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Building Intelligent User Interfaces for Human-AI Alignment

Danqing Shi

TL;DR

Problem: human-AI alignment hinges on high-quality human feedback, which is shaped by UI design. Approach: introduce a reference model that structures intelligent user interfaces around data sampling, visual transformation, interactive display, and a human feedback control loop, drawing on information visualization principles. Contributions: detailed taxonomy, two case studies (IGC for RL and DxHF for LLM) and a comparative view of six UIs illustrating design strategies for evaluation-alignment. Significance: offers a practical framework for HCI researchers and open-source collaboration to advance alignment through better feedback interfaces and scalable oversight.

Abstract

Aligning AI systems with human values fundamentally relies on effective human feedback. While significant research has addressed training algorithms, the role of user interface is often overlooked and only treated as an implementation detail rather than a critical factor of alignment. This paper addresses this gap by introducing a reference model that offers a systematic framework for analyzing where and how user interface contributions can improve human-AI alignment. The structured taxonomy of the reference model is demonstrated through two case studies and a preliminary investigation featuring six user interfaces. This work highlights opportunities to advance alignment through human-computer interaction.

Building Intelligent User Interfaces for Human-AI Alignment

TL;DR

Problem: human-AI alignment hinges on high-quality human feedback, which is shaped by UI design. Approach: introduce a reference model that structures intelligent user interfaces around data sampling, visual transformation, interactive display, and a human feedback control loop, drawing on information visualization principles. Contributions: detailed taxonomy, two case studies (IGC for RL and DxHF for LLM) and a comparative view of six UIs illustrating design strategies for evaluation-alignment. Significance: offers a practical framework for HCI researchers and open-source collaboration to advance alignment through better feedback interfaces and scalable oversight.

Abstract

Aligning AI systems with human values fundamentally relies on effective human feedback. While significant research has addressed training algorithms, the role of user interface is often overlooked and only treated as an implementation detail rather than a critical factor of alignment. This paper addresses this gap by introducing a reference model that offers a systematic framework for analyzing where and how user interface contributions can improve human-AI alignment. The structured taxonomy of the reference model is demonstrated through two case studies and a preliminary investigation featuring six user interfaces. This work highlights opportunities to advance alignment through human-computer interaction.
Paper Structure (13 sections, 4 figures, 1 table)

This paper contains 13 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Reference Model
  3. User Interface Design
  4. Data sampling.
  5. Visual transformation.
  6. Interactive Display.
  7. Human Feedback Control
  8. Analysis & Interaction.
  9. Feedback.
  10. Case Studies
  11. Case Study 1: RL agent alignment
  12. Case Study 2: Language model alignment
  13. Discussion

Figures (4)

  • Figure 1: Human feedback interface of InstructGPT ouyang2022training.
  • Figure 2: The reference model applied to intelligent user interfaces for human-AI alignment. The intelligent user interface system samples AI-generated data, transforms it into a visual representation, and provides interactive displays (blue boxes). Humans observe the visual representation, interact with the display, and provide feedback (yellow boxes).
  • Figure 3: IGC interface kompatscher2025interactive displays multiple trajectories from the model organized in a hierarchical radial chart in the left. Suggestions for comparisons are shown as gray lines, while previously preferences are color encoded. Human annotators can select groups for comparison in the right.
  • Figure 4: DxHF interface shi2025dxhf decomposes the model's text outputs into individual claims. Similar claims across two responses are connected with a keyword label. By using hover highlights, human annotators can more easily identify differences and compare the claims.