Explainable artificial intelligence (XAI)
Explainable artificial intelligence (XAI) is a collection of techniques and strategies that enable human users to grasp and trust machine learning algorithms’ results and output. Explainable AI refers to a model’s projected influence and probable biases. It contributes to the definition of model correctness, fairness, transparency, and results in AI-powered decision making. Explainable AI is critical for a business to create trust and confidence when deploying AI models. AI explainability also assists a company in taking a responsible approach to AI development.
Explainable artificial intelligence (XAI) History:
The paper that was written by Van Lent et al. in 2004 was when we first came across the term XAI. In its most basic form, XAI is an effort to make AI systems more understandable to human beings. There is an issue caused by the fact that the terms “transparency,” “interpretability,” and “explainability” are commonly used interchangeably with one another. However, there are differences between these concepts that should not be overlooked.
Why is explainable AI important?
“Black box” AI systems that forecast without explanation are problematic for a variety of reasons, including a lack of transparency and the concealment of any biases inside the system. It is essential for a business to have a comprehensive understanding of the decision-making processes involving AI, including model monitoring and AI accountability, and to avoid placing complete faith in these processes. People may find that having access to explainable AI helps them better understand and articulate machine learning (ML), deep learning, and neural networks.
Explainable artificial intelligence is one of the most important criteria for adopting responsible AI, which is a method for the large-scale adoption of AI techniques in real-world enterprises that combines accountability, explainability of models, and fairness. Businesses need to develop AI systems on the basis of trust and transparency so that they may include ethical notions into their applications and processes for artificial intelligence (AI). This will help ensure that AI is used in a responsible manner.
Explainable AI in Medical:
Medical professionals needing to know the reasoning behind a computer’s judgement is what prompted the use of XAI in this field. There is a growing need for AI methods that are not only useful, but also reliable, open, interpretable, and comprehensible by a human specialist. The public, policy, and governance all take a hit as a result, since the public’s trust in medical professionals increases in sync with the credibility of AI technology used in healthcare.
Model performance is continuously assessed using Explainable AI:
Through the use of explainable AI, businesses can assist stakeholders better understand the actions taken by AI models, which in turn aids in both troubleshooting and improving model performance. To successfully scale AI, it is necessary to investigate model behaviours by keeping track of model insights on deployment progress, fairness, quality, and drift. Comparison of model predictions, risk quantification, and performance optimization may all be achieved through continuous model assessment.
