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Teaching Machines and Turing Machines: The History of the Future of Labor and Learning
"In all things, all tasks, all jobs, women are expected to perform affective labor – caring, listening, smiling, reassuring, comforting, supporting. This work is not valued; often it is unpaid. But affective labor has become a core part of the teaching profession – even though it is, no doubt, “inefficient.” It is what we expect – stereotypically, perhaps – teachers to do. (We can debate, I think, if it’s what we reward professors for doing. We can interrogate too whether all students receive care and support; some get “no excuses,” depending on race and class.)

What happens to affective teaching labor when it runs up against robots, against automation? Even the tasks that education technology purports to now be able to automate – teaching, testing, grading – are shot through with emotion when done by humans, or at least when done by a person who’s supposed to have a caring, supportive relationship with their students. Grading essays isn’t necessarily burdensome because it’s menial, for example; grading essays is burdensome because it is affective labor; it is emotionally and intellectually exhausting.

This is part of our conundrum: teaching labor is affective not simply intellectual. Affective labor is not valued. Intellectual labor is valued in research. At both the K12 and college level, teaching of content is often seen as menial, routine, and as such replaceable by machine. Intelligent machines will soon handle the task of cultivating human intellect, or so we’re told.

Of course, we should ask what happens when we remove care from education – this is a question about labor and learning. What happens to thinking and writing when robots grade students’ essays, for example. What happens when testing is standardized, automated? What happens when the whole educational process is offloaded to the machines – to “intelligent tutoring systems,” “adaptive learning systems,” or whatever the latest description may be? What sorts of signals are we sending students?

And what sorts of signals are the machines gathering in turn? What are they learning to do?
Often, of course, we do not know the answer to those last two questions, as the code and the algorithms in education technologies (most technologies, truth be told) are hidden from us. We are becoming as law professor Frank Pasquale argues a “black box society.” And the irony is hardly lost on me that one of the promises of massive collection of student data under the guise of education technology and learning analytics is to crack open the “black box” of the human brain.

We still know so little about how the brain works, and yet, we’ve adopted a number of metaphors from our understanding of that organ to explain how computers operate: memory, language, intelligence. Of course, our notion of intelligence – its measurability – has its own history, one wrapped up in eugenics and, of course, testing (and teaching) machines. Machines now both frame and are framed by this question of intelligence, with little reflection on the intellectual and ideological baggage that we carry forward and hard-code into them."



"We’re told by some automation proponents that instead of a future of work, we will find ourselves with a future of leisure. Once the robots replace us, we will have immense personal freedom, so they say – the freedom to pursue “unproductive” tasks, the freedom to do nothing at all even, except I imagine, to continue to buy things.
On one hand that means that we must address questions of unemployment. What will we do without work? How will we make ends meet? How will this affect identity, intellectual development?

Yet despite predictions about the end of work, we are all working more. As games theorist Ian Bogost and others have observed, we seem to be in a period of hyper-employment, where we find ourselves not only working numerous jobs, but working all the time on and for technology platforms. There is no escaping email, no escaping social media. Professionally, personally – no matter what you say in your Twitter bio that your Tweets do not represent the opinions of your employer – we are always working. Computers and AI do not (yet) mark the end of work. Indeed, they may mark the opposite: we are overworked by and for machines (for, to be clear, their corporate owners).

Often, we volunteer to do this work. We are not paid for our status updates on Twitter. We are not compensated for our check-in’s in Foursquare. We don’t get kick-backs for leaving a review on Yelp. We don’t get royalties from our photos on Flickr.

We ask our students to do this volunteer labor too. They are not compensated for the data and content that they generate that is used in turn to feed the algorithms that run TurnItIn, Blackboard, Knewton, Pearson, Google, and the like. Free labor fuels our technologies: Forum moderation on Reddit – done by volunteers. Translation of the courses on Coursera and of the videos on Khan Academy – done by volunteers. The content on pretty much every “Web 2.0” platform – done by volunteers.

We are working all the time; we are working for free.

It’s being framed, as of late, as the “gig economy,” the “freelance economy,” the “sharing economy” – but mostly it’s the service economy that now comes with an app and that’s creeping into our personal not just professional lives thanks to billions of dollars in venture capital. Work is still precarious. It is low-prestige. It remains unpaid or underpaid. It is short-term. It is feminized.

We all do affective labor now, cultivating and caring for our networks. We respond to the machines, the latest version of ELIZA, typing and chatting away hoping that someone or something responds, that someone or something cares. It’s a performance of care, disguising what is the extraction of our personal data."



"Personalization. Automation. Management. The algorithms will be crafted, based on our data, ostensibly to suit us individually, more likely to suit power structures in turn that are increasingly opaque.

Programmatically, the world’s interfaces will be crafted for each of us, individually, alone. As such, I fear, we will lose our capacity to experience collectivity and resist together. I do not know what the future of unions looks like – pretty grim, I fear; but I do know that we must enhance collective action in order to resist a future of technological exploitation, dehumanization, and economic precarity. We must fight at the level of infrastructure – political infrastructure, social infrastructure, and yes technical infrastructure.

It isn’t simply that we need to resist “robots taking our jobs,” but we need to challenge the ideologies, the systems that loath collectivity, care, and creativity, and that champion some sort of Randian individual. And I think the three strands at this event – networks, identity, and praxis – can and should be leveraged to precisely those ends.

A future of teaching humans not teaching machines depends on how we respond, how we design a critical ethos for ed-tech, one that recognizes, for example, the very gendered questions at the heart of the Turing Machine’s imagined capabilities, a parlor game that tricks us into believing that machines can actually love, learn, or care."
2015  audreywatters  education  technology  academia  labor  work  emotionallabor  affect  edtech  history  highered  highereducation  teaching  schools  automation  bfskinner  behaviorism  sexism  howweteach  alanturing  turingtest  frankpasquale  eliza  ai  artificialintelligence  robots  sharingeconomy  power  control  economics  exploitation  edwardthorndike  thomasedison  bobdylan  socialmedia  ianbogost  unemployment  employment  freelancing  gigeconomy  serviceeconomy  caring  care  love  loving  learning  praxis  identity  networks  privacy  algorithms  freedom  danagoldstein  adjuncts  unions  herbertsimon  kevinkelly  arthurcclarke  sebastianthrun  ellenlagemann  sidneypressey  matthewyglesias  karelčapek  productivity  efficiency  bots  chatbots  sherryturkle 
august 2015 by robertogreco

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