Explainable AI, a blog by CEAIExplainable AI (XAI): The Need for Trust


A trust-based approach is essential for users to accept AI-based solutions and systems that take their decisions into account. AI no longer remains the future. It is now here in the present. Artificial intelligence is present everywhere, from your living room to your car to your office to your pocket. Technology advances have led to an important question: Can and should we place our trust in AI systems? 


What is Explainable AI(XAI)?

Explainable AI is a collection of techniques and procedures that make it possible for human users to understand and believe the output and results produced by machine learning algorithms. Explainable AI is a term used to describe an AI model, its anticipated effect, and any biases that may exist. It contributes to defining model accuracy, fairness, transparency, and results in decision-making supported by AI. When an organisation puts AI models into production, Explainable AI is essential for fostering confidence and trust. A company can adopt a responsible approach to AI development with the aid of AI explainability.


Simply put, explainable AI is AI that is transparent in its operations so that humans can trust its decisions. Explainable AI doesn’t have to be about understanding the intricacies of the entire model, but about understanding what factors can affect its results. Understanding how a model operates and understanding why it produces a particular result are two very different things.


There are three types of explanations in Explainable AI. 



As opposed to explaining how a prediction or decision is reached, global explanations reveal what the system is doing as a whole. These reports often include summaries of how a system uses a feature to make a prediction, as well as meta information, such as what type of data was used to train the system.


Local Explanations

Local explanations explain how the model made a specific prediction. A model might use features to generate an output, or flaws in input data might affect that output.


Social Influence

It describes how “socially relevant” others, such as users, respond to predictions from a system. These types of explanations may include statistics about model adoption, or user rankings based on similar characteristics (e.g., people over a certain age).

Why Explainable AI


Why Explainable AI(XAI)? 

Artificial Intelligence could fundamentally alter the world. In addition to operating self-driving cars and improving human intelligence, it could also help treat cancer. 


Decision-making processes like loan disbursements, employment placements, and medical diagnoses are just a few of the critical decisions that AI algorithms are assisting businesses with. This means that an unreliable AI program could result in lawsuits, regulatory scrutiny, and a loss of clients, revenue, and reputation. Or a potential robot apocalypse.  


So, is there a way to make AI systems more trustworthy?


The solution is to increase transparency. AI systems will eventually select the correct, unbiased algorithms by adhering to the principles of transparency and explainability. This means that an AI system that is transparent will be able to explain why it made specific predictions that resulted in a particular decision or why it refrained from making other predictions in a particular situation.


One way to make AI more reliable is to create explainable AI that allows users to comprehend how it came to that specific result. For any organization to develop trust and confidence in its AI systems, explainable AI becomes crucial. By offering techniques and methods to produce justifications for the AI being used and the decisions it makes, explainable AI aids in gaining public trust. 



Understanding why decisions are made is necessary for the effective use of machine learning. Delivering consistent results depends on being aware of the data that was used to make a decision. 


When interpretability is incorporated into AI systems, there are significant business benefits. There are many advantages to being proactive and making an investment in explainability now, in addition to helping to address pressures like regulation and adopting good practices around accountability and ethics. The AI can be deployed more quickly and widely the more confidence there is in it.


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