The games we play: critical complexity improves machine learning

by   Abeba Birhane, et al.

When mathematical modelling is applied to capture a complex system, multiple models are often created that characterize different aspects of that system. Often, a model at one level will produce a prediction which is contradictory at another level but both models are accepted because they are both useful. Rather than aiming to build a single unified model of a complex system, the modeller acknowledges the infinity of ways of capturing the system of interest, while offering their own specific insight. We refer to this pragmatic applied approach to complex systems – one which acknowledges that they are incompressible, dynamic, nonlinear, historical, contextual, and value-laden – as Open Machine Learning (Open ML). In this paper we define Open ML and contrast it with some of the grand narratives of 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. To achieve this, 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 inequality and 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.


On the Value of ML Models

We argue that, when establishing and benchmarking Machine Learning (ML) ...

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression

We introduce a novel machine learning strategy, kernel addition Gaussian...

Theories of Parenting and their Application to Artificial Intelligence

As machine learning (ML) systems have advanced, they have acquired more ...

Generalised linear models for prognosis and intervention: Theory, practice, and implications for machine learning

In health research, machine learning (ML) is often hailed as the new fro...

A Critical Evaluation of Open-World Machine Learning

Open-world machine learning (ML) combines closed-world models trained on...

Simulating Performance of ML Systems with Offline Profiling

We advocate that simulation based on offline profiling is a promising ap...

Open-plan Glare Evaluator (OGE): A New Glare Prediction Model for Open-Plan Offices Using Machine Learning Algorithms

Predicting discomfort glare in open-plan offices is a challenging proble...

Please sign up or login with your details

Forgot password? Click here to reset