Computer Ethics

Required:

• Read chapter one from "Artificial Unintelligence: How Computers Misunderstand the World" by Meredith Broussard, 2018

  In her introductory chapter, computer scientist and data journalist Broussard lays out both her love of and skepticism for computing technology. You might find a bit of yourself in her.

• Read "Be Careful What You Code For" by danah boyd, 2016 (2 pages)

  danah boyd, a researcher at Micosoft and at Data and Society, highlights just how few guardrails there are for developers, from the consequences of algorithmic bias to the implications of crazy metaphors. She offers a call to action, solutions, and ample evidence for considering the implications of code. We highly recommend chasing down some of the links provided.

Optional:

• Read "How to read a book" by Paul N. Edwards, 2000 (8 pages)

  Here, an adroit scholar walks through some tips and tricks for reading more effectively. He hits the major points and includes some bonus tips, like where to best organize your reading notes. This is an invaluable resource as our course’s weekly reading load begins to increase. Skim now, but revisit throughout the course.

• Read introduction through page 16 from "Race after technology: Abolitionist tools for the new jim code" by Ruha Benjamin, 2019

  Benjamin, in a recent book, offers a “race conscious orientation to emerging technology not only as a mode of critique but as a prerequisite for designing technology differently.” Affiliated with Princeton’s Center on Information Technology and Policy, she brings a fresh perspective to many of the foundations of computing.

• Read "Discussion Leading" by John Rickford et al., 2007

  This resource from the Stanford Teaching Commons offers an in-depth analysis of how to have better discussions. Their recommendations, from setting an agenda, asking questions, and increasing discussant engagement are all a part of how to create a better climate for discourse. For leading discussions in this class and beyond, it’s worth a read.

Before Class:

• Daily assignment, due at 8pm the night before class:
  • What do you want to get out of our class discussions?
  • Do you feel able to change outcomes of how tech affects society?
• Preview the course project, part zero

Required:

• Read "Chapter 1: Conceptualising Data" by Rob Kitchin, 2014 (25 pages)

  The introduction to the book describes ways of thinking about what data is (ontologies) and goes on to discuss ethical and political considerations of data. It postulates the framework of “data assemblages” and how thoughts about them influence their own conceptions.

• Read "On Being a Data Skeptic" by Cathy O'Neil, 2014 (26 pages)

  This rapid-fire, well-articulated article is about the advantages and perils of data science, with ample advice and examples to advocate for why those who use data ought to be this special kind of “skeptical”.

Before Class:

• Daily assignment, due at 8pm the night before class:

  Use the following questions as a resource to guide your reading. Then, respond to at least three of them.

  • What is the difference between data, information, and knowledge? How are they related?
  • How can wider or economic concerns “frame” data? That is, in what sense do data act and how? (Answers might include: as an economic resource, as a form of power or knowledge, etc.) Explain why.
  • How have politics or economics influenced how some data have been defined or created?
  • What are reasons or incentives for controlling:
    • The creation of data
    • The access to data
    • The standards of data, such as metrics or units
    • The means of data collection, such as sensors or know-how
  • From “On being a data skeptic” explain “measuring the distortion.”
  • What is the relationship between models and proxies? Why are proxies used? Give an example.
  • Why might O’Neil have singled out “nerds” and “business people” separately? What do the differences in her comments indicate about how those groups view problems differently? Do you agree?
  • Submit a clarifying question which you’d like to discuss in class.
• Finish the course project, part zero by tonight.

Required:

• Read introduction (pg. 1 - 16; 31 - 32; 17 pages total) from "Sorting things out: classification and its consequences" by Geoffrey C. Bowker et al., 1999 (377 pages)

  Sorting Things Out is a classic text on classification and standardization. The introduction writes about the importance of considering the ubiquity of classification and the processes that generate them, standardize them, and enforcement. It also looks at how classification has caused harm and how the processes which create standards can at times yield an inferior solution. The authors take an expansive view of classification, so be prepared to think about the American Psychiatric Association’s Diagnostic and Statistical Manual (DSM) or VHS vs. Betamax.

