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

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

Katare, D. (TU Delft Information and Communication Technology)
Kourtellis, Nicolas (Telefonica Research)
Park, Souneil (Telefonica Research)
Perino, Diego (Telefonica Research)
Janssen, M.F.W.H.A. (TU Delft Engineering, Systems and Services)
Ding, Aaron Yi (TU Delft Information and Communication Technology)

Engineering, Systems and Services

2023

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.

http://resolver.tudelft.nl/uuid:9882e6e2-1689-45cd-83bc-b94df32d241d

https://doi.org/10.1109/SEC54971.2022.00050

2023-07-01

9781665486118

Proceedings - 2022 IEEE/ACM 7th Symposium on Edge Computing, SEC 2022

IEEE/ACM Symposium on Edge Computing (SEC), 2022-12-05 → 2022-12-08, Seattle, United States

Proceedings - 2022 IEEE/ACM 7th Symposium on Edge Computing, SEC 2022

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 D. Katare, Nicolas Kourtellis, Souneil Park, Diego Perino, M.F.W.H.A. Janssen, Aaron Yi Ding