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Curating Explanations of Machine Learning Models for Business Stakeholders

Consumer-oriented products and services are increasingly incorporating artificial intelligence (AI) functionality or being influenced by machine learning in development; for example, AI is replacing rule-based systems in identifying anomalous behavior or selecting services for customers. With AI's increasing uses and impact on consumers, explainability becomes required by consumers, business stakeholders, and regulator. Many of relevant researchers have been geared toward End Users and Technical Audiences, especially in the space of feature importance and local explainability. However, Business Sponsors and Regulators have different requirements which can be further explored. We present a framework of potential requirements and concerns encompassing these stakeholders, including End Users, Technical Audiences, Business Sponsors and Regulators. Finally, we apply the framework to an example in the financial services space.