public culture a robust virtual life.
3. State power and government regulation. A focus on advertising points to the role of the state in possibly regulating algorithmic activities. However, Virginia Eubanksuncovers ways in which state power has also been misused to exacerbate long-running problems of managing the poor rather than addressing poverty itself. Sometimes this has to do with cynical political decisions or extreme political views, but the managerial-algorithmic complex operating in both corporate-commercial Silicon Valley spaces and governmental decisions may well be as crucial to confront as the problems of consumerist economics underlying the Internet infrastructure.
4. How does greater awareness of the role of algorithms in contemporary society relate to the need for increased numeracy? How might we better understand the logics and statistics and approaches of math, statistics, and numbers as part of our civic obligation when it comes to digital technologies, the Internet, and the presence of algorithms in larger systems of oppression? And how do we do so not in a Luddite fantasy of blaming the machines, but rather with a goal of liberation—or at least reform to systems that continue to sustain regimes of racial inequity and injustice?

These are just a few issues that arose in our conversation, which also touched on matters of how we handle the benefits and drawbacks of automation through algorithmic computation, whether the makers of algorithms are ethically responsible for their creations, at one point those who use the algorithms become ethically responsible for their actions, and how we might notice or imagine alternatives to the current technological systems that, despite the more wildly utopian rhetoric about digital culture, have not only reinforced long-running forces of racism, but even escalated them further. How can we devise practical solutions and reforms as well as continue to imagine more wild, utopian alternatives and imaginaries?

In addition to noticing the presence of algorithms in our shared lives as citizens, our conversation also turned to the classroom and curriculum at Middlebury. How do we teach in new ways to advance digital fluencies when it comes to the relationship between algorithms and racism? A debate emerged between two models: does one concentrate on core courses that explore digital fluencies around topics such as the ethics of algorithms or should awareness and thinking about algorithmic thinking suffuse the curriculum across multiple disciplines?

Perhaps the answer is both should happen. Core courses in Computer Science, the social sciences, information environmentalism, philosophy, and history of technology can go deep with the many facets of algorithmic analysis. Our freshman seminars might all contain some kind of digital fluency component. There might be other moments to create cross-campus engagements with the problem and possibilities of the algorithm.

At the same time, heightened awareness of algorithmic thinking might also appear within many different disciplinary areas. The challenge would be to use the increased consciousness of what algorithms are up to in order to deepen student learning about particular fields of study. A good model for this approach might be found in Benjamin Schmidt‘s work on the effort to apply algorithmic thinking to specialized scholarship in literary studies and history (in his commentary on the Jockers/Swafford debates about the Syuzhet Package and sentiment analysis of nineteenth-century European novels). Here, a seemingly esoteric scholarly disagreement cracks open a view on issues not only of algorithms but also the history of the European novel. To be sure, we’ve moved away from racism in its contemporary or historical context in this instance, but we might be able to delve deeper into all sorts of topics such as racism via considerations of the algorithm in various departments, disciplines, courses, units of courses, and fields of study.

In short, we need the history and context of algorithms to understand their workings more critically; at the same time, we might be able to use the growing prevalence of algorithms in society—and debates about their effectiveness and accuracy—as opportunities to gain deeper comprehension of the histories, contexts, methods, approaches, modes of inquiry, information, data, and knowledge that algorithms now increasingly mediate.

Addendum: Atossa Araxia Abrahamian writes about “algorithmic citizenship” in New York Times opinion piece, “Data Subjects of the World, Unite!”, 28 May 2018, drawing upon the work of James Bridle.

X-posted from Keywords: The Middlebury College Libraries Newsletter

By Michael Roy, Dean of the Library

We’re upgrading our library catalog software this spring, and as we do this, we have a chance to re-visit choices we’ve made in how we configure the system.  One of those choices involves how we handle records pertaining to patron information.  Legally, we are allowed to store borrowing records for our own internal purposes, provided we limit the use of this information to library staff, only sharing it outside of the library when compelled to do so by law enforcement via a subpoena.  Historically, the library has never stored any information about material individual patrons have checked out once the materials are returned to us, largely because we believe that not storing that information at all is the best way to protect patron privacy, one of the core values of librarianship.  (Individual patrons can currently choose to view their own borrowing histories through MyMIDCAT, but the library does not have access to that information.)

If, however, we did choose to keep this data, it would allow us to do some useful things.  We would, for example, be better able to determine who may have damaged an item if we failed to notice a problem upon checking it in.  With some additional programming, we might be able to build on top of this data a recommendation system that would enable us to suggest other materials to our readers based on the habits of similar patrons.  We might conceivably also be able to gather aggregate usage statistics to see how our collections are used by discipline, by class year, or even, perhaps, by GPA, and to explore if there is any relationship between academic achievement and use of our resources.  These potential uses are all very enticing, and might make our currently rather pedestrian catalog a bit more exciting and interactive.

We won’t be attempting any of that.

