In recent years; The pace of institutions ’resort to artificial intelligence techniques accelerated with the aim of analyzing huge data and making decisions quickly and accurately. However, these advanced models often produce the outputs of a “black box” that is difficult to understand or justify. Decisions are taken, fate determined, and the reason? Not clear. Here the urgent need arises until we understand why the algorithm said what she said, and to explain how she reached what she reached, not only to build confidence with decision makers and the public; It is also to comply with increasing regulatory requirements. For example, in the financial sector, lenders are required to clarify the reasons for rejecting any loan request; It is not enough to say that “the system predicted the stumbling of the payment” without explaining the factors on which this prediction is built. The ability of institutions to explain the results of their models has become an integral part of the responsible use of these technologies, and a legal demand in some high -risk areas such as health care, financial services and criminal justice.
So the concept of explanatory artificial intelligence has emerged (Explainable AI – XAI) as a pivotal element that enables institutions to make the decisions of their smart systems understand. Interpretated artificial intelligence is intended to develop methods that make smart systems decisions clear for humans. Unlike the traditional approach in which the algorithms of automated learning remains transparent; (XAI) aims to uncover the factors that affect the predictions and clarify the decision -making mechanism.
Moreover, interpretable artificial intelligence makes the outputs of models more reliable and capable of adoption. When users and workers in the institution understand how the system connects to a specific result; They have a sense of confidence, and they tend more to accept the decision -based decision. Recent research has indicated that the institutions that adopt the explanation and interpretation in their systems gain a competitive advantage by building bridges of confidence with their fans.
Practical examples show the effect of this methodology clearly. In one of the major European banks; The adoption of interpretative models to clarify the reasons for rejecting loan requests to reduce disputes over decisions by 30%, as customers felt more understanding of the regime’s decisions, when they provided them with specific interpretations for each case. Likewise, in the field of human resources management, it was found that providing a clear explanation for the applicants about the reasons for not accepting a job through an artificial intelligence system that increases their acceptance of the decision by 42%; This indicates the role of transparency in improving the feeling of justice and the satisfaction of individuals.
And from the experience, it recently contributed to a research paper that met with a positive approval and evaluation by international arbitrators and auditors, and it is intended to present its results at the tenth international conference of computer engineering and communication (ICCCE) to be held in Malaysia at the end of this month. Where I employed one of the informed artificial intelligence tools known to be shorted as (SHAP), to analyze and predict job performance data. This tool enabled me to provide an added research value, by dismantling the complex predictions to understandable and visual components that accurately appear the factors that affected the results, and measuring the relative effect of each. This method was not satisfied with improving the prediction, but also allows decision -makers a clearer understanding of the institutional behavior associated with performance; Which makes the decision -making process more transparent and based on an applicable scientific interpretation.
The general trend is clear, explaining the decisions of artificial intelligence is no longer an additional choice; Rather, it is a strategic necessity and an ethical need for any institution that aspires to succeed and sustains in the digital age. Expenses indicate that by 2026, a large percentage of the institutions will adopt the policy policy policy by Default in its systems. The institutions that excel in integrating transparency and interpretation in their smart solutions will not be satisfied with adhering to controls; Rather, it will also gain the confidence of her fans and a tangible competitive advantage in a digital economy in which confidence is the cornerstone of the foundation.
