GC Digital Initiatives at the CUNY Graduate Center

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GC Digital Initiatives at the CUNY Graduate Center

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Workshop session on machine learning’s opacity

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    Achim Koh
    Participant

    Hi everyone,
    (sorry if you are getting duplicate messages)

    I will be hosting a workshop next Wednesday evening where we sit and talk
    about what people mean when they say machine learning algorithms are like
    black boxes. We will also look at what some people out there are doing
    about it. Anyone interested, feel free to RSVP or contact me (
    akoh@gradcenter.cuny.edu) for further information. Thanks!

    Achim

    RSVP: https://goo.gl/forms/nYyqfKc9XBXcpnzc2

    *Machine learning: black box*

    Wed 5/3 6:30 – 8:30
    Graduate Center, Room 8203

    Workshop session led by Achim Koh (MA in Liberal Studies)

    Machine learning algorithms are becoming increasingly important elements of
    the computational processes operating in the contemporary society, in
    fields such as finance, journalism, advertising, medicine, education,
    translation, robotics, and much more. Along with the prospect of efficiency
    through automation and new possibilities in the field of artificial
    intelligence, this technological change also raises concerns about the
    sociopolitics of, for example, who will benefit from it and who won’t.
    Critiques of machine learning and its application frequently point to its
    opacity, its being a ‘black box.’ In this session we will try to articulate
    more precisely what this metaphor means, and look at some works that engage
    with the critique of machine learning as black box.

    This session is meant to be introductory and assumes participants from
    diverse levels of familiarity with the topic. If your familiarity is that
    you have heard of the term machine learning, this could be an introduction
    to the basic terminology and why people are expressing concerns over it. If
    you are a computer scientist, it could serve as a primer to a social
    sciences/humanities perspective on the field. People in the middle are
    welcome, too!

    We will address some of the basic technical terms in the session, including
    the definition of machine learning. Still, skimming over the items in the
    following link could be helpful for context:
    https://www.are.na/critical-machine-learning/primers
    If you don’t have time, the AI Literacy piece by Liza Daly and the
    Artificial Intelligence, Revealed videos by Facebook (the first two) are
    short and easy introductions to terms such as machine learning, AI, and
    deep learning.

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