The term "explainable AI" refers to a mathematical model of artificial intelligence (AI), its effects, and biases. It contributes to defining the accuracy, fairness, transparency, and outcomes of models used in data-driven decision making.
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Current understanding of an algorithm
As AI becomes increasingly embedded into our lives, our dependence on its decision-making increases. Understanding artificial intelligence and its reasoning capabilities becomes essential for our confidence in this potentially revolutionary technology. However, as AI's decisions get more complex and abstract, humans become more disconnected from this understanding.
Explainable artificial intelligence is concerned with explaining why and how an algorithm arrived at a decision. Although the subject of explainable AI is not new, recent academic achievements and various applications of AI have re-energized efforts to explain it. Despite growing interest in algorithm explainability, the AI community remains split on whether it is a viable study subject. According to some, thorough testing is sufficient to provide an explanation—that one can infer a model's logic just by seeing its behavior under a number of different conditions.
Why is it essential to understand how the black box works?
Explainable AI must reveal a model's real limitations, so that users are aware of the parameters within which the model may be trusted. This is especially important considering the public's apparent propensity to accept AI explanations without question. To avoid consumers placing incorrect trust in AI models, their descriptions must properly depict the model's functioning.
Numerous companies are required to comply with a variety of regional, national, and international regulations, and a growing number of artificial intelligence models are integrated and turned into products. Certain highly regulated industries, such as banking or insurance, are already obliged to explain their model predictions.
The process of developing a high-performance algorithm is iterative and gradual in nature. Explanations may help accelerate this process by helping in the identification of model error sources. For example, when erroneous predictions are made, this technique may highlight problems with the training datasets.
Bias is a multifaceted problem that may show itself at any stage of the artificial intelligence process, not only during model building. Explainable AI may be advantageous for finding bias in AI models; it is a method for detecting bias in models.
Explainable mathematical models have the potential to significantly enhance our systems' dependability, compliance, efficacy, fairness, and robustness, thus boosting adoption and economic value.
