( SDG ) by making AI more accessible and reliable for applications in areas like healthcare , agriculture , and disaster management , ensuring that AI can be trusted and effectively used in real-world scenarios ,” explains Fred .
These include visualisation tools , which use techniques like attention maps and saliency maps to track how these models make decisions , such as highlighting which parts of the input data are most important for a given output .
Explainable AI techniques like LIME ( Local Interpretable Model-agnostic Explanations ) and SHAP ( SHapley Additive exPlanations ) can also help interpret model predictions by highlighting important features .
And perhaps most importantly , developing inherently interpretable models , such as decision trees or rule -based systems , alongside deep learning models that put demystification at the very heart of any AI project .
“ Future developments of LLMs will likely focus on enhancing interpretability and reducing biases to foster greater trust , ethical use and regulatory compliance ,” explains Pramod .
While increased transparency in LLMs offers numerous benefits , it also comes with potential risks .
98 November 2024