Prune Responsibly

09/10/2020
by   Michela Paganini, et al.
0

Irrespective of the specific definition of fairness in a machine learning application, pruning the underlying model affects it. We investigate and document the emergence and exacerbation of undesirable per-class performance imbalances, across tasks and architectures, for almost one million categories considered across over 100K image classification models that undergo a pruning process.We demonstrate the need for transparent reporting, inclusive of bias, fairness, and inclusion metrics, in real-life engineering decision-making around neural network pruning. In response to the calls for quantitative evaluation of AI models to be population-aware, we present neural network pruning as a tangible application domain where the ways in which accuracy-efficiency trade-offs disproportionately affect underrepresented or outlier groups have historically been overlooked. We provide a simple, Pareto-based framework to insert fairness considerations into value-based operating point selection processes, and to re-evaluate pruning technique choices.

READ FULL TEXT
research
08/03/2020

Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach

In the application of machine learning to real-life decision-making syst...
research
04/15/2022

Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection

Recent work has emphasized the importance of balancing competing objecti...
research
03/04/2022

FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis

Many works have shown that deep learning-based medical image classificat...
research
12/17/2020

Fairkit, Fairkit, on the Wall, Who's the Fairest of Them All? Supporting Data Scientists in Training Fair Models

Modern software relies heavily on data and machine learning, and affects...
research
08/07/2023

My Model is Unfair, Do People Even Care? Visual Design Affects Trust and Perceived Bias in Machine Learning

Machine learning technology has become ubiquitous, but, unfortunately, o...
research
03/14/2022

Ethical and Fairness Implications of Model Multiplicity

While predictive models are a purely technological feat, they may operat...
research
01/12/2023

Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning

The state of neural network pruning has been noticed to be unclear and e...

Please sign up or login with your details

Forgot password? Click here to reset