Many high-performing machine learning models are not interpretable. As they are increasingly used in decision scenarios that can critically affect individuals, it is necessary to develop tools to better understand their outputs. Popular explanation methods include contrastive explanations. However, they suffer several shortcomings, among others an insufficient incorporation of background knowledge, and a lack of interactivity. While (dialogue-like) interactivity is important to better communicate an explanation, background knowledge has the potential to significantly improve their quality, e.g., by adapting the explanation to the needs of the end-user. To close this gap, we present REASONX, an explanation tool based on Constraint Logic Programming (CLP). REASONX provides interactive contrastive explanations that can be augmented by background knowledge, and allows to operate under a setting of under-specified information, leading to increased flexibility in the provided explanations. REASONX computes factual and constrative decision rules, as well as closest constrative examples. It provides explanations for decision trees, which can be the ML models under analysis, or global/local surrogate models of any ML model. While the core part of REASONX is built on CLP, we also provide a program layer that allows to compute the explanations via Python, making the tool accessible to a wider audience. We illustrate the capability of REASONX on a synthetic data set, and on a a well-developed example in the credit domain. In both cases, we can show how REASONX can be flexibly used and tailored to the needs of the user.

In his blog post on artificial intelligence (AI), GovTech Graduate Jonathan Manning draws on the New Zealand Law Foundation: Government use of artificial intelligence in New Zealand (the NZFL report) to discuss the role and effectiveness of explanation tools. As algorithms and AI become ubiquitous we all become'data subjects' to organisations such as governments and businesses. In response, regulations such as the EU's General Data Protection Regulation are beginning to emerge. The New Zealand government is currently exploring how governments, business and society can work together to meet the challenge of regulating AI. A part of this challenge is ensuring when things like algorithmic harm arise, we can explain what happened and why so that mistakes can be fixed and not repeated or obscured.