The games we play: critical complexity improves machine learning

• URL: http://arxiv.org/abs/2205.08922v1
• Date: Wed, 18 May 2022 13:37:22 GMT
• Title: The games we play: critical complexity improves machine learning
• Authors: Abeba Birhane, David J. T. Sumpter
• Abstract summary: We argue that best practice in Machine Learning should be more consistent with critical complexity perspectives than with rationalist, grand narratives. We identify thirteen 'games' played in the ML community that lend false legitimacy to models, contribute to over-promise and hype about the capabilities of artificial intelligence, lead to models that exacerbate inequality and cause discrimination.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A Large-Scale Study of Model Integration in ML-Enabled Software Systems [4.776073133338119]
  Machine learning (ML) and its embedding in systems has drastically changed the engineering of software-intensive systems. Traditionally, software engineering focuses on manually created artifacts such as source code and the process of creating them. We present the first large-scale study of real ML-enabled software systems, covering over 2,928 open source systems on GitHub.
  arXiv  Detail & Related papers  (2024-08-12T15:28:40Z)
• LLAVADI: What Matters For Multimodal Large Language Models Distillation [77.73964744238519]
  In this work, we do not propose a new efficient model structure or train small-scale MLLMs from scratch. Our studies involve training strategies, model choices, and distillation algorithms in the knowledge distillation process. By evaluating different benchmarks and proper strategy, even a 2.7B small-scale model can perform on par with larger models with 7B or 13B parameters.
  arXiv  Detail & Related papers  (2024-07-28T06:10:47Z)
• Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines [74.42485647685272]
  We focus on Generative Masked Language Models (GMLMs) We train a model to fit conditional probabilities of the data distribution via masking, which are subsequently used as inputs to a Markov Chain to draw samples from the model. We adapt the T5 model for iteratively-refined parallel decoding, achieving 2-3x speedup in machine translation with minimal sacrifice in quality.
  arXiv  Detail & Related papers  (2024-07-22T18:00:00Z)
• What is it for a Machine Learning Model to Have a Capability? [0.0]
  We develop an account of machine learning models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA), crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'
  arXiv  Detail & Related papers  (2024-05-14T23:03:52Z)
• Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation [19.22391463965126]
  Some uses of machine learning (ML) involve high-stakes and safety-critical applications. This paper investigates novel algorithms for scaling up the performance of logic-based explainers.
  arXiv  Detail & Related papers  (2024-05-14T03:42:33Z)
• Understanding the Double Descent Phenomenon in Deep Learning [49.1574468325115]
  This tutorial sets the classical statistical learning framework and introduces the double descent phenomenon. By looking at a number of examples, section 2 introduces inductive biases that appear to have a key role in double descent by selecting. section 3 explores the double descent with two linear models, and gives other points of view from recent related works.
  arXiv  Detail & Related papers  (2024-03-15T16:51:24Z)
• Logic-Based Explainability in Machine Learning [0.0]
  The operation of the most successful Machine Learning models is incomprehensible for human decision makers. In recent years, there have been efforts on devising approaches for explaining ML models. This paper overviews the ongoing research efforts on computing rigorous model-based explanations of ML models.
  arXiv  Detail & Related papers  (2022-10-24T13:43:07Z)
• The Need for Interpretable Features: Motivation and Taxonomy [69.07189753428553]
  We claim that the term "interpretable feature" is not specific nor detailed enough to capture the full extent to which features impact the usefulness of machine learning explanations. In this paper, we motivate and discuss three key lessons: 1) more attention should be given to what we refer to as the interpretable feature space, or the state of features that are useful to domain experts taking real-world actions.
  arXiv  Detail & Related papers  (2022-02-23T19:19:14Z)
• Does Explainable Machine Learning Uncover the Black Box in Vision Applications? [1.0660480034605242]
  We argue that the current philosophy behind explainable ML suffers from certain limitations. We also provide perspectives on how explainablity in ML can benefit by relying on more rigorous principles.
  arXiv  Detail & Related papers  (2021-12-18T10:37:52Z)
• Panoramic Learning with A Standardized Machine Learning Formalism [116.34627789412102]
  This paper presents a standardized equation of the learning objective, that offers a unifying understanding of diverse ML algorithms. It also provides guidance for mechanic design of new ML solutions, and serves as a promising vehicle towards panoramic learning with all experiences.
  arXiv  Detail & Related papers  (2021-08-17T17:44:38Z)
• An Information-Theoretic Approach to Personalized Explainable Machine Learning [92.53970625312665]
  We propose a simple probabilistic model for the predictions and user knowledge. We quantify the effect of an explanation by the conditional mutual information between the explanation and prediction.
  arXiv  Detail & Related papers  (2020-03-01T13:06:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.