I write this a few weeks after Mark Zuckerberg, Facebook’s CEO, was summoned to Washington for two days of testimony in the wake of the disclosure that Cambridge Analytica had used Facebook’s API to collect personal data on hundreds of thousands of Americans as a means to influence the 2016 presidential election.  As we’ve known for decades, when a service is free to a consumer, the consumer is not really the customer, but is instead the product; the actualcustomer is the advertiser (or political operative) that is paying for access to the personal data of the service’s users.  Increasingly, the systems we buy come configured in a way to encourage us to collect massive amounts of data about our community.  Computers are really, really good at doing this sort of thing.  In turn, we need to also get really, really good at thinking about our values, our core principles, and the ramifications of the seemingly small choices that we make as we procure and configure these systems.  And we need to be clear with our community about what data we are collecting, how we are protecting it, who has access to it, and how it is being used.

In our new strategic framework, we’ve identified critical digital fluency as one of the core elements of a Middlebury education.  We’ve begun a series of workshops and conversations that will help us as a community develop a shared vocabulary about what critical digital fluency means, and what it looks like in terms of both technical skills and habits of mind.  This is a timely development.  We live and work in a world that is increasingly mediated by technology, and have to make choices both large and small that collectively result in how we live out all dimensions of our personal and professional lives.  Creating community norms around privacy and data, and learning to think critically about what technologies we choose, and how we choose to configure them, must be a core part of our collective development of this newly defined fluency.  In addition to being thoughtful, critical consumers of technology, we can also, through collective action, identify opportunities to positively influence the future direction of the technologies that we increasingly rely upon, to reclaim the scholarly commons, to resist and reject models where we are the product, and to help build new infrastructures that support our values.

In their digital project, DLA Fellow Florence Feiereisen (German Department) and colleague Erin Sassin (History of Art and Architecture) explore the soundscapes and architectural spaces of the Meyershof building in Berlin, circa 1932.

By Florence Feiereisen (German) and Erin Sassin (History of Art and Architecture)

We continue to work full steam ahead on our audible architectural history project! Our ultimate goal is to create a website that allows to visually and aurally experience Meyershof, a large Mietskaserne (tenement building, literally a “rental barrack”) that was constructed in the working-class neighborhood of Berlin-Wedding in 1873/74 and remained there until Allied bombing destroyed the structure toward the end of World War II.

Through 3D modeling, soundscapes, and animations of aggregate historical figures who serve as guides to the building, our project creatively uses digital technology to offer new ways of accessing the sensorial experiences of working-class Berliners. We have started from extensive empirical research, and are now adding digital dimensions to contribute to emerging scholarly literatures in sensory studies, architectural and urban history, and related fields. We are particularly interested in how digital tactics allow for more subtle analysis and presentation of the acoustic ecology of the building. We want to hear as well as see how sensory perceptions shaped—and were shaped by—class consciousness as well as class conflict in early-twentieth century Berlin. We use 1932 as a key temporal moment because it was when residents of Meyershof joined a city-wide rental strike and the building became a Communist Party stronghold in the rise of fascism in Germany.

A “city within a city,” Meyershof featured a front building with 6 multi-functional cross-buildings and courtyards staggered behind each other on Acker Street 132/33. Initially housing more than 2,000 people in 257 apartments, along with small businesses and workshops, Meyershof came to symbolize all that was wrong with working-class housing in Berlin. Newspapers in the 1930s—the time frame on which we concentrate—tell the story of a dungeon-like “fortress” whose damp, dark, condemned spaces, lead pipes, and unregulated living arrangements, including the presence of overnight lodgers, posed a danger to public health and a threat to the social order. As Berliner and illustrator Heinrich Zille famously claimed: “with an apartment you can kill a man just as easily as with an ax.”

While descriptions of Meyershof are vivid and, indeed, capture most senses, we found it curious that publications of the time do not feature noise complaints in the context of working class accommodations, even though the soundscapes of working class life were louder and more complex than in pre-industrial society. Hence, our project not only includes a 3D model of Meyershof in 1932 and evocations of the sounds within the building, but also the composite portrayal of four residents who guide visitors around our website as they navigate the aural dimensions of their daily lives, which could be cacophonous or quiet, but always profoundly modern in the context of mid-twentieth century Berlin.

The ambient sounds of a mother doing piecework in her kitchen at night in Cross Building #1, a preliminary soundscape for Meyershof ca. 1932, created by Bilal Khan, Kat Finck, Mikaela Chang in Professor of the Practice Erin Davis’s Introduction to Podcasting course, J-Term 2018.

Funding from the DLA has supported me as a year-long fellow as well as research assistance by two very talented Middlebury students: Emma Harnett, who has begun to construct a 3D model of Meyershof in SketchUp, and and Oliver Oglesby, who has begun to design a web interface for the project. Currently, we are working on a talk we’ll give at the Vernacular Architecture Forum in Alexandria, Virginia in May and we are creating the four fictional characters (aggregate figures based closely on historical data). The next step will be to finalize our 3D model and recreate/produce soundscapes that evoke life in Meyershof around the year 1932. You’ll hear the ambient sounds of a mother doing piecework in her kitchen at night in Cross Building #1, the roaring of newly acquired engines in workshops located in the back buildings, and the sounds of Bakery Royl’s kitchen in the front building. With the help of DLA funding, we will be attending the DHSI (Digital Humanities Summer Institute) in Victoria, British Columbia in June so that we can learn more about digital soundscape creation. We will report back to to the Middlebury DLA community on what we learn.

