How to Help People Understand Pay and Compensation at a Glance

Why Read This from an HCI Perspective

This article frames AI not merely as an answer tool, but as a new kind of ‘information interface’ that people use to find wage and compensation information. From an HCI/UX perspective, it’s important to look beyond the content of the answer and consider what users ask, what they choose to trust, and what decisions they make based on that trust. In particular, it’s meaningful to examine not only AI’s role in reducing information gaps, but also where incorrect trust or biased advice can emerge.

CIT's Commentary

This case shows that LLMs are shifting from knowledge search toward interactions that directly shape people’s economic decision-making. The interesting part is that the bigger variable isn’t ‘whether the answer is correct,’ but ‘how much it makes people willing to trust and act.’ In sensitive areas like compensation information, answer quality alone isn’t enough—clearly indicating sources, expressing uncertainty, and providing paths for users to verify through follow-up questions are crucial. Especially in the context of domestic services, when such features are introduced into everyday, lifestyle-oriented platforms like Naver or Kakao, it’s necessary to provide comparison criteria and explanations tailored to the Korean labor market rather than relying on simple summaries. Ultimately, the key is not to have AI speak for users, but to design structures that help users make their own judgments.

Questions to Consider While Reading

• Q.In sensitive domains like wages and compensation, to what extent do users trust an AI’s answers, and what kinds of wording increase or decrease that trust?
• Q.How does compensation information provided by an LLM affect real decision-making, and how can it be evaluated rigorously as a UX measurement tool?
• Q.In the context of job hunting and salary comparisons in Korea, how should global HCI research frameworks for reducing information gaps be adapted?