The capability of Artificial Intelligence (AI) is expanding. Prior to the introduction of AI in the 1950s, more and more AI technologies have been introduced for implementation until today. Recently, the impact of AI in daily life has become more prevalent, as an AI tool (ChatGPT) based on deep learning and generative AI has become popular among people. Undoubtedly, AI is a helpful technology, and one of its most significant applications is how it assists leaders in making effective decisions for their organizations.
Decision-making is essential. It determines the success or failure of a business plan and can even impact the productivity of workers. Decision-making relies on numerous data analyses, and the best way to make use of it is to integrate it with an AI model that suits your organization’s needs. AI models that apply various AI techniques and processes such as machine learning and deep learning are complex. Therefore, the way they arrive at a recommendation or prediction is hardly interpretable. Nevertheless, as technology advances, the rise of Explainable AI or XAI has made these AI models understandable.
XAI is and will become increasingly significant in the future, as adoption of AI models to enhance decision-making continues to increase. It is designed to instill trust, reliability, and confidence in an AI model. So, how does it work in achieving these objectives?
Basically, XAI relies on three important techniques; prediction accuracy, traceability and decision understanding. Together, the techniques address every question that may arise about a decision made by an AI model, from the “whys” to the “hows”.”
1. Prediction accuracy
Prediction accuracy in XAI is the ability of an AI model to accurately predict outcomes or results. This can be achieve through techniques such as
Local Interpretable Model-Agnostic Explanations (LIME) – a technique that explains the predictions of complex machine learning models by perturbing the input data and observing the changes in the model’s output.
SHapley Additive exPlanations (SHAP) – a technique used to explain the predictions of machine learning model that assigns a contribution score to each feature according to importance in the prediction to gives more global explanations.
Counterfactual Explanations – A method that provides explanations by generating a set of changes to the input data that would result in a different prediction.
Traceability refers to the ability to track and understand the decisions made by an AI model. This technique can be achieved by limiting the ways in which decisions are made and setting up a more focused scope for machine learning rules and features. For instance, DeepLiFT, which is short for Deep Learning Important Features, is an algorithm designed to be applied on top of deep neural network predictions. This algorithm observes the activation of each neuron, shows a traceable link between each activated neuron, and reveals dependencies between them.
3. Decision Understanding
In XAI, decision understanding refers to the ability of users to comprehend and interpret the decisions made by an AI model. This technique is achieved by educating the team working with the AI models to understand how and why decisions are made.
Overall, the importance of Explainable AI in making AI models understandable is expected to grow as more and more organizations implement it to improve their decision-making processes. By implementing XAI techniques, organizations can ensure that their AI models are transparent, interpretable, and trustworthy, ultimately improving their ability to make data-driven decisions and increase productivity.
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