• Read "Do algorithms reveal sexual orientation or just expose our stereotypes?" by Blaise Aguera y Arcas et al., 2018 (5 pages)

  This essay sets out to debunk a scientific study which claimed to have built a “sexual orientation detector” using machine learning. “Do algorithms reveal sexual orientation or just expose our stereotypes?” presents a thorough analysis of the offending paper and shows that one move to debunk “junk science” is validate the study’s results against some other baseline. In this case, the authors use Amazon’s Mechanical Turk. As you’re reading this, think about what one can learn from a face?

Optional:

• Read chapter one (pg. 46 - 50 from "Infrastructure" on) from "Sorting things out: classification and its consequences" by Geoffrey C. Bowker et al., 1999 (377 pages)

• Read "Deep neural networks are more accurate than humans at detecting sexual orientation from facial images." by Yilun Wang et al., 2017

  This is the article critiqued by “Do algorithms…”

Before Class:

• Daily assignment, due at 8pm the night before class:
  • “Do algorithms…” claims to focus on the underlying “science.” Why do you think the authors did so? Why was this distinction important?
  • What strategies did “Do algorithms…” use to make its argument?
  • The authors of “Do algorithms…” conclude that the paper they examined was misguided. Drawing on the discussion of classification in “Sorting Things Out,” think of another misguided method (any method, scientific or otherwise, and not necessarily one related to the “Do Algorithms” paper). Now, describe that misguided method which you have identified.

Required:

• Read "Introduction: Why Data Science Needs Feminism" by Catherine D'Ignazio et al., 2020

  Through a historical examination of women in technology, D’Ignazio and Klein, both leading scholars in the field, introduce us to feminism and its role in shaping technologies. This is just a taste and the whole book is worth a read.

• Read "Gender Equality Paradox Monkey Business: Or, How to Tell Spurious Causal Stories about Nation-Level Achievement by Women in STEM" , 2020 (5 pages)

  A widely known study argued that countries with more gender equity in society have fewer women studying STEM, but this article accompanies a peer-reviewed publication casting doubt on the study’s analysis – a scholarly back-and-forth also playing out in the blogosphere.

Optional:

• Read "Patriarchy, technology, and conceptions of skill" by Judy Wajcman, 1991 (16 pages)

  When considering the future of work, one question that’s often raised is how technology negatively impacts the amount of “skill” required to complete a task, aka “deskilling”. In “Patriarchy, Technology, and Conceptions of Skill”, Judy Wajcman questions the underlying assumption that skill is entirely technically derived. Instead, she considers how men’s historical control over technology in the workplace has extensively influenced the ideological and material conceptions of skill, thus concluding in part that “definitions of skill, then, can have more to do with ideological and social constructions than with technical competencies which are possessed by men and not by women”.

• Read "Technically Female: Women, Machines, and Hyperemployment" by Helen Hester, 2016 (10 pages)

  This essay surveys a history of “electronic secretaries” to frame relevant questions of today’s tech, such as: Why are AI assistants so often feminized? We question what it means for technology to “do gender” and in service of which “imagined technology user”? Yet we can turn that question around and ask who “does technology”? and how does labor gets redistributed with the introduction of new software and AI assistants? Ultimately, Hester asks us to confront questions concerning lived experiences of gender and how its programmed, productive vs. reproductive labor, and the (dis)advantages of automation.

• Read "Testosterone rex: unmaking the myths of our gendered minds" by Cordelia Fine, 2017

  If you were looking to throw the book at someone who continues to insist that sex differences are sufficient to explain gender differences, this would be that book. Take her word for it: “Every influence is modest, made up of countless small instances of its kind. That’s why everything—a doll packaged in pink, a sexist joke, a male-only expert panel—can seem trivial, of intangible effect. But that’s exactly why calling out even seemingly minor points of sexism matters. It all adds up, and if no one sweats the small stuff, the big stuff will never change.” And it’s funny, too: “If we stop believing that boys and men are emotional cripples and fly-by-night Casanovas who just want sex, and start believing that they’re full, complete human beings who have emotional and relational needs, imagine what might happen.”

