Model interpretability is an important consideration in the development of any machine learning algorithm. As technology advances, so too does our ability to use artificial intelligence (AI) to process natural language. With the increasing use of large language models, the need for explainability and understanding of how the model works has become paramount. The Talking Language AI #5 project highlights the need for language model UI that allows us to understand and interact with AI models. By utilizing graphical representations of the model's inner workings, it becomes possible to gain insight into the decisions the model is making. This enables us to better understand the model's rationale and make informed decisions about the performance of the model.

Does it happen a lot with you when you have an awesome ML model for a particular use case but stake holders question your ML model in terms of transparency? In this digital world of so many anti-trust litigations and billion of dollars of penalties for breaking the regulation, a number of times stake holders are hesitant to release best solution (complex Machine Learning (ML)or Deep Learning(DL) models) instead go with rule based or linear models with easier interpretability. Is there a way to get best of both worlds? ML explainability or interpretability can help you release the best solution with reasonable explanation for the prediction. For a long time, ML models were considered as black boxes because it was almost impossible to explain what happened to the data between the input and the output.

ML is no longer just an aspirational technology exclusive to academic and research institutions; it has evolved into a mainstream technology that has the potential to benefit organizations of all sizes. However, a lack of transparency in the ML process and the black box nature of resulting models is a hindrance for improved ML adoption in industries such as financial services and healthcare. For a team developing ML models, the responsibility to explain model predictions increases as the impact of predictions on business outcomes increase. For example, consumers are likely to accept a movie recommendation from an ML model without needing an explanation. The consumer may or may not agree with the recommendation, but the need to justify the prediction is relatively low on the model developers.