The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes
arXiv:2402.08922v3 Announce Type: replace Abstract: Large-scale black-box models have become ubiquitous across numerous applications. Understanding the influence of individual training data sources on predictions made by these models is crucial for improving their trustworthiness. Current influence estimation techniques involve computing gradients for every training point or repeated training on different subsets. These approaches face obvious computational challenges when scaled up to large datasets and models. In this paper, we introduce and explore the Mirrored Influence Hypothesis, highlig
The proliferation of very large, opaque AI models has made understanding their behavior and trustworthiness a pressing concern, necessitating more efficient techniques for model explainability and auditing.
Efficient data influence estimation is critical for improving trust, accountability, and the interpretability of large AI models, particularly as they are deployed in high-stakes applications.
This research proposes a new, more computationally efficient method for understanding how individual training data points affect AI model predictions, potentially democratizing access to influence estimation techniques that were previously prohibitive.
- · AI researchers
- · ML ethicists
- · Companies using large-scale AI models
- · Users of AI systems
- · Inefficient influence estimation methods
- · Techniques requiring repeated full model retraining
More widespread adoption of influence estimation methods for large AI models.
Improved model debugging, fairness auditing, and data curation leading to more robust and ethical AI systems.
Enhanced regulatory frameworks for AI based on a deeper understanding of model behavior and data dependencies.
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Read at arXiv cs.LG