by Nasreen Parvez

The growing usage of Artificial Intelligence (AI) in everyday computer systems is leading us down a path where the computer makes decisions and we, the humans, must live with the consequences. In any event, there’s a lot of buzzes these days about how AI systems should be configured to provide explanations for anything they’re doing. Explainable AI (XAI) is swiftly becoming a popular topic of discussion. People who use AI systems will most likely expect and perhaps demand that they be given an explanation. Given the rapidly increasing number of AI systems, there will be a large demand for a machine-produced explanation of what the AI has done or is doing.

What areas or applications could benefit from XAI the most? Autonomous Vehicles are one such subject of study (AVs). We will gradually develop autonomous modes of transportation, with the goal of achieving the mantra “mobility for all.” Self-driving cars, self-driving trucks, self-driving motorbikes, self-driving submarines, self-driving drones, self-driving planes, and more vehicles will be available.

In genuine self-driving vehicles at Levels 4 and 5, there will be no human driver involved in the driving task. All of the people on board will be passengers and specifically, XAI will be in charge of driving.

What is Explainable AI and its benefits?

People who use AI systems will most likely expect and perhaps demand that they be given an explanation. Given the fast-increasing number of AI systems, there will be a large demand for a machine-produced explanation of what the AI has done or is doing.

The problem is that AI is frequently ambiguous, making it difficult to generate an explanation.

Consider the application of Machine Learning (ML) and Deep Learning (DL). These are data-mining and pattern-matching algorithms that look for mathematical patterns in data. The inner computing aspects can be complex at times, and they don’t always lend themselves to being discussed in a human-comprehensible and logic-based manner.

This means that the AI’s underlying design isn’t set up to provide explanations because of its structure. There are frequent attempts to introduce an XAI component in this instance. This XAI either probes the AI to figure out what happened, or it sits outside the AI and is preprogrammed to deliver answers based on what is supposed to have happened within the mathematically mysterious machinery.

How will it help people accept Autonomous Vehicle easily?

In the previous few years, autonomous driving control has advanced dramatically. Recent attempts imply that deep neural networks can be effectively used for the controllers in an end-to-end way in the suggested vehicle controllers. These models, on the other hand, are well-known for being opaque. A situation-specific dependence on visible items in the scene, which is, only attending to image areas that are causally linked to the driver’s actions, is one technique to simplify and disclose the underlying thinking. However, the attention maps that result are not always appealing or understandable to humans. Another alternative is to use natural language processing to verbalise the autonomous vehicle’s actions.

The training data, on the other hand, limits the network’s comprehension of a scene: picture portions are only attended to if they are relevant to the (training) driver’s following action. It was discovered that this results in semantically shallow models that ignore essential cues (such as pedestrians) and do not predict car behaviour as well as other indications, such as the presence of a signal or intersection.

Explainability is a crucial need of an advisable driving model—revealing the controller’s internal state is important for a user as confirmation that the system is following advice. In previous research, two methods for generating introspective explanations were found: visual attention and textual explanations. Non-salient picture regions are filtered out by visual attention, and image areas within the attended region may have a causal impact on the result (that outside cannot). It was also suggested to use a richer representation, such as text categorization, which gives pixel-by-pixel prediction and delineates object boundaries in images by attaching the anticipated attention mappings to the segmentation model’s output. The rationale for the controller’s actions is constrained by visual attention, but individual actions are not tied to specific input regions.

Presumably, a well-designed XAI will not be burdensome on the AI driving system, allowing you to have a long conversation with the XAI. In fact, the most common question asked of self-driving cars is how the AI driving system does work. The XAI should be prepared to deal with such a scenario.

The one thing we shouldn’t expect XAI to manage is inquiries that aren’t related to the driving task.

It’s about explainability, according to Bryn Balcombe, chair of the ITU Focus Group and founder of the Autonomous Drivers Alliance (ADA). If there has been mortality, whether in a collision or during surgery, the explanations after the incident help you create trust and work toward a better future.