Abstract
Algorithms play a key role nowadays in many technological systems that control or affect various aspects of our lives. As a result, providing explanations to address the needs of users and organisations is increasingly expected by the laws and regulations, codes of conduct, and the public. However, as laws and regulations do not prescribe how to meet such expectations, organisations are often left to devise their own approaches to explainability, inevitably increasing the cost of compliance and good governance. Hence, we put forth "Explainability by Design", a holistic methodology characterised by proactive measures to include explanation capability in the design of decision-making systems. This paper describes the technical steps of the Explainability-by-Design methodology in a software engineering workflow to implement explanation capability from requirements elicited by domain experts for a specific application context. Outputs of the Explainability-by-Design methodology are a set of configurations, allowing a reusable service, called the Explanation Assistant, to exploit logs provided by applications and create provenance traces that can be queried to extract relevant data points, which in turn can be used in explanation plans to construct explanations personalised to their consumers. Following those steps, organisations will be able to design their decision-making systems to produce explanations that meet the specified requirements, be it from laws, regulations, or business needs. We apply the methodology to two applications, resulting in a deployment of the Explanation Assistant demonstrating explanations capabilities. Finally, the associated development costs are measured, showing that the approach to construct explanations is tractable in terms of development time, which can be as low as two hours per explanation sentence.
Abstract (translated)
URL
https://arxiv.org/abs/2206.06251
