Accepted Paper:

Responsible innovation as social learning  


Jack Stilgoe (University College London)

Paper short abstract:

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

Paper 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.

Panel F01
Machine learning, social learning