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The Role of Learning Algorithms in Collective Action (2405.06582v3)

Published 10 May 2024 in cs.LG, cs.CY, and stat.ML

Abstract: Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on assessing the impact of collectives against Bayes (sub-)optimal classifiers, this perspective is limited in that it does not account for the choice of learning algorithm. Since classifiers seldom behave like Bayes classifiers and are influenced by the choice of learning algorithms along with their inherent biases, in this work we initiate the study of how the choice of the learning algorithm plays a role in the success of a collective in practical settings. Specifically, we focus on distributionally robust optimization (DRO), popular for improving a worst group error, and on the ubiquitous stochastic gradient descent (SGD), due to its inductive bias for "simpler" functions. Our empirical results, supported by a theoretical foundation, show that the effective size and success of the collective are highly dependent on properties of the learning algorithm. This highlights the necessity of taking the learning algorithm into account when studying the impact of collective action in machine learning.

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Citations (2)
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Summary

  • The paper finds that algorithm choice significantly alters collective influence, with DRO sometimes enabling smaller groups to outperform larger ones.
  • It demonstrates that SGD's simplicity bias can be strategically exploited by collectives targeting complex data features to manipulate model outcomes.
  • The study introduces the 'effective collective size' metric, showing how algorithm-induced weight adjustments affect a group's overall success.

Exploring the Impact of Learning Algorithms on Collective Action in Machine Learning

Understanding Algorithmic Impact on Collective Strategy

This paper kicks off an interesting conversation on the impact of learning algorithm choices on the efficacy of collective actions within machine learning models. Fundamentally, the paper hinges on how different algorithms alter a collective's influence when they endeavor to manipulate model outputs by adjusting the data they provide.

Dive into Specific Algorithms: DRO and SGD

Two main types of algorithms were dissected in this paper:

  1. Distributionally Robust Optimization (DRO): These algorithms optimize worst-case performance across subgroups of data, which becomes critical for ensuring fairness. The paper reveals a counterintuitive result: smaller collectives may achieve greater success under DRO compared to larger ones.
  2. Stochastic Gradient Descent (SGD): This common training methodology inherently prefers simpler solutions, which the paper posits can be exploited by a collective to achieve higher success by targeting the overlooked complex features of a learning model.

Experimenting with Weights and Validation Control

  • The concept of effective collective size was introduced. Unlike simple counting of collective members, this metric considers how algorithmically-induced weighting adjustments can amplify or diminish the influence of data points within the collective.
  • Through experiments, it was observed that certain algorithmic adjustments intended to prioritize weaker data signals (like two-stage re-weighting algorithms JTT and LFF) can inadvertently bolster the collective's influence when the collective is relatively small.
  • Particularly in models employing iterative re-weighting approaches like DRO, the point at which training is halted—often dictated by performance on a validation set—can dramatically affect collective success. If a collective has sway over this validation set, their ability to manipulate outcomes can increase significantly.

Leveraging Algorithm Bias

Switching gears, the paper explores strategic manipulations a collective could employ by exploiting known biases of algorithms, particularly in terms of complexity and simplicity:

  • Simplicity Bias in SGD: With SGD's inclination toward simpler models, a collective can increase success by embedding signals in complex aspects of data that SGD likely eschews.
  • Complexity Leverage: By tuning the complexity of the signal (such as embedding signals within more complex data features), collectives can potentially raise their success rates, particularly against algorithms that tend to ignore these complexities during training.

Theoretical and Practical Implications

The research provides compelling theoretical underpinnings to show that the choice of learning algorithm plays a critical role in determining the success of collective actions. From a practical standpoint, knowing these dynamics can help entities in designing better collective strategies or in fortifying algorithms against unwanted manipulations.

Future Avenues

The paper sets the stage for several intriguing areas:

  • Further analysis across diverse algorithms: It would be insightful to extend this examination to other popular algorithms and investigate if similar impacts on collective action are evident.
  • Role of collective information level: Investigating how the amount of information a collective has about the learning algorithm affects their ability to successfully manipulate outcomes could yield valuable insights.
  • Broader impacts on fairness and bias: There's potential to explore how these findings interact with broader issues of fairness, privacy, and robustness in machine-learning models.

In sum, this paper initiates crucial discourse on the intersection of algorithm choice and collective action efficacy in machine learning, proposing both practical guidelines for leveraging known biases and theoretical frameworks to understand these dynamics further. As machine learning continues to evolve, understanding these relationships will be paramount in harnessing or defending against collective actions within AI systems.

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