As the spectrum around machine learning and AI continues to grow exponentially, many new things have come up to butter this transition for users. One such thing is explainable AI, which has gained immense popularity in recent times.
“The term ‘explainable AI’ or ‘interpretable AI’ refers to humans being able to easily comprehend through dynamically generated graphs or textual descriptions the path artificial intelligence technology took to make a decision.” –Keith Collins, executive vice president and CIO, SAS.
Explainable artificial intelligence (XAI) is a collection of procedures and strategies that allow human users to understand and trust machine learning algorithms' findings and output. The term "explainable AI" refers to a model's predicted impact and probable biases. It aids in the evaluation of model correctness, fairness, transparency, and results in AI-assisted decision-making.
When it comes to bringing AI models into production, an organization's ability to explain AI is critical. AI explainability also aids an organization's adoption of a responsible AI development strategy.
As AI advances, humans will find it more difficult to grasp and retrace how the computer arrived at a conclusion. The entire mathematical process is converted into what is known as a "black box" that is hard to decipher. The data is used to construct these black box models. Even the algorithm's creators, the engineers and data scientists, have no idea what's going on within them or how the AI algorithm arrived at a certain conclusion.
Explainable AI, as it is known in the business world, provides insights that lead to improved business outcomes and predicts the most desired behaviour.
First and foremost, XAI provides direct management of AI operations to the firm owner, who already understands what the machine is doing and why. It also ensures the company's safety, as all operations should follow safety regulations and be recorded if there are any breaches.
When stakeholders have the capacity to see and grasp the logic of AI systems, it helps to build trusting connections with them. New security regulations and efforts, such as GDPR, need complete commitment. All choices taken quickly are prohibited under the existing statute on the Right to Justify.
Also read: 6 major branches of AI
4 Principles of Explainable AI
These principles are heavily influenced by considering the AI system’s interaction with the human recipient of the information. The requirements of the given situation, the task at hand, and the consumer will all influence the type of explanation deemed appropriate for the situation. These situations can include, but are not limited to, regulator and legal requirements, quality control of an AI system, and customer relations.
The four principles are intended to capture a broad set of motivations, reasons, and perspectives.
Principles of Explainable AI
Before proceeding with the principles, we need to define a key term, the output of an AI system. The output is the result of a query to an AI system. The output of a system varies by task.
A loan application is an example where the output is a decision: approved or denied.
For a recommendation system, the output could be a list of recommended movies.
For a grammar checking system, the output is grammatical errors and recommended corrections.
The four principles of explainable AI are:
All outputs are accompanied with evidence or reason(s) from the systems. For each output, the Explanation principle requires AI systems to provide proof, support, or rationale. This concept does not require that the evidence be right, instructive, or understandable in and of itself; it only asserts that a system can provide an explanation.
Currently, a lot of work is being done to create and validate explainable AI techniques. Currently, a number of methods and technologies are being implemented and developed. This idea imposes no quality criterion on their explanations.
Individual users can grasp explanations provided by systems. If the recipient understands the system's explanations, the system satisfies the Meaningful principle. This concept is often met if the user understands the explanation and/or it is helpful in completing a job.
This principle does not imply that there is a one-size-fits-all answer. For a system, various explanations may be required for distinct groups of users. Because of the Meaningful principle, explanations may be customised to each user group.
Developers of a system vs. end-users of a system, lawyers/judges vs. juries, and so on are examples of large groups. These groups' objectives and desires may differ. For example, what is important to a forensic practitioner may not be the same as what is important to a jury.
This concept also allows for personalised explanations at the individual level. For a number of reasons, two people seeing the same AI system's output will not necessarily perceive it in the same manner.
(Similar read: What is Conversational AI? Works, Benefits, and Challenges)
The description accurately represents the system's output generation process. The Explanation and Meaningful principles only need a system to generate explanations that are meaningful to a user group when used together. These two principles do not demand that a system provide an explanation that accurately represents the system's output generation process.
