Machine learning explainability refers to the ability to understand and interpret the reasoning behind the predictions made by a machine learning model. It is important for ensuring transparency and accountability in the decision-making process. Explainable AI techniques, such as feature importance analysis and model interpretability, help to provide insights into how a model arrives at its output. This can help to detect and prevent bias, increase trust in AI systems, and facilitate regulatory compliance. Model insights, also known as model interpretability or explainability, refer to the ability to understand how a machine learning model works and why it makes certain predictions or decisions.

As machine learning is increasingly deployed in high-stakes contexts affecting people's livelihoods, there have been growing calls to open the black box and to make machine learning algorithms more explainable. Providing useful explanations requires careful consideration of the needs of stakeholders, including end-users, regulators, and domain experts. Despite this need, little work has been done to facilitate inter-stakeholder conversation around explainable machine learning. To help address this gap, we conducted a closed-door, day-long workshop between academics, industry experts, legal scholars, and policymakers to develop a shared language around explainability and to understand the current shortcomings of and potential solutions for deploying explainable machine learning in service of transparency goals. We also asked participants to share case studies in deploying explainable machine learning at scale. In this paper, we provide a short summary of various case studies of explainable machine learning, lessons from those studies, and discuss open challenges.

The new European Union General Data Protection Regulation (GPDR, General Data Protection Regulation) includes regulations on how to use Machine Learning. These regulations aim to give control of personal data to the user introducing the Right to explanation. Right to explanation is a demand from the European Union to make Artificial Intelligence more transparent and ethical. This regulation promotes to build Algorithms that ensures an explanation for every Machine Learning decision. Explainability is still without a consensus on how an explanation needs to look like.