Before Class:

• Daily assignment, due at 8pm the night before class:
  • Why is feminism relevant to data science?
  • What considerations does data feminism require us to make?
  • “Gender Equality Paradox” is an example of data feminism at work. How so?
• Finish the course project, part one by tonight.

Required:

• Read "The Internet Is Acid, and America Is Having a Bad Trip" by Douglas Rushkoff, 2018 (1 page)

  Douglas Rushkoff, a noted media theorist, draws connection between software and hallucination. Keenly attuned to the wild abstractions and metaphor used in computation: “Computers and networks were part of a much larger cultural phenomenon: a realization that reality is a collaboration…It seemed as if the world was about to become a whole lot more like a lucid dream, where the future was less a place we arrived at than a thing we created together.” Where are we today? Where do we go from here?

• Read page 151 until chapter end from "Rise of the machines: A cybernetic history" by Thomas Rid, 2016 (8 pages)

  Rid, a scholar of conflict and information technology, aptly summarizes the act of reimagining as it took place around the idea of the “cyborg,” particularly as evoked by Donna Haraway (see optional readings). This kind of reimagining is important to consider in the historical development of technology. Note how this work references I.J. Good and other thinkers in the history of AI and ethical machines.

Optional:

• Read sections 1 and 5 (10 pages total) from "A Cyborg Manifesto: Science, Technology, and Socialist-Feminism in the Late 20th Century" by Donna Haraway, 2006 (42 pages)

  Readers not familiar with critical theory will likely struggle with this reading, particularly the fluid language and arcane references. Haraway uses the extended metaphor of cyborg as a vehicle to explore the relationship between gender and identity, nature and technology—pushing back on traditional conceptions of radial feminism and affinity vs. identity politics, and fiercely critiquing the language of capitalism. Although much tech has changed since 1991 when it was written, the essay stays as influential as ever.

• Read "Epistemological Pluralism: Styles and Voices within the Computer Culture" by Sherry Turkle et al., 1990 (31 pages)

  In this classic paper witness the meeting of minds of a great scholar of science studies and one of computer science as they discuss how it is that computers may allow us to expand our own minds. Find yourself in the diverse stories of computers as tools for thinking.

• Read "Dynamicland and the Whimsical Digital Object" by Olivia Kan-Sperling, 2019 (3 pages)

  A relatively short piece that comments on a programming lab–dynamicland–which, while situated near Silicon Valley, eschews the ethos of human transcendence. The article is sweet and attentive to the project, without withholding criticism, but concludes that the conceptual indulgences it is most weary of are also what make it amazing and notable.

• Read "What the dormouse said-- : how the sixties counterculture shaped the personal computer industry" by John Markoff et al., 2005 (364 pages)

  This history traces computer science back to the counter-cultural roots of the 1960s bay area. Read it to appreciate the environment in which the imaginative and libertarian ideas of computing took off. Consider how a person’s background influences what it means for them to reimagine.

Before Class:

• Daily assignment, due at 8pm the night before class:

  Choose a vision of our technological present or future. What do you see as the purpose of re-imagining it? Example visions include:

  • The way we use computers (such as in “Dynamicland” and “Epistemological”),
  • Our belief in the abstraction of software (such as in “What the Dormouse Said” and “Epistemological”),
  • The exclusivity of classifications and labels (such as in “The Cyborg Manifesto”),
  • The role of technology in nature (such as in “The Cyborg Manifesto”),
  • or any other media which we’ve covered.

Required:

• Read "It's the (Democracy-Poisoning) Golden Age of Free Speech" by Zeynep Tufekci, 2018 (4 pages)

  Zeynep Tufekci considers how power to censor functions on our oversaturated social networks, and the role of misinformation and the attention economy in this. The article provides striking clarity to issues we collectively face on this platform.

• Read chapter 17 from "Genius Makers : The Mavericks Who Brought AI to Google, Facebook, and the World" , 2021

  In this chapter of his book on the rise of neural networks, Metz, a veteran journalist of Silicon Valley, concisely describes the claims which some make about those tools. Will AI “solve” content moderation for us? Read on.

