A Methodology to Support Explanations in Decision-Making Systems

ODSC - Open Data Science
3 min readJun 1, 2022


Editor’s note: Luc Moreau is a speaker for ODSC Europe 2022. Be sure to check out his talk, “Explainability by Design: a Methodology to Support Explanations in Decision-making Systems,” there!

There are increasing calls for the explainability of data-intensive applications. Such a demand for explainability stems from various reasons, such as regulations, governance frameworks, or business drivers. Explanations are becoming a mechanism to demonstrate good governance of data-processing pipelines. Good explanation capability should be able to answer a vast range of queries including the following “Was consent obtained from the user to process their data?”, “What process was followed to check a model before its deployment?”, “What are the factors that influenced a decision”, “What action could be taken to correct some data”, and “Is the actual execution of the Data Science application complying with a given data governance framework?”

So far, there has been a lack of a principled approach to generating explanations. From an implementation viewpoint, there is no good separation of concerns between decision-making systems, their querying, and the composition of explanations. From a methodological viewpoint, there is not a well-specified workflow for engineers to follow, which leaves every organization to “reinvent the wheel” for their explanation capability.

Against this background, we introduce “Explainability by Design” a methodological approach that tackles this issue in the design and architecture of IT systems and business practices. Explainability-by-Design (EbD) is a methodology that is characterized by proactive measures to include explanations in the design rather than relying on reactive measures to bolt on explanation capability after the fact, as an add-on.

A key aspect in the journey of developing a methodology is to understand what an explanation is, and all the properties it is intended to satisfy. To this end, I will outline a taxonomy of explanations and their requirements.

Another aspect of this journey is to conceptualize an explainability component as an integral part of a business system, enriching its functionality with capabilities that can address regulatory requirements but also functional and business needs. We have produced a reference implementation of this component, which is called the Explanation Assistant. A configured and ready-to-be-deployed Explanation Assistant is a key output of the Explainability-by-Design methodology.

The final aspect is the breaking down of the methodology into steps ingesting explanation requirements in order to construct an explanation capability that a system’s triggers can activate, in order to generate the required explanations.

The above steps of the methodology are to be carried out by various roles in an organization. The socio-technical Engineer focuses on the requirement analysis, leading to a set of explanation requirements that the Data Engineer processes in order to produce a configuration of the explanation assistant that can be validated by the Application Stakeholder.

I look forward to seeing you at ODSC Europe 2022, my presentation will further motivate the methodology and will dive into the various steps of a well-specified workflow, which should be carried out by the data engineer.

Original post here.

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