explain17Ms

Explain17Ms is a structured framework designed to guide the development and evaluation of explanations in machine learning systems and artificial intelligence applications. The model consists of seventeen interrelated elements that teachers, developers, and users consider when constructing clear, actionable, and trustworthy explanations. Each element addresses a distinct dimension of what constitutes a useful explanation, ranging from the content of the explanation itself to its contextual relevance for different stakeholders.

The seventeen elements include Source, Scope, Format, Complexity, Relevance, Accuracy, Rationale, Narrative, Visualization, Personalization, Transparency, Comparability, Context, Feedback, Interactivity, Cultural Sensitivity, and Ethical Compliance. The Source element specifies who or what produced the explanation; Scope defines the breadth of coverage; Format and Complexity determine linguistic and representational styles; Relevance and Accuracy focus on alignment with user needs and factual integrity; Rationale and Narrative provide deeper logical grounding; Visualization offers graphic representations; Personalization adapts content to individual users; Transparency exposes the underlying processes; Comparability enables users to contrast alternative explanations; Context captures situational factors; Feedback and Interactivity allow dynamic refinement; Cultural Sensitivity ensures inclusivity; and Ethical Compliance guarantees adherence to legal and moral norms.

Explain17Ms emerged from interdisciplinary research in human‑computer interaction, data science, and philosophy of explanation. The framework was first articulated in a 2022 conference on explainable AI and has since been adopted in a number of industry white papers and academic studies. It is frequently used in the design of explainable recommendation engines, credit‑risk classifiers, and autonomous driving systems. By providing a systematic checklist, the model helps teams avoid common pitfalls such as overly technical jargon, omission of assumptions, or failure to address ethical concerns. The framework supports evaluative studies that compare explanations across domains and demographic groups, thereby informing standards and guidelines for responsible AI deployment.