Human-Centered-Ai

When Hallucination Is More Than a Wrong Fact: Measuring Reliability Through the User

TL;DR for operators A model change can improve an automated hallucination benchmark while leaving users dissatisfied for a different reason: sources are hard to verify, reasoning appears unsupported, false claims are stated with confidence, or corrections are ignored. The System Hallucination Scale (SHS) gives teams a structured way to measure those experiences across five dimensions rather than reducing reliability to a binary factual-error judgment.1 ...

Metrics vs Minds: Why Your XAI Scorecard Lies to Your Users

Scorecards look objective until a user reads the explanation Scorecards are comforting. They turn a messy judgment into a neat row of numbers: sparsity, proximity, plausibility, trust score, completeness. The model team can rank explanation methods. The governance team can file the validation report. The product team can say the system is explainable. Everyone gets to leave the meeting before dinner. ...

First Contact with the Graph: The Exploration Cold Start in Knowledge Systems

Search boxes look innocent. They sit there politely, waiting for the user to type something useful. In ordinary software, this feels reasonable. In a document repository, a customer support portal, or a product catalogue, the user usually arrives with at least a rough idea: a name, a keyword, a complaint, a document type, a half-remembered phrase. ...

When Your House Talks Back: Teaching Buildings to Think About Energy

A high electricity bill arrives. You ask your smart-home assistant what happened. It checks the meter data, explains that the electric-vehicle charger ran during peak-rate hours, and recommends a cheaper schedule. Useful. Then you ask how much the new schedule will save next month. The assistant retrieves the tariff, forecasts consumption, applies export credits from the solar panels, and confidently reports a number. ...

When More Explanation Hurts: The Early‑Stopping Paradox of Agentic XAI

A farmer does not need ninety-three charts before deciding what to do next. That sounds obvious. Unfortunately, “obvious” is where many agentic AI workflows go to die. Give an LLM a model explanation, ask it to improve the explanation, let it generate more analysis, feed the results back, and repeat. The process feels responsible. More checks. More plots. More reasoning. More “depth.” Somewhere in the background, a product manager begins to hear the soft music of enterprise automation. ...