The world is entering a new age of AI applications with dramatic success in machine learning. These autonomous systems that perceive, learn, decide, and act independently evolve at a fast pace with each advancement in the field. However, the effectiveness of these systems also depends on the ability to explain their decisions to human users.
You want the computer systems to be transparent in their decisions and reasons so that human users can understand, trust, and effectively manage the emerging machine learning models. This is where explainable AI, an emerging field in machine learning, is of vital importance. Explainable AI aims to improve the systems’ explainability by addressing how the AI’s underlying decisions are made.
Why Is It Important?
If you are an end-user of an AI system, often, you are unaware of the transformation and operations that the data input goes through before giving the final prediction. This is because the AI training uses a lot of parameters and the whole process of pre-processing, and the model building has made it into a black-box model that is hard to interpret.
In the initial stage of mainstream adoption of AI, the black-box approach was considered well as long as the results were good. But now, the focus has turned towards more transparency and explainability of the models. The reasons being:-
- Better Understanding and Explainability Lead to Widespread Adoption of Technology:- Adopting new technologies takes time as businesses need to be convinced of its benefits. Better performance and explainability of the models will lead to more businesses taking the new technology on board.
- Users are More Comfortable Using a System They Trust:- Rather than having a magical device that makes good predictions, you would prefer a technology that you can trust by knowing how it works from the inside. Making models with better transparency and explainability improves the trust factor of the AI systems.
- In Specific Sectors, Legislative Restrictions Make it a Must That the Models Used are Explainable:- When organizations are dealing with large quantities of sensitive public data, mandatory regulations makes it compulsory to explain how the data are processed and used. Insurance, banking, and healthcare are some of the sectors that come under these restrictions.
- In Critical Fields, it is Paramount That the Models Used can be Trusted Without a Hint of Doubt:- Sometimes, the AI predictions can have life-changing consequences. An example is the diagnosis and treatment of fatal diseases. There is no scope for error when implementing AI in such cases. This calls for AI systems used to be fully explainable without the slightest of doubt on their effectiveness.
- Better Explainable Models can Give Business/Research Organizations More Control Over Their Decision-Making and Thereby Extract Even Better Results from the System:- Artificial Intelligence is deployed in business to help you make better data-driven decisions and improve productivity. A better understanding and control over the model’s decision-making process makes the business adopt and integrate the AI system more effectively to their overall business process.
How Explainable AI Works?
While the machine learning model’s interpretability is inherent to it, the usage of artificial neural networks and random forests in modelling makes it hard to interpret. One way to improve the machine learning model’s explainability is by using inherently explainable algorithms such as Bayesian classifiers, decision trees, and others providing traceability and transparency.
But this means compromising on power, accuracy, and performance for explainability. You could still use complicated algorithms without losing out on explainability by adding a layer of interpretability over the top of harder to explain models.
Explainability of an AI system means:
- Machine learning models could describe how they reached conclusions accurately from the given inputs.
- Make humans able to comprehend the decision making of the machines better.
- Have the ability to trace back and inspect the decisions made by the machines.
The advent of powerful Explainable AI applications has led to great benefits to businesses regarding convenience and productivity. But there are cases when these robust AI systems lose human control and trust as their decision-making process is hard to interpret.
Power and accuracy should go hand in hand with explainability and transparency so that actions of the AI could be traced to some level, and you feel in control of the system. Explainable AI aims to bridge this gap in the explainability and transparency of machine learning models so that businesses can adopt technologies with better conviction and trust.
