Decision-making is the most crucial part in both personal and professional world. In an organisation, the right decision can increase productivity, save time and cost, make better use of resources and much more. In this competitive world, making the right decision can make an organisation a leader in its field, hence the implementation of Artificial intelligence (AI) Model. AI model however comprises of multiple AI techniques; machine learning (ML), deep learning, Artificial Neural Network (ANN) which these combination of technology makes AI algorithms used to provide prediction and recommendation in decision making more complex. This is where the role of Explainable AI or XAI become elevated. Why there is need for Explainable AI (XAI)?
Firstly, transparency. As AI model is fundamentally complex, the evident on how the model arrived at its recommendation can lack clarity. With XAI, users can have clear and comprehensive explanation of the decision-making process.
Secondly, accountability. As a decision maker, you need to be accountable in every decision being made. XAI provides an understanding of the reasoning behind each recommendation, reducing the likelihood of questions about the decision.
Thirdly, trust. An AI model being implemented should be trustworthy. Decisions that are made and will be made can affect the future of an organisation. XAI gives clear and transparent explanation to the adopted AI model, making the decision-makers more confident in accepting and implemented the recommended decisions.
Next, Compliance. Industries and organizations are subject to regulatory requirements that mandate the use of transparent and interpretable decision-making processes. The decision-making explanation provided by XAI enables organisations to meet these regulatory requirement.
Finally, biases. While AI model being advantageous in enhancing better decision making, it can be biased. This disadvantage can lead to unfair decision making. XAI helps identify and mitigate biases to ensure that decisions are fair and unbiased.
In conclusion, Explainable AI (XAI) is important for organisations that implement AI models. The complexity of AI models, which involve multiple techniques such as machine learning, deep learning, and ANN, can make decision-making processes more challenging. XAI offers transparency, accountability, trust, and compliance in decision-making processes, as well as helps identify and mitigate biases that can lead to unfair decision making. By providing clear and comprehensive explanations of the decision-making process, XAI can help decision-makers make informed and confident decisions. As industries and organisations face regulatory requirements that mandate the use of transparent and interpretable decision-making processes, XAI can help meet those requirements. Therefore, XAI plays a crucial role to help organisations achieve their goals and stay competitive in a fast-paced and constantly evolving environment.
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