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  • Mark 8:01 pm on November 11, 2019 Permalink | Reply
    Tags: anti-hermeneutic, data science, evisceration, , human agency, , platform epistemics   

    Humans as blackboxes, machines as transparent 

    From Automating Inequality by Virginia Eubanks pg 167:

    Parents in Allegheny County helped me articulate an inchoate idea that had been echoing in my head since I started my research. In Indiana, Los Angeles, and Allegheny County, technologists and administrators explained to me that new high-tech tools in public services increase transparency and decrease discrimination. They claimed that there is no way to know what is going on in the head of a welfare caseworker, a homeless service provider, or an intake call screener without using big data to identify patterns in their decision-making. I find the philosophy that sees human beings as unknowable black boxes and machines as transparent deeply troubling. It seems to me a worldview that surrenders any attempt at empathy and forecloses the possibility of ethical development. The presumption that human decision-making is opaque and inaccessible is an admission that we have abandoned a social commitment to try to understand each other. Poor and working-class people in Allegheny County want and deserve more: a recognition of their humanity, an understanding of their context, and the potential for connection and community.

  • Mark 8:32 am on February 5, 2019 Permalink | Reply
    Tags: data science, social data science   

    The Data Science & Society Institute 

    This looks like an interesting job at a new institute I’d like to keep track of:

    The Department of Science & Technology Studies at Cornell University seeks
    a Postdoctoral Researcher to play a major role in a two-year project on
    Data Science & Society. We invite applications from scholars with a recent
    Ph.D. in science & technology studies (STS) or related fields (e.g.,
    sociology, anthropology, law, media studies, information science) and an
    active research agenda on the social aspects of data science.

    The Postdoctoral Researcher will be expected to devote 50% time to his or
    her own research agenda and 50% time to working with S&TS Department
    faculty on developing the Data Science & Society Lab, a new and innovative
    course that is part of the Cornell Data Science Curriculum Initiative. The
    lab will engage undergraduate students in two components: instruction in
    theoretical tools and practical skills for analyzing social and ethical
    problems in contemporary data science (e.g., data science as ethical
    practice; fairness, justice, discrimination; privacy; openness, ownership,
    and control; or credibility of data science); and participation in
    interdisciplinary project teams that work with external partners to address
    a real-world data science & society problem.

    The Postdoctoral Researcher will have the opportunity to help launch and
    shape the initiative, to develop curriculum and engagement projects, build
    relationships with external partners and participate in teaching the
    course. S/he will work with two S&TS Department faculty members, Malte
    Ziewitz and Stephen Hilgartner, who will have primary responsibility for
    teaching the course.

    Applicants should send:

    • Cover letter summarizing the candidate’s relevant background,

    accomplishments, and fit with the position

    • CV
    • Up to two publications (or writing samples)
    • Three letters of recommendation
    • A transcript of graduate work (unofficial is acceptable)

    Required Qualifications:

    PhD in science & technology studies (STS) or related fields (e.g.,
    sociology, anthropology, law, media studies, information science) and an
    active research agenda on the social aspects of data science. ABD students
    are eligible to apply, but proof of completion of the Ph.D. degree must be
    obtained prior to beginning the position. Recent graduates who received
    their Ph.D. during the last five years are especially encouraged to apply.

    The position is available for a Summer 2019 start (as early as July 1). We
    will begin to review applications on February 28. Apply at
    https://academicjobsonline.org/ajo/jobs/13236. For further information,
    please contact Sarah Albrecht, saa9@cornell.edu.

    Diversity and inclusion are a part of Cornell University’s heritage. We are
    a recognized employer and educator valuing AA/EEO, Protected Veterans, and
    Individuals with Disabilities.

  • Mark 9:58 am on January 25, 2019 Permalink | Reply
    Tags: , data science,   

    The Politics of Big Data: Social Listening and Reclaiming the Future 

    From an interesting British Academy funded workshop last November:

  • Mark 10:46 am on June 29, 2017 Permalink | Reply
    Tags: alexander nix, , , clinton campaign, data science, , , electoral data science, , , , ,   

    The data warriors and the electoral wars they wage 

    One of the most interesting issues raised by the rise of data science in party politics is how to untangle corporate rhetoric from social reality. I have much time for the argument that we risk taking the claims of a company like Cambridge Analytica too seriously, accepting at face value what are simply marketing exercises. But the parallel risk is that we fail to take them seriously enough, dismissing important changes in how elections are fought as marketing hype propounded by digital charlatans.

    Perhaps we need to focus more on the data scientists themselves. As much as there is something of the Bond villain about Alexander Nix, CEO of Cambridge Analytica, it’s important that we don’t become preoccupied with corporate leaders. Who are the rank-and-file data scientists working on campaigns? What motivates them? How do they conceive of the work they do? There were interesting hints about this in the recent book Shattered, looking at Hilary Clinton’s failed election campaign. Much as was the case with Jeb Bush’s near entirely stalled campaign, there had been much investment in data analytics, with buy-in right from the top of the campaign. From pg 228-229:

    These young data warriors, most of whom had grown up in politics during the Obama era, behaved as though the Democratic Party had come up with an inviolable formula for winning presidential elections. It started with the “blue wall”—eighteen states, plus the District of Columbia, that had voted for the Democratic presidential nominee in every election since 1992. They accounted for 242 of the 270 electoral votes needed to win the presidency. From there, you expanded the playing field of battleground states to provide as many “paths” as possible to get the remaining 28 electoral votes. Adding to their perceived advantage, Democrats believed they’d demonstrated in Obama’s two elections that they were much more sophisticated in bringing data to bear to get their voters to the polls. For all the talk of models and algorithms, the basic thrust of campaign analytics was pretty straightforward when it came to figuring out how to move voters to the polls. The data team would collect as much information as possible about potential voters, including age, race, ethnicity, voting history, and magazine subscriptions, among other things. Each person was given a score, ranging from zero to one hundred, in each of three categories: probability of voting, probability of voting for Hillary, and probability, if they were undecided, that they could be persuaded to vote for her. These scores determined which voters got contacted by the campaign and in which manner—a television spot, an ad on their favorite website, a knock on their door, or a piece of direct mail. “It’s a grayscale,” said a campaign aide familiar with the operation. “You start with the people who are the best targets and go down until you run out of resources.”

    Understanding these ‘data warriors’ and the data practices they engage in is crucial to understanding how data science  is changing party politics. Perhaps it’s even more important than understanding high profile consultancies and the presentations of their corporate leaders.

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