Model Multiplicity: Opportunities, Concerns, and Solutions
By Emily Black, Manish Raghavan, Solon Barocas. 2022
174 citations recorded by the source corpus.
Recent scholarship has brought attention to the fact that there often exist multiple models for a given prediction task with equal accuracy that differ in their individual-level predictions or aggregate properties. This phenomenon—which we call model multiplicity—can introduce a good deal of flexibility into the model selection process, creating a range of exciting opportunities. By demonstrating that there are many different ways of making equally accurate predictions, multiplicity gives model developers the freedom to prioritize other values in their model selection process without having to abandon their commitment to maximizing accuracy. However, multiplicity also brings to light a concerning truth: model selection on the basis of accuracy alone—the default procedure in many deployment scenarios—fails to consider what might be meaningful differences between equally accurate models with respect to other criteria such as fairness, robustness, and interpretability. Unless these criteria are taken into account explicitly, developers might end up making unnecessary trade-offs or could even mask intentional discrimination. Furthermore, the prospect that there might exist another model of equal accuracy that flips a prediction for a particular individual may lead to a crisis in justifiability: why should an individual be subject to an adverse model outcome if there exists an equally accurate model that treats them more favorably? In this work, we investigate how to take advantage of the flexibility afforded by model multiplicity while addressing the concerns with justifiability that it might raise?
Subfields: Safety, Robustness & Interpretability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Safety, Robustness & Interpretability
- Martin Strobel
- Hong Chang
- Reza Shokri
- Prakhar Ganesh
- Prateek Mittal
- Liwei Song
- Vitaly Shmatikov
- Yang Zhang
Citations in the corpus
- On The Impact of Machine Learning Randomness on Group Fairness (2023, background): “Thus, randomness during training is integral to the success of neural networks, but its impact on model behavior needs to be carefully examined [7, 10].”