The increasing use of Artificial Intelligence (AI) in everyday computer systems is leading us down a path in which the computer makes decisions and we, as humans, must live with the consequences. In any case, there are a lot of talks these days about how AI systems should be set up to provide explanations for everything they do.
Explainable AI (XAI) is quickly becoming a hot topic of conversation. People who use AI systems will almost certainly expect, if not demand, an explanation. Given the rapidly growing number of AI systems, there will be a high demand for a machine-generated explanation of what the AI has done or is doing.
What areas or applications stand to benefit the most from XAI? One such area of research is autonomous vehicles (AVs). We will gradually develop self-driving vehicles to achieve the mantra “mobility for all.” There will be self-driving cars, self-driving trucks, self-driving motorcycles, self-driving submarines, self-driving drones, self-driving planes, and other vehicles.
There will be no human driver involved in the driving task in genuine self-driving vehicles at Levels 4 and 5. All the people on board will be passengers, and XAI will be in charge of driving.
Explainable AI & its advantages
People who use AI systems will almost certainly expect, if not demand, an explanation. Given the rapidly growing number of AI systems, there will be a high demand for a machine-generated explanation of what the AI has done or is doing.
The issue is that artificial intelligence is frequently ambiguous, making it difficult to generate an explanation.
Consider the way Machine Learning (ML) and Deep Learning (DL) can be utilized. These are algorithms for data mining and pattern matching that search for mathematical patterns in data. The inner workings of computers can be complicated at times, and they don’t always lend themselves to a discussion in a human-comprehensible and logic-based manner.
Because of its structure, the AI’s underlying design isn’t set up to provide explanations. In this case, attempts to introduce an XAI component are common. This XAI either probes the AI to determine what happened, or it sits outside the AI and is preprogrammed to provide answers based on what is supposed to have occurred within the mathematically mysterious machinery.
Role of XAI in aiding people to accept Autonomous Vehicle easily
Autonomous driving control has come a long way in the last few years. Recent attempts suggest that deep neural networks can be used effectively for controllers in the proposed vehicle controllers in an end-end way start. These models, on the other hand, are familiar for their opacity. A situation-specific reliance on visible items in the scene, that is, only attending to image areas causally linked to the driver’s actions, is one technique for simplifying and revealing the underlying thinking.
However, the resulting attention maps are not always appealing or understandable to humans. Another option is to use natural language processing to verbalize the actions of the autonomous vehicle.
The training data, on the other hand, limits the network’s comprehension of a scene: image segments are only attended to if they are relevant to the (training) driver’s next action. This results in semantically shallow models that ignore important cues (like pedestrians) and do not predict car behavior as well as other indicators, like the presence of a signal or intersection.
Explainability is a critical requirement of a good driving model—revealing the controller’s internal state is important for a user as confirmation that the system is following instructions. Previous research discovered two methods for producing introspective explanations: visual attention and textual explanations. Visual attention filters out non-salient image regions and image areas within the attended region may have a causal impact on the outcome (that outside cannot).
It was also suggested that a richer representation, like text categorization, be used, which provides pixel-by-pixel prediction and delineates object boundaries in images by attaching the expected attention mappings to the segmentation model’s output. The controller’s actions are limited by visual attention, but individual actions are not tied to specific input regions.
A well-designed XAI, presumably, will not be taxing on the AI driving system, allowing us to have a lengthy conversation with the XAI. The most frequently asked question about self-driving cars is how the AI driving system works. The XAI should be ready to handle such a scenario.
We shouldn’t expect XAI to handle inquiries that aren’t related to the driving task.
According to Bryn Balcombe, chair of the ITU Focus Group and founder of the Autonomous Drivers Alliance (ADA), it’s all about explainability. If there is a fatality, whether in a collision or during surgery, the explanations provided after the incident assist you in building trust and working toward a better future.
