How can you make your NLP model transparent and explainable?

1 Why transparency and explainability matter

Transparency and explainability are two key aspects of ethical and responsible AI. Transparency involves disclosing how your model was built, trained, and deployed, as well as what data, assumptions, and objectives it used. Explainability means providing understandable and meaningful reasons for your model's decisions, predictions, and outputs. By making your NLP model transparent and explainable, you can improve its quality and accuracy by identifying errors, biases, and limitations. It can also enhance the user experience by providing clear feedback, guidance, and options. Additionally, it can increase the trust of users and stakeholders by demonstrating that the model is reliable, fair, and accountable. Moreover, it can help you comply with ethical and legal standards for your domain and use case.

2 How to make your NLP model transparent

Making your NLP model transparent involves documenting and communicating various aspects, such as the data used to train, test, and validate the model, the model architecture, performance metrics and benchmarks, and deployment. To document and communicate your model transparency, you can use code comments and documentation, data dictionaries and schemas, model cards and reports, and dashboards and visualizations. For example, data dictionaries and schemas can help explain the structure, format, and meaning of your data. Model cards and reports can summarize the goal, scope, inputs, outputs, performance, limitations, and biases of your model. Dashboards and visualizations can display information about the data, architecture, performance, and deployment of your model.

3 How to make your NLP model explainable

Making your NLP model explainable requires providing and generating global, local, and counterfactual explanations. Global explanations describe the overall goal and function of your model, how it processes input data, and the main factors and features that influence its output. Local explanations focus on why your model produced a certain output for a specific input, its confidence level, and the relevant words or phrases that support or contradict it. Counterfactual explanations address how your model would work differently if certain aspects of the input, output, or instance were changed.

Tools such as annotation and feedback can be used to explain the input and output data of your model, while attribution and saliency can be used to identify the importance of input features or words for the model's output. Interpretation and generation can be used to explain the logic and reasoning of your model's output, as well as create natural language explanations that describe and justify it.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?