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...
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