• Read chapter four (pg. 74 - 110; 36 pages total) from "Custodians of the internet: platforms, content moderation, and the hidden decisions that shape social media" by Tarleton Gillespie, 2018 (288 pages)

  Read the whole chapter, but, if you’re short on time, focus on the “automatic detection” section (pg. 97 to 110). Tarleton Gillespie, a social media scholar, establishes the groundwork for understanding social media platforms. In chapter 4, “Three Imperfect Solutions to the Problem of Scale”, Gillespie considers several models for content moderation, one of which is algorithmic “automatic detection” techniques.

Optional:

• Read "The Problem of Free Speech in an Age of Disinformation" by Emily Bazelon, 2020 (14 pages)

  Read this article for an up-to-date account of free speech and its history in the United States. Notice the similarities between analog and social media. It questions how different governmental approaches to speech may be making us more or less free.

• Read "How Facebook Hides How Terrible It Is With Hate Speech" by Noah Giansiracusa

  Cut through the claims of AI content moderation and listen to Facebook’s own assessment that they ‘may action as little as 3-5% of hate … on Facebook.’

• Read "The Risk of Racial Bias in Hate Speech Detection" by Maarten Sap et al., 2019 (9 pages)

Before Class:

• Daily assignment, due at 8pm the night before class:

  From the 2016 Russian misinformation campaign, to the 2021 booting of the U.S. president, to the recent (late 2021) whistle-blower revelations regarding mental health and hate speech, a maelstrom has surrounded Facebook and other popular social media platforms for the past few years (e.g. see the optional reading, “How Facebook Hides How Terrible It Is With Hate Speech”).

  What do you think ought to be done about the content and its moderation on Facebook? Try to be specific in exactly what problem you are addressing and the trade-offs involved in your proposal.

  Your answer might include:

  • De-platforming
  • More automated moderation
  • Hire more moderators
  • Regulation…
    • e.g. changing liability of publisher
    • or otherwise

Required:

• Read chapters 15 and 16 from "Genius Makers : The Mavericks Who Brought AI to Google, Facebook, and the World" , 2021

  In this chapter of his book on the rise of neural networks, Metz, a veteran journalist of Silicon Valley, provides an up-to-date account of the roles that these, particularly computer vision, methods have played in both bigotry and weaponization. Can AI solve those problems for us?

• Watch "STEALING UR FEELINGS" by Noah Levenson, 2019

  Also consider reading through the implementation!

Optional:

• Read "Physiognomy's New Clothes" by Blaise Aguera y Arcas et al., 2017 (16 pages)

  Can an algorithm detect criminality from your face alone? In this paper, the authors critique a 2016 paper that claims to be able to do so. By focusing on the historical context and current science, this article refutes the paper claims while providing a salient illustration of the danger of using Neural Networks inappropriately.

• Read introduction , background, and discussion (pg. 1 - 2; pg. 2 - 5; pg.10 - 14; 7 pages total) from "The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition" by Os Keyes, 2018 (21 pages)

  This paper surveys the HCI papers to understand how researchers understand “gender” and the implications for how Automatic Gender Recognition systems are developed. As you’re reading this, think about who gets to decide what is “gender” when developing a product and how different operationalizations of this concept can impact how AGR systems are deployed and impact Trans people.

• Read "Facial feature discovery for ethnicity recognition" by Cunrui Wang et al., 2019 (17 pages)

  This paper, pilloried by Agüera et al., is another example of the ways facial recognition may err.

• Read "Dark matters: On the surveillance of blackness" by Simone Browne, 2015

  Chapter three, page 108 on

Before Class:

• Daily assignment, due at 8pm the night before class:
  • Within the context of facial recognition systems there are many incentives for increasing systems’ efficacy. Chose one incentive (e.g. make money or win an election). Now, how does that incentive influence (pick one of the below):
    • The system’s ability to control, or direct the behavior of its users?
    • Ideas about what it means to be human?
    • Reputability of the science and applications of the facial recognition systems?
  • What are possible blind-spots in our own conceptions of human traits?
• Finish the course project, part two by tonight.

