Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making
This work addresses the need for better explanations in algorithmic decision-making for stakeholders, but it is incremental as it adapts existing frameworks from other disciplines.
The paper tackles the problem of explaining algorithmic decision-making systems by applying the 'six facets of understanding' framework from the learning sciences, resulting in a qualitative task-based study that proposes interdisciplinary practices to improve explanations.
We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-based study incorporating the "six facets of understanding" framework. We close with questions guiding the discussion of how future studies can leverage an interdisciplinary approach.