Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving 


A machine learning model can often produce biased outputs 
for a familiar group or similar sets of classes during 
inference over an unknown dataset. The generalization of 
neural networks have been studied to resolve biases, 
which has also shown improvement in accuracy and 
performance metrics, such as precision and recall, and 
refining the dataset's validation set. Data distribution 
and instances included in test and validation-set play a 
significant role in improving the generalization of 
neural networks. For producing an unbiased AI model, it 
should not only be trained to achieve high accuracy and 
minimize false positives. The goal should be to prevent 
the dominance of one class/feature over the other 
class/feature while calculating weights. This paper 
investigates state-of-art object detection/classification 
on AI models using metrics such as selectivity score and 
cosine similarity. We focus on perception tasks for 
vehicular edge scenarios, which generally include 
collaborative tasks and model updates based on weights. 
The analysis is performed using cases that include the 
difference in data diversity, the viewpoint of the input 
class and combinations. Our results show the potential of 
using cosine similarity, selectivity score and invariance 
for measuring the training bias, which sheds light on 
developing unbiased AI models for future vehicular edge services.

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author={Katare, Dewant and Kourtellis, Nicolas and Park, Souneil and Perino, Diego and Janssen, Marijn and Ding, Aaron Yi}, 
booktitle={The Seventh ACM/IEEE Symposium on Edge Computing}, 
title={Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving},
How to cite:

Dewant Katare, Nicolas Kourtellis, Souneil Park, Diego Perino, Marijn Janssen, Aaron Yi Ding, "Bias Detection and Generalization in AI Algorithms on Edge for Autonomous Driving", in Proceedings of the Seventh ACM/IEEE Symposium on Edge Computing, 2022.