Understanding partial explanation systems

Partial explanations clarify key elements of how AI works while leaving out parts that are unknown, highly complex, or not useful for the immediate task. They focus on factors that matter for user decisions.

Consider a news app showing "Choose topics to see stories curated just for you." This explains that personalization happens through topic selection without detailing recommendation algorithms. Users understand how to influence their feed without needing technical knowledge.

Similarly, a weather chatbot might say "I need your location to provide accurate weather." This reveals a data requirement without explaining how it processes geographic and meteorological data. These explanations reveal just enough to be helpful. Too much detail overwhelms. Too little leaves users guessing. The key is identifying what helps users make decisions.^[1]