Critical Algorithm Studies
A reading list by Tarleton Gillespie and Nick Seaver:
"This list is an attempt to collect and categorize a growing critical literature on algorithms as social concerns. The work included spans sociology, anthropology, science and technology studies, geography, communication, media studies, and legal studies, among others. Our interest in assembling this list was to catalog the emergence of “algorithms” as objects of interest for disciplines beyond mathematics, computer science, and software engineering.
As a result, our list does not contain much writing by computer scientists, nor does it cover potentially relevant work on topics such as quantification, rationalization, automation, software more generally, or big data, although these interests are well-represented in these works’ reference sections of the essays themselves.
This area is growing in size and popularity so quickly that many contributions are popping up without reference to work from disciplinary neighbors. One goal for this list is to help nascent scholars of algorithms to identify broader conversations across disciplines and to avoid reinventing the wheel or falling into analytic traps that other scholars have already identified. We also thought it would be useful, especially for those teaching these materials, to try to loosely categorize it. The organization of the list is meant merely as a first-pass, provisional sense-making effort. Within categories the entries are offered in chronological order, to help make sense of these rapid developments.
In light of all of those limitations, we encourage you to see it as an unfinished document, and we welcome comments. These could be recommendations of other work to include, suggestions on how to reclassify a particular entry, or ideas for reorganizing the categories themselves. Please use the comment space at the bottom of the page to offer suggestions and criticism; we will try to update the list in light of these suggestions." (http://socialmediacollective.org/reading-lists/critical-algorithm-studies/)
0.1 technical and philosophical precursors / emic “what are algorithms?” essays
0.2 field surveys / keywords / initial provocations
0.3 books about algorithms addressed to broader audiences
0.4 lists of algorithm studies resources
1. the specific implications of algorithms and the choices they make
1.1 algorithms have embedded values / biases, lead to personalization / social sorting / discrimination
1.2 with algorithms come rationalization / automation / quantification,and the erasure of human judgment / complexity / context
1.3 questions of accountability and policy responses around algorithms
2. algorithms fit with, and help advance, specific ideological worldviews
3. algorithms are complex technical assemblages, that have to be mapped
4. algorithms aren’t just technical artifacts, they’re fundamentally human in their design and their use
4.1 people design and maintain algorithms, in specific ways, and that matters
4.2 people work, play, and live algorithms, in specific ways, and that matters
4.3 what do users understand about algorithms
4.4 the discursive production of algorithms to shape their public perception
5. methods and approaches for studying algorithmic systems