The world of Machine learning has made algorithms more complicated with judgments that are difficult to understand and read. How can Artificial Intelligence make decisions? Since humans build them, humans ought to know it.
However, while we consider high-profile instances of Machine learning bias, it makes us understand that you fail to predict outcomes. For example, even when we know what is fed to the training data and the strategy we use to program the computer for learning, the outcomes remain unpredictable.
Since AI talent is used largely in the fields that involve critical applications like healthcare, safety, etc; we need to consider the scenario where AI would replace human supervisory positions. In such circumstances, it is significant to show how the AI was successful to make this judgment or solution.
This is where explainable AI is relevant and important to various industries, which we shall discuss in detail in the coming sections.
Explainable AI or otherwise called interpretable AI is Artificial intelligence where humans can interpret or understand the results of their solution. Explainable AI is in contrast to the ‘black box’ concept in Machine learning, in which even the designers fail to justify the decision that AI creates.
Since the field of AI has matured, there have been complex opaque models which were deployed to find solutions to difficult problems. Unlike the earlier models, these advanced models have complex architecture, making them hard to oversee or understand.
When these kinds of models don't behave as expected, the end users or developers find it difficult to prove why these methods can address the problems effectively. Explainable AI or XAI meets the growing demands of AI engineering to provide insights into the internal operations of such opaque models. Thereby, the oversights result in exponential improvements in the performance of the systems.
For instance, an IBM study revealed that the XAI platform users achieved about a 15-30% rise in the accuracy of the models and an increase of USD 4.1 - 15.6 million in terms of profits.
Let us take an example to understand what explainable AI means. The deep learning algorithms augment healthcare applications like cancer screening, where it is significant for doctors to analyze the logic behind the diagnosis algorithm. When it is a false negative, it means the patient doesn't get a life-saver treatment, while false positive means that the patient receives the invasive treatment while it is not essential.
To generate high stakes for any business, a comprehensive explanation of the Artificial Intelligence model with facts and reasoning is crucial. For instance, the explanations that benefit the system owners— say recommended music, movies, etc. are delivered to the system owner.
Here, the explainable AI should offer understandable explanations to the partners and stakeholders of a company. Various users can have numerous ways to interpret their explanations due to psychological differences. For a car driver, when AI misinterprets a plastic bag as a rock, the developer should understand the reason behind the misclassification.
The explanation accuracy metrics can help businesses to emphasize the accuracy of the explanations to their stakeholders. For example, while a loan approval algorithm should describe a decision as per the income and debt of the applicant, and the decision is made based on the zip code, the accuracy of the explanation is missing.
AI models should work within the knowledge limits t prevent discrepancies or undesirable outcomes. AI systems should identify as well as share the knowledge limits to nurture the trust factor between a company and the stakeholders. Consider a system that can classify the fish species for the aquaculture business, and has some fish debris. The system can notify that it was unable to detect a fish, instead of giving a misleading detection.
To adopt AI with efficiency, organizations should integrate ethical principles into the AI applications or processes through the development of transparent AI systems. Here are the core industries where explainable AI can make wonders.
With a machine that uses explainable AI, it can help medical staff with time to focus only on the interpretive task instead of working on repetitive tasks. XAI helps machines to evaluate data and arrive at a conclusion and also intimates the doctor, the decision lineage information to realize how it concluded.
The XAI can determine why the assembly line doesn’t work properly and how it needs adjustment over time. It is significant to improve machine understanding and to create great awareness among humans and machines.
With explainable AI, you can explain the reason why AI systems or autonomous vehicles make decisions. This is important to avoid ethical challenges like why the system misidentified a specific object or failed to fire on the target.
The XAI is important in this sector due to unexpected accidents that autonomous vehicles can cause. Here the algorithms can make safety-critical decisions with improved situational awareness against unexpected events or crashes.
The XAI can explain why the loan is approved or denied. It is essential since it helps to prevent the potential ethical drawbacks through human-machine understanding.
Financial services use Explainable AI to explain the reason behind flagging a transaction as suspicious and many other activities. It can also help you to put an end to the unfair bias or discrimination concerns while you identify the fraudulent transactions.
The definitions of explainable AI are interpreted differently in different contexts. Certain researchers consider interpretability and explainability interchangeably to define the concept of building understandable models.
Google's so-called racist AI algorithm for photo tagging to distinguish black people from Gorillas created a worldwide hassle. Similarly, another incident was in 2013, where without human oversight, Amazon AI automated platform sold T-shirts using the tagline ‘Keep Calm and Punch Her’.
Also, it lacks real-world guidance about choosing, deploying, and testing the explanations to support the project requirements.
Though there are explanations presented to grow the understanding of ML systems, the research still goes on to know how to scale the explainability to grow trust also among the non-AI experts.
AI experts should be able to supervise the AI systems and state why the business-critical decisions have been made by the AI applications of platforms. It is necessary to let the AI professionals interpret the complicated systems and understand that the models have learned exactly what they should have learned. Through AI education from a reputed AI certification course provider, an AI talent can develop models, put them into production, and adopt a versatile approach to how the systems explain the reason behind the decisions.