Why explainable AI is becoming essential for the modern world
By Sanjit Kumar Ghosh, Associate Professor of Practice, International School of Management Excellence (ISME), Bengaluru
Artificial intelligence is increasingly being applied to decisions that carry financial, operational and social consequences. In such settings, accuracy alone cannot be the sole criterion for evaluating an AI system.
When an algorithm influences a credit decision, assists a clinical assessment or determines which candidates are shortlisted for employment, the affected individual and the organisation using the system need to understand the basis of that outcome. This growing requirement has placed Explainable AI, or XAI, at the centre of discussions on responsible and reliable adoption of artificial intelligence.
Many contemporary machine learning models, particularly complex models based on deep learning, can produce highly accurate predictions without offering an easily interpretable account of how those predictions were generated. This characteristic is often described as the black box problem. Explainable AI seeks to address this limitation by providing methods through which the factors contributing to a model’s output can be examined and communicated in a form that is meaningful to its intended users. Explainability does not imply that every mathematical operation within a model must be exposed; rather, it concerns providing an appropriate basis for understanding and evaluating significant outputs.
The relevance of this distinction becomes evident in financial services. Consider an automated lending system that declines a business loan application. A decision without an intelligible explanation provides little assistance to either the applicant or the institution reviewing the decision.
An explainable system may indicate that particular financial characteristics, repayment history or other variables materially influenced the assessment. Such information enables the decision to be examined and, where necessary, challenged. It also assists institutions in identifying weaknesses in their models and determining whether decisions are being influenced by variables that should not carry such weight.
Explainability is therefore closely associated with accountability and model governance. AI systems are trained on data that may contain incomplete, historical or unintended patterns. If these patterns influence predictions, an apparently efficient model can reproduce or amplify existing forms of bias. The ability to examine the factors associated with an output can help technical and managerial teams investigate such behaviour. It also creates a basis for human oversight, particularly in applications where an automated decision can have material consequences for an individual.
The need for explainability is particularly relevant as regulatory attention towards artificial intelligence increases. Organisations deploying AI are expected to consider questions of transparency, fairness, accountability, privacy and risk rather than evaluating models solely on predictive performance.
Explainability can contribute to this governance framework by documenting how important decisions are generated and by establishing mechanisms through which unusual or disputed outcomes can be reviewed.
Across industries, the applications are diverse. In healthcare, an explanation of the factors contributing to an AI assisted diagnosis can support a clinician’s professional judgement. In banking, explainable models can assist in credit assessment and fraud detection. In manufacturing, explanations generated by predictive maintenance systems can help engineers understand the conditions associated with potential equipment failure. In human resources, explainability can provide greater scrutiny of automated screening processes and help organisations assess whether selection criteria are producing unintended outcomes.
There are, however, important limitations. Interpretability may involve a trade off with model complexity and predictive performance, while an explanation that is technically accurate may not necessarily be meaningful to a non technical user. Excessive disclosure may also create security or intellectual property concerns. Consequently, explainability should be designed according to the context, the nature of the decision and the needs of the stakeholder receiving the explanation. The adoption of
Explainable AI should therefore be viewed as part of a broader approach to AI governance rather than as an isolated technical capability. Organisations need to consider data quality, model validation, human oversight, security and accountability alongside interpretability.
As artificial intelligence becomes increasingly embedded in consequential business processes, the ability to examine why a system has produced a particular outcome will become an important component of establishing confidence in its use. The central question for organisations is no longer simply whether an AI model can make a prediction, but whether that prediction can be understood sufficiently to support responsible decision making.