The Explanation Accuracy principle demands that a system's explanations be accurate. Decision accuracy is not the same as explanation correctness. When it comes to decision tasks, decision accuracy relates to whether or not the system's judgement is right.
Regardless of how accurate the system's judgement is, the accompanying explanation may or may not correctly represent how it arrived at that result. Standard metrics of algorithm and system correctness have been created by AI researchers. While proven decision accuracy measurements exist, academics are currently working on performance metrics for explanation correctness.
(Read also: Artificial Intelligence vs human intelligence)
The system only works in the conditions for which it was built, or when the system's output is sufficiently reliable. The preceding concepts imply that a system operates within its knowledge boundaries. This Knowledge Limits concept argues that systems detect situations in which they were not designed or approved to function, or in which their responses are not trustworthy.
This approach protects responses by identifying and expressing knowledge limitations, ensuring that no judgement is made when it is not necessary. By eliminating deceptive, harmful, or unfair decisions or outputs, the Knowledge Limits Principle can improve confidence in a system. A system can approach its knowledge limitations in one of two ways. First, the inquiry might be outside the system's jurisdiction.
A user might, for example, upload a picture of an apple into a system designed to categorise bird species. The system might provide an answer indicating that it was unable to locate any birds in the supplied image and so could not offer an answer. This is a response as well as an explanation. The confidence of the most likely response may be too low, based on an internal confidence threshold, in the second method a knowledge limit might be reached.
For example, the input image of a bird for a bird classification system may be too fuzzy to establish its species. The algorithm may detect that the image is of a bird in this example, although it is of poor quality. “I discovered a bird in the photograph, but the image quality is too low to identify it,” for example.
(Also read: History of Artificial Intelligence)
Industries affected by explainable AI
The various industries that have grown leaps and bounds by the introduction of XAI are:
Industries affected by explainable AI
Machine learning and artificial intelligence (AI) are already being used and applied in the healthcare industry. Doctors, on the other hand, are unable to explain why certain choices or forecasts are made. As a result, there are restrictions on how and where AI technology may be used.
Doctors can use XAI to determine why a patient is at high risk of hospitalisation and what treatment is most appropriate. This allows clinicians to make decisions based on more accurate information.
(Related blog: AI in healthcare)
AI and machine learning continue to play a key role in marketing operations, with amazing potential to optimise marketing ROI thanks to the business insights they give.
Marketers must question themselves, "How can I trust the rationale behind the AI's suggestions for my marketing actions?" with such strong information that helps steer marketing tactics.
Marketers can use XAI to discover and mitigate any flaws in their AI models, resulting in more accurate results and insights they can trust. This is feasible because XAI gives them a better knowledge of projected marketing outcomes, the reasons for recommended marketing activities, and the keys to enhancing efficiency by making quicker and more accurate marketing decisions while lowering potential expenses.
With such a large influence on the insurance sector, insurers must trust, understand, and audit their AI systems in order to maximise their full potential.
For many insurers, XAI has proven to be a game-changer. Insurers report higher client acquisition and quotation conversion, increased productivity and efficiency, and lower claims rates and false claims as a result of using it.
Capital One and Bank of America are two financial organisations that are actively utilising AI technology. They aim to give financial stability, financial knowledge, and financial management to its consumers.
Financial services use XAI to offer fair, unbiased, and transparent results to their consumers and service providers. It enables financial organisations to comply with various regulatory obligations while maintaining ethical and fair practises.
Improving market forecasts, guaranteeing credit score fairness, finding variables linked with theft to decrease false positives, and reducing possible expenses caused by AI biases or errors are just a few of the ways XAI improves the financial industry.
XAI is a new and growing approach that aids people in comprehending the consequences and decisions that their AI technology suggests. With the constant advancement and application of new technologies, the ability to adapt to and comprehend these changes becomes increasingly important for businesses.
Many sectors will require XAI to comprehend AI and machine learning systems' insights, answers, and forecasts. It is now more vital than ever to embrace and learn XAI.