(F01)
Machine learning, social learning
Location Bowland North Seminar Room 7
Date and Start Time 25 Jul, 2018 at 15:00
Sessions 1

Convenor

  • Jack Stilgoe (University College London) email

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Short abstract

Machine learning is advancing rapidly, accompanied by grand promises of hype and doom. The everyday applications of machine learning are already to be found in our smartphones and our homes and, soon, in self-driving cars. But who is doing the learning?

Long abstract

Machine learning is advancing rapidly, accompanied by grand promises of hype and doom. Self-driving cars have become a test case for the efficacy of machine learning. But this quintessentially 'smart' technology is not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking 'Who is learning, what are they learning and how are they learning?' Trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. 'Self-driving' or 'autonomous' cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.

The popular debate about machine learning focuses on what is being learnt. However, the politics of these technologies are likely to revolve around alternative questions: who is learning and how? STS has the potential to inject social learning into what is currently a narrow debate about machine learning.

This panel is closed to new paper proposals.

Papers

Making machine learn - ethnographic insights on learning algorithms in the field of robotics

Author: Arne Maibaum (Technical University of Berlin) email
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Short abstract

Machine learning algorithms claim to learn from raw data. I show, following a field observation in a robotic lab, how algorithms instead are made learning by tinkering and ask about the consequences for their application.

Long abstract

Drawing from ethnographic field studies in robotic labs I want to tackle the addressed question how machines are (made) learning. Following the implementation of a machine learning algorithm into the software of a robot that is prepared for a robotic competition my presentation will show the underlying effort of the robotic scientists that is needed for the algorithm to function. The involved tinkering of the learning input and the algorithm itself deconstructs the myth that surrounds the hype of machine learning partially.

Going from there I want illustrated how specific forms of knowledge (here the expertise of robotic engineers) are inscribed into the algorithms of robots and what consequences a thereby biased algorithm has for contexts of robotic application like security, surveillance or elderly care.

Responsible innovation as social learning

Author: Jack Stilgoe (University College London) email
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Short abstract

Reframing machine learning in terms of responsible innovation allows us to focus on who is doing the learning and how.

Long abstract

Machine learning is advancing rapidly, accompanied by grand promises of hype and doom. Self-driving cars have become a test case for the efficacy of machine learning. But this quintessentially 'smart' technology is not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking 'Who is learning, what are they learning and how are they learning?' Improving social learning by constructively engaging with the contingencies of machine learning. The popular debate about machine learning focuses on what is being learnt. STS has the potential to inject social learning into what is currently a narrow debate about machine learning.

This panel is closed to new paper proposals.