Abstract
Bias in machine learning is a significant problem that demands industry-wide attention, and in the case of driverless vehicles, life and death are at stake. The debate is whether autonomous vehicles are safe, yet more likely to strike a pedestrian of color than a person of white skin color. It is essential to obtain a greater understanding of the algorithmic bias that occurs during driving-centric object recognition. Major automakers plan to develop cars with a degree of autonomy between Level 4 and Level 5 within the next decade. At Level 5, the system is meant to behave similarly to a human driver; it can drive anyplace lawful and can make independent decisions (SAE International 2016).
Recommended Citation
Majumdar, Swarnamouli and Deokar, Amit V., "Toward a framework for mitigating object detection decision making bias in driverless cars" (2022). NEAIS 2022 Proceedings. 23.
https://aisel.aisnet.org/neais2022/23