Required:

• Read "Reclaiming conversation: the power of talk in a digital age" by Sherry Turkle, 2015 (21 pages)

  As technologists we set out to do the good work of automation. We formalize, experiment, and implement, increasing the space of what computers can do. But, as Turkle asks, is this what we want? Do we want to be replacing each other? She says, “It seemed that we all had a stake in outsourcing the thing we do best—understanding each other, taking care of each other.” Consider what happens when our Utopian projects are personal.

• Read "Communist Commentary on "The Triple Revolution"" by Richard Loring, 1964 (10 pages)

  This essay was published contemporaneously to the “Triple Revolution”, and is largely favorable of the reforms demanded. It also touches on the utility of utopianism in futurism, while considering labor issues in a distinctly Marxist, but still American, manner. In particular, the authors summarize, and take issue with, the Triple Revolution as saying “it is useless to fight the path progress is taking and they should therefore re-direct the aims of their fight to seek a better future in a world in which labor and its role will no longer be a basic factor.” The scan we have is a bit difficult to read, but we have been unable to find another.

Optional:

• Read "The Automation Charade" by Astra Taylor, 2018 (5 pages)

  Astra Taylor urges: “We shouldn’t simply sit back, awestruck, awaiting the arrival of an artificially intelligent workforce. We must also reckon with the ideology of automation, and its attendant myth of human obsolescence.”

• Read "The Microsoft Provocateur" by Ken Auletta, 1997 (14 pages)

  This piece shows us the thinking of tech folks at a pivotal time when the nature of the internet was not yet decided. Skim the sections which are just about Mhyrvold’s life—we mean to focus on founders and their utopian visions.

• Read "Origin Stories of Tech Companies if Their Founders Had Been Women" by Ginny Hogan, 2019 (1 page)

Before Class:

• Daily assignment, due at 8pm the night before class:
  • Oftentimes in computer science, we consider only the upshot, only the Utopian worlds we might create. Today has two readings, both of which consider the implications of technologies, one on an interpersonal level and the other on a societal level.
    • For one of the technologies mentioned in the readings or another of your choosing, comment on the future worlds we imagine for it (e.g. a Utopia) and complications to that future which might arise in practice (e.g. a Dystopia).
  • What are other utopias you have heard described when talking about emerging technologies? Do you find those utopias compelling? Why or why not?

Required:

• Read "Digital Pregnancy Test Deconstruction (Twitter Thread)" by Foone Turing, 2020

  This and the next reading offer informal Twitter threads with some surprising twists on how a technology actually works and why it may or may not be useful. Is this misleading people or providing an ethically valuable service or both? If you had chosen this for your project, what would you have investigated?

• Read "Response to Digital Pregnancy Test Deconstruction (Also on Twitter)" by Naomi Wu, 2020

  (See above.)

Before Class:

• Daily assignment, due at 8pm the night before class:

  Come to class prepared to share a description of your project to others.

Required:

• Read chapter 12 from "Artificial Unintelligence: How Computers Misunderstand the World" by Meredith Broussard, 2018

  Having read Broussard’s commentaries on the first day and as an introduction to the data unit, we now finish with her conclusion: a renewed plea for computing technology to serve the people who made it—humans.

• Read foreward and introduction (pg. xi - xxvi; pg. 1 - 5; 23 pages total) from "Hope in the dark: Untold histories, wild possibilities" by Rebecca Solnit, 2016

  Solnit, a writer and activist, reflects on our desire for social, cultural, or political change given the appearance that we have not arrived there (considering issues from global warming, to human rights abuse). Originally responding to the war in Iraq, she explores how news cycles and our personal narratives frame these issues and makes the case for hope nonetheless–“tiny and temporary victories”

Optional:

• Read From 'SAN DIEGO' on from "The Code: Silicon Valley and the Remaking of America" by Margaret O'Mara, 2019

  From the UW historian O’Mara hear this story of how two UW CSE alumna have navigated their careers as computer scientists.

Before Class:

• Daily assignment, due at 8pm the night before class:
  • Do you feel able to change outcomes of how tech affects society?
  • What’s an idea from this course that every UW CSE student ought to understand?
• Finish the course project, part three by tonight.