Explanationsto

Explanationsto is a term used to describe a family of explanatory outputs embedded in digital systems to help users understand outputs, decisions, or actions. An explanationto combines a concise rationale with contextual cues, focusing on factors most relevant to the user’s current task.

Origin and usage: The term is a neologism from design and AI explainability discussions in the 2020s. It is not universally standardized and may be used alongside targeted explanations. Some researchers treat explanationstos as an interface layer rather than a property of models.

Key characteristics: Explanationstos are targeted, timely, and actionable. They highlight causal factors, state uncertainties, and offer next steps. They use plain language and may employ visuals or formatting to manage cognitive load.

Implementation approaches: Designers combine template-driven language with model-agnostic methods, contextual previews, and progressive disclosure to balance usefulness with brevity. Evaluation focuses on usefulness, trust, and task performance.

Applications and examples: In AI decision support, explanationstos show which features influenced a result; in education, they justify why a step was chosen; in e-commerce, they explain rankings. Privacy and accuracy considerations are important.

See also: explainable AI, interpretability, user experience design.