Different AI models are being built by various organizations. These models are used for decision making in various parts of the organization. The decisions are based on ML number crunching and algorithms. However, these are not clear to others. Thus, explainable AI becomes relevant to comprehend how the models arrive at a decision.
Till the 1980s, AI systems were generally rule-based. The decision-making pathway could be traced. Later the models become complex with billions of parameters. Some pathways to decision making are clear such as Decision Trees, while most other pathways are opaque. Explainable AI should play a role here, especially in sectors such as healthcare and finance.
These are the days of generative AI. The issue is here the model generates new data or content and whether explainable AI has any relevance here. Explainable AI makes the model transparent and accurate. There are LLMs for different domains. The output generated by these models must not be fallacious. There is constantly evolving technology, and LLMs still have not reached a stable state. The outputs from these models would not be effective, and there could be hallucinations. Explainable AI can rein in LLMs.
The models should be assessed on how they predict responses, the coherence shown by them and the quality of output. Explainable AI increases the trust quotient of generative AI models. If the process is understood, it is easier to implement safety protocols.
It goes without saying that explainable AI itself should evolve to remain relevant for generative AI. There should be a unified definition of explainable AI. XAI methods should adopt feedback loops to improve the models and their outputs.
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