DLA’s Digital Fluencies Series investigates what it means to develop more critical facility and engagement with digital technologies. Meetings usually combine 1-3 readings (a link to materials will be provided when necessary) and a case study for hands-on exploration. Faculty, students, and staff at all levels of digital skill are welcome to attend.

Our inaugural meeting focused on databases. Since databases undergird almost every digital project, platform, interface, and tool, but not all databases are alike, we asked how we might better understand what databases are—and what they can be—as core components of digital liberal arts scholarship. We also wanted to investigate how we might become more critically aware of database design’s history, logic, ethical questions, and potential for our scholarship, from research to teaching to working with students in other capacities.

Our readings included:

N. Katherine Hayles, “Databases,” in How We Think: Digital Media and Contemporary Technogenesis (Chicago: University of Chicago Press, 2012), 37-40

Lev Manovich, “Database as Symbolic Form,” Convergence 5, 80 (1999), 80-99

Christiane Paul, “The Database As System and Cultural Form: Anatomies of Cultural Narratives,” in Database Aesthetics: Art in the Age of Information Overflow, ed. Viktorija Vesna Bulajic (Minneapolis: University of Minnesota Press, 2007), 95-109

Sarah Whitwell, “Resistance, Racialized Violence, and Database Design,” Sherman Centre for Digital Scholarship, McMaster University, 26 February 2018

Matthew E. Davis, “The Database as a Methodological Tool,” Digital Medievalist, 10 August 2017

Matthew Lincoln, “Best Practices for Using Google Sheets in Your Data Project,” Matthew Lincoln, PhD Art History and Digital Research Blog, 26 March 2018

Hadley Wickham, “Tidy Data,” The Journal of Statistical Software 59 (2014)

[Additional post-meeting readings include M.H. Beals, “On transcending Excel, building a better world, and achieving inner peace on social media” and “Building a better data trap; or, why data structures matter.”]

Data services librarian Ryan Clement offered a wonderful basic introduction to the difference between a flat file and relational database (slides) and we looked at readings by Katherine Hayles, Lev Manovich, and Christiane Paul, among others.

A few tentative conclusions from our meeting.

First, we are using databases almost all the time when we turn to digital tools, from WordPress to filling out a form online. So it is incumbent on us to at least be aware of their presence—and the implications of their presence—even if we do not become software engineers, database designers, or coders. This can help us be more critically fluent in terms of questions of data ethics (what should be shared publicly and what should not?) since databases store data and can provide or control various kinds of access (or restrictions to access).

Second, sometimes we work with databases that arrive in more rigid form: licensed datasets and databases (a library catalog, BannerWeb) or applications that sit on top of databases (Adobe Premiere). If we and our students are more critically fluent with how the databases function within these out-of-the-box proprietary tools, we can sometimes find ways to use them or bend them toward our own ends or toward surprising new uses. Sometimes, you can tweak database tools even when they seem constraining at first.

Third, research projects in digital liberal arts often start from scratch. Most typically in an Excel or Google Sheets flat file. Matthew Lincoln has lots of great things to say about this in a recent blog post, Matthew Lincoln, “Best Practices for Using Google Sheets in Your Data Project,” Matthew Lincoln, PhD Art History and Digital Research Blog, 26 March 2018. We noticed that there is an opportunity from the beginning of these research projects to think strategically about database design. We might start out by asking two key questions: what are the qualities of the data? And what does one wish to do with the data? These questions might drive database design (or choice of out-of-the-box database software). Database design arose historically out of pragmatic solutions to data management and analysis issues. We too can join that history by exploring what the qualities of our particular data are and what we are curious about trying to do with our data (look for patterns of connection; create tagged sequences of narrative; generate surprising patterns through computational processing that people might not detect; undergird and support publication; remix and resort by certain parameters; manage fluid data of students and their interests; and so on).

Finally, we came up with three aspects of data worth thinking about: what are the particular units of data with which one is working (names, locations, dates)? What are the relations one is curious about exploring among data? And what are the sorts of operations one wishes to pursue with the data?

We thank all participants for attending the event and starting to think about what it might mean to develop our and our students’ critical digital fluency when it comes to databases.

— Michael J. Kramer, Associate Director of the DLA Initiative

Upcoming topics in the Digital Fluencies Series include: Algorithms, Bots, Data, Platforms, Archives, Gender in Code, Digital Racism, Open Access, Podcasting, Remix, Publishing and Peer Review, Animation, Glitching and Deformance Tactics, Memes, Web Design, the Template, Data Visualization, GIS and Spatial Data/Thinking, and User Experience. Feel free as well to suggest a topic. We welcome your attendance at this ongoing series!

Co-sponsored by DLA, CTLR, Davis Library, and DLINQ. Organized by Leanne Galletly, User Experience & Digital Scholarship Librarian, and Michael J. Kramer, Assistant Professor of the Practice, Digital History/Humanities and Associate Director of the Digital Liberal Arts Initiative. Middlebury go link: go/digitalfluencies.