Why Do Industries Need Explainable AI?

Oct 13, 2021 5:43:27 PM


Today, Artificial Intelligence (AI) has found a significant place in our lives and across a broad range of industries and businesses. But most of us, including industry stakeholders, have a very vague understanding of how AI systems make the decisions that they do. That is where Explainable AI (XAI) comes in handy, producing transparent and detailed explanations for the way AI functions.

Several of the machine learning algorithms in use today are not able to be examined after implementation to understand their logic. Deep learning neural network approaches, more so than others, especially present this knowledge gap. 

The below passages will discuss the tools that future developers will need to fully comprehend XAI. Symbiotic systems, human-AI dialogue interfaces, and various other such techniques will be discussed ahead.

The Emergence of XAI Models

The XAI paradigm is focused on the development of multiple systems by looking into two major challenge areas. One is the machine learning problem of classifying events of significance into heterogeneous multimedia data.

The second challenge to be addressed is creating decision policies for autonomous systems to perform a variety of simulations of real-life tasks. Understanding the intersection of these problem areas is helping experts form explanations for AI techniques.

Currently, experts have been able to create XAI models for systems that implement classification and reinforcement learning algorithms. More research and experiments need to be done to come up with all-encompassing XAI models. Until then we must make do with algorithm-specific custom solutions.

Levels Of Explainability

To make machine learning and AI more explainable, the implementation of deep learning approaches is essential. Scientists hope to make sufficient progress in XAI so that we have both power and accuracy to provide AI decision transparency in the coming years.

The actions and decision-making of AI should ideally be traceable to a certain level. These levels are determined by the consequences of the decisions made by the AI system or machine. Significant transparency and explanation must accompany systems with more important consequences.

Deciding these levels of transparency is a high-priority task that must be assigned to persons with experience in machine learning and its impact. As per the requirements, the levels of explainability can be standardized even if the algorithms cannot. Medical diagnosis and defense infrastructure require more standardization of explainability.

The Psychology of XAI

The most recent innovation in the field of XAI involves cognitive therapy researchers trying to understand the psychology of explanation. This is especially useful when deep neural networks make it nearly impossible to express how a model makes its decisions.

It becomes all the more essential to understand the AI model's decisions when the AI's decisions have direct consequences for human life. Cognitive psychologists' understanding of XAI has massive potential for AI's application in various nations' departments of defense. An increase in interpretability, fairness, and transparency of machine learning is tantamount to such touch-and-go industry applications of AI.

Cognitive psychologists hope to translate over 150 years of experience with the human mind into understanding AI black boxes. There are certain blind spots in our understanding of how AI works which can be filled by cognitive psychology expertise.  

Symbiotic Systems For Better AI Perception

There has been an explosion of success in AI-based applications in the past less than a decade. Constant advances in technology have led to better perception, learning and decision-making in autonomous systems. The effectiveness of these systems is completely dependent on the machines' current ability to explain their decisions and actions to human users.

More intelligent, autonomous, and symbiotic systems that combine technological advancement with human instinct are the need of the hour. Operators need XAI if they are to understand, effectively trust, and appropriately manage a growing generation of AI partners. 

XAI is a domain of knowledge that is constantly working to produce more explainable models. Experts and researchers in this domain are trying to maintain a high level of learning performance as well as prediction accuracy.

XAI prototypes must be continually tested and evaluated. This would help to arrive upon a final delivery after all this research with a toolkit library. The library would include machine learning and human-computer interface software modules. 

Third-Wave AI Systems

The oncoming wave of AI systems will have the ability to rationalize and categorize their strengths and weaknesses. They will also be able to communicate to the operators how they will behave in the future. The so-called AI third-wave of AI machines will have modified machine-learning techniques to produce more explainable models.

Upcoming XAI models will leverage state-of-the-art human-computer interface techniques. These techniques are capable of translating AI models into insightful explanation dialogues for the end-user, be it an expert or a layman. 

The current strategy to prepare for the third wave of more complex models is to pursue a variety of explanation techniques. A portfolio of methods needs to be developed for future developers to be able to gauge the performance-vs-explainability trade space. A range of design options for creating explainable interfaces is also in the works.

ALSO READ: The Growing Significance Of AI For Software Testing In 2021

Good XAI Governance For Your Business Requirements

Organizations looking to capitalize XAI also need to have governance of the operation of the AI system in addition to its explainability. Proper in-house committees or bodies need to be set up to oversee the regulation of XAI. This will also ensure that no incorrect AI systems are rolled out into circulation.

As AI becomes a more integral part of our lives and several industries, explainable AI is also growing in terms of significance. To understand how AI capabilities and its complete understanding can help your business solution stand out, book a free consultation today

Allen Victor

Written by Allen Victor

Writes content around viral technologies and strives to make them accessible for the layman. Follow his simplistic thought pieces that focus on software solutions for industry-specific pressure points.