How Lidar Minimizes AI Bias in Smart Spaces Applications
Recent advances in automation have resulted innumerous reports of AI bias. Specifically, AI enabled camera surveillance suffers increasingly from analytical bias, as facial recognition becomes more commonplace.
AI bias does not arise from intelligent algorithms themselves, but rather from the data sources that underpin them. Collecting anonymous data is an ideal way to minimize AI bias.
AI systems rely on perception devices to feed data into an algorithm, which processes that data and drives responses accordingly. A response could be anything from signaling an alarm to scheduling an activity. Cameras are the most commonly used perception devices in surveillance applications today, and have been for decades, but as privacy concerns and bias issues rise, they have been viewed as problematic.
Cameras collect unnecessary biometric data such as skin color, gender, and facial features. Biometric data doesn’t just infringe on people’s privacy, but can also interfere with an algorithm’s inferences, affecting its accuracy and efficiency.
Radar partially addresses these limitations because it is anonymized, but its low angular resolution means it can suffer from poor location and spatial accuracy.
Lidar is fully anonymous, only focusing on information needed for accurate and effective analytics. Although their data output is leaner, lidar sensors are in fact more accurate than cameras and radars, because they provide true 3D images with accurate object size, location and speed information. Unlike cameras, lidars perform well in various lighting and weather conditions, making them available and reliable 24/7.
With lidars’ anonymous, accurate perception capabilities, they can be deployed in privacy-sensitive places to provide data-rich crowd analytics, using only a fraction of the data used by camera-based systems. For security applications, smart lidar systems can provide first-layer monitoring that triggers cameras or human responses only when risk verification or criminal identification is needed.
Surveillance is just one area that can benefit from lidar integration. The high accuracy, reliability, and anonymity of lidar makes it a valuable and powerful technology far beyond the autonomous vehicles space. To learn more, please read this article at Tech for Good.