Machine Learning and Artificial Intelligence are growing at the fastest pace, with numerous industries adopting them to resolve their business decisions. To expedite business-specific decisions, AI professionals work on various aspects and prepare the data for AI & ML platforms.
These systems pick appropriate algorithms, give answers based on the predictions, and deliver recommendations for the business. The stakeholders or end-users expect better clarity on the solutions these systems provide, and this grey area is termed a Black-box.
This article focuses on discussing the revolutionary concept of Explainable AI (XAI) that has transformed the way ML or AI engineering works, making the processes more convincing to businesses.
What is Explainable AI?
XAI defines a set of well-defined procedures and methods that let users recognize the output that AI & ML systems produce. Explainable AI helps AI professionals to trust the results based on the problem statements that define the AI model, expected impacts, and possible biases.
The global XAI market size is expected to leap from 3.5 billion USD in 2020 to 21 billion USD in 2030, says Research and Markets. Hence, more organizations are making efforts to adopt explainable AI to their businesses.
Explainable AI models are of three types:
- Inherently explainable models which are designed to be easy to understand.
- Black box models – designed not based on XAI principles, but need special methods to retrieve the meaning.
- Replicable models – the research results you obtain with ML models can be replicated. However, it is sometimes hard to understand whether replication is rightly performed.
Why Explainable Artificial Intelligence for AI Professionals?
Based on FICO reports, 65% of the employees failed to explain how AI models determine decisions or predictions. The key concern of business stakeholders is the limited interpretability of the systems since the present ML solutions are highly susceptible to human biases.
The majority of Data Science and AI-based projects never proceed to production and experience an 87% failure rate due to the lack of explainability, VentureBeat says. To eliminate this issue, XAI has been presented to the ML life cycle, which acts to translate black-box algorithms and explain them to help in the significant decision-making process.
With XAI, you deal with a group of techniques that helps to choose algorithms and function them at every stage of the ML process, which explains the why’s and hows of the ML model outcomes.
Various Explainability Techniques
- Model-Specific Explainability: Based on the Mxnet survey, 35% of AI engineers expressed that their colleagues failed to interpret how the models were built, while almost 47% of them were not able to present the results to such colleagues.
The benefit of these models is that they allow you to gain an in-depth understanding of decisions with a great understanding of the internal models.
- Model-Agnostic Explainability: This ML model doesn’t consider the algorithm being utilized. They are flexible unlike model-specific explainability and don’t take any model structure into account.
Model agnostic methods don’t affect the performance of the ML model and they can work without training the model.
- Model-Centric Explainability: This is a traditional explanation method, which explains how the target values and features are adjusted to apply the algorithms. It also interprets and explains how specific sets of outcomes are extracted.
- Data-Centric Explainability: This method understands the nature of data and is highly appropriate to resolve business problems. Data plays a major role in prediction and classical modeling.
Inconsistent data can lead to high chances of failure in the Machine Learning model. Data profiling, data-drift monitoring, etc. are the major data-centric explainability methods.
Explainable Artificial Intelligence Theory
There are certain benchmarks to make XAI successful in business as listed below:
- Commitment and Reliability: XAI should be able to provide a reliable explanation that leads to the model’s commitment. These are essential factors to contribute to the Root Cause Analysis.
- Perceptions and Experiences: They are important while managing the Root Cause Analysis to enable model predictions. The explanations should be human-friendly and abstract, without any overloaded details that would impact the user’s experience.
- Limiting the abnormality: Abnormality in data is a general concern, and we need to be alert about the data fed to the algorithm. It is important to have a model explanation that explains the abnormality. This is because it should help the end-user with ease of understanding the model outcomes irrespective of the repeated value in the datasets.
Five Considerations for Explainable AI
About 92% of organizations believe XAI is crucial, while few of them have already created or purchased the required explainability tools to trigger their AI systems. To obtain the necessary outcomes with explainable Artificial Intelligence, you must consider the following:
- Scan the potential biases to manage and track the fairness of the systems.
- Consider the model and provide recommendations based on the highest logical outcome. Release alerts once the models deviate from the required outcomes.
- Get alerts whenever the model undergoes any risk and analyze what occurred when the deviations continue.
- Create, execute and manage the models and then unify the tools or processes into a single platform. Clearly explain the Machine learning model dependencies.
- Implement the AI projects on public, private, and hybrid clouds. Promote confidence and trustworthiness with explainable AI.
Few XAI Industrial Use-Cases
As per the IBM Ethics survey, about 85% of IT professionals say that consumers are more likely to opt for a company that shows transparency in the creation, management, and usage of AI models. Let’s now have a look at some of the industries where XAI has a huge impact.
- Healthcare: Risks could be higher if an insecure AI system is placed in the healthcare field. The decisions that AI models make to help doctors to categorize diseases, medical imaging, etc. should be seriously considered. Once AI models make decisions, it is crucial to figure out whether these decisions are accurate since lives are at stake.
- BFSI: The capabilities of AI to assess credit risk has been widely used in the insurance and banking sectors. XAI systems can explain the decisions such as inflating and deflating prices, stock trading suggestions, etc. that have financial stakes.
- Automobile: In autonomous driving, self-driving cars are super-exciting since they make no wrong moves. When you can realize the risks of auto-pilot modes, then they can be explained and fixed under high priority.
- Manufacturing: It is necessary to diagnose and resolve equipment failures when it comes to the manufacturing of products. XAI helps professionals to understand the recommendations about maintenance standards, sensor reading, and business-specific data that pave the way for major decisions in equipment manufacturing.
The Parting Shot
Explainability is crucial to every organization while moving AI models to the production process. XAI is the most innovative evolution of AI, that offers opportunities to organizations to build unbiased and trustworthy AI applications. Organizations highly demand that professionals be XAI-ready so that they can improve the explainability of the AI and ML systems deployed in the business. Through AI Engineer Certification from a reputed institute, AI professionals can build high-level expertise in explainable AI and ensure you are an asset to the organization.