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Ecosystem-level Analysis Reveals Systemic Failures and Homogeneous Outcomes in Deployed Machine Learning


Core Concepts
Deployed machine learning models exhibit systemic failures, where individuals are exclusively misclassified by all available models, and homogeneous outcomes, where models either consistently succeed or fail on the same instances. These trends persist even as individual models improve over time, with model improvements primarily benefiting those already correctly classified by other models.
Abstract
The authors introduce ecosystem-level analysis as a methodology to study the societal impact of deployed machine learning systems. Rather than analyzing individual models in isolation, ecosystem-level analysis considers the collective behavior of all models deployed in a given context. The key findings are: Homogeneous Outcomes: Across three modalities (text, images, speech) and eleven datasets, the authors find that deployed machine learning exhibits systemic failures, where some users are exclusively misclassified by all available models, as well as consistent classification success, where some users are correctly classified by all models. These homogeneous outcomes occur at rates significantly higher than would be expected if model failures were independent. Model Improvements and Systemic Failures: When individual models improve over time, the benefits of these improvements predominantly accrue to individuals who are already correctly classified by other models. Improvements rarely reduce the prevalence of systemic failures. Racial Disparities in Medical Imaging: Applying ecosystem-level analysis to medical imaging for dermatology, the authors find that while both models and humans exhibit racial performance disparities, models show a new form of racial disparity where they are more homogeneous in their predictions for darker skin tones. The authors argue that ecosystem-level analysis is a valuable tool for holistically understanding the societal impact of deployed machine learning systems, as it captures the cumulative outcomes experienced by individuals interacting with multiple models.
Stats
Across 11 datasets spanning text, images, and speech, the systemic failure rate (fraction of instances misclassified by all models) ranges from 0.002 to 0.181. When a model improves, on average only 10% of the instance-level improvement occurs on instances misclassified by all other models. For the dermatology dataset DDI, the observed systemic failure rate for the darkest skin tones is 8.2% higher than the baseline, while for the lightest skin tones it is 1.5% lower than the baseline.
Quotes
"Precisely 0 out of the model's 303 improvements are on instances on which all other models had failed." "On average, just 10% of the instance-level improvement of a single commercial system occurs on instances misclassified by all other models." "Models are more homogenous when evaluating images with darker skin tones, meaning that all systems agree in their correct or incorrect classification, whereas human homogeneity is consistent across skin tones."

Deeper Inquiries

How do the architectural similarities, training data, and learning objectives shared across deployed machine learning models contribute to the observed homogeneous outcomes?

The architectural similarities, shared training data, and learning objectives across deployed machine learning models contribute to the observed homogeneous outcomes in several ways: Architectural Similarities: When multiple machine learning models share similar architectures, they are likely to make similar mistakes or have similar blind spots. If these models are based on the same foundational models or methodologies, they may exhibit similar biases or limitations. As a result, when faced with challenging or ambiguous cases, these models are more likely to make the same errors, leading to homogeneous outcomes. Shared Training Data: If multiple models are trained on the same or similar datasets, they will learn from the same examples and patterns. This can lead to models making similar predictions on instances that they were not explicitly trained on. If the training data is biased or lacks diversity, this shared bias or lack of representation can manifest in homogeneous outcomes across models. Learning Objectives: Models with similar learning objectives will prioritize certain features or patterns in the data. If these objectives are narrow or biased, the models will focus on similar aspects of the data, potentially leading to consistent errors or misclassifications. This alignment in learning objectives can result in models reaching the same incorrect conclusions, contributing to homogeneous outcomes. Algorithmic Monoculture: The prevalence of algorithmic monoculture, where many deployed systems rely on the same underlying models or components, exacerbates the issue. If a dominant model or approach is widely adopted, it can propagate homogeneous outcomes across different applications and contexts, amplifying the impact of shared architectural similarities, training data, and learning objectives. In summary, the convergence of architectural similarities, shared training data, and learning objectives in deployed machine learning models creates a scenario where models are more likely to make similar decisions, leading to the observed homogeneous outcomes.

How can ecosystem-level analysis be extended to capture the dynamic, interactive nature of how individuals engage with multiple machine learning systems over time?

Extending ecosystem-level analysis to capture the dynamic and interactive nature of how individuals engage with multiple machine learning systems over time requires a comprehensive approach that considers the evolving relationships between users, models, and decision-making processes. Here are some key strategies to enhance ecosystem-level analysis in this context: Longitudinal Data Collection: Collecting longitudinal data that tracks individual interactions with multiple machine learning systems over time is essential. This data should capture changes in user behavior, model performance, and outcomes to understand how ecosystem dynamics evolve. User Feedback Mechanisms: Implementing robust user feedback mechanisms can provide valuable insights into user experiences and preferences. By incorporating user feedback into the analysis, researchers can assess how individuals adapt their choices based on past interactions with machine learning systems. Model Versioning and Updates: Tracking model versions and updates is crucial for understanding how changes in individual models impact ecosystem-level outcomes. Analyzing the effects of model improvements, updates, or changes on user experiences can reveal trends in system performance over time. Contextual Considerations: Considering the context in which individuals interact with machine learning systems is vital. Factors such as user demographics, task complexity, and environmental variables can influence how users navigate multiple systems and make decisions. Incorporating these contextual considerations into the analysis can provide a more nuanced understanding of ecosystem dynamics. Network Analysis: Applying network analysis techniques to model the relationships between users, models, and outcomes can uncover patterns of influence and interaction within the ecosystem. By visualizing the network of interactions, researchers can identify key nodes, trends, and feedback loops that shape ecosystem dynamics. By integrating these strategies and approaches, ecosystem-level analysis can be extended to capture the dynamic and interactive nature of how individuals engage with multiple machine learning systems over time. This holistic perspective can offer valuable insights into the evolving impact of machine learning on society and individuals.
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