Critical Complexity in Open Machine Learning

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Source arXiv

Computers and Society

Critical Complexity in Open Machine Learning

The games we play: critical complexity improves machine learning

In this paper we define the concept of open machine learning (open ml) and contrast it with some of the grand narratives of machine learning (ml) of two forms : 1) closed ml,ml which emphasizes learning with minimal human input (e.g.

Google s alphazero) and 2) partially open ml, ml which is used to parameterize existing models.

We use theories of critical complexity to both evaluate these grand narratives and contrast them with the open ml approach.

Specifically, we deconstruct grand ml `theories'by identifying thirteen'games'played in the ml community.

These games lend false legitimacy to models, contribute to over-promise and hype about the capabilities of artificial intelligence, reduce wider participation in the subject, lead to models that exacerbate inequalityand cause discrimination and ultimately stifle creativity in research.

We argue that best practice in ml should be more consistent with critical complexity perspectives than with rationalist, grand narratives.

Authors

Abeba Birhane, David J. T. Sumpter

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