Enterprise AI Analysis
ChessMate: Intention Disclosure to Support Sense of Agency in Human-Computer Collaboration
This study explores how intention disclosure in AI-human collaboration affects users' Sense of Agency (SoA). Through a chess-playing robot, ChessMate, different disclosure levels (No, Low, High, Misleading) were tested. High disclosure significantly increased SoA, while misleading disclosure surprisingly didn't reduce it, suggesting cognitive engagement is key. Richer explanations prompt users to actively evaluate AI reasoning, fostering a stronger sense of control and improving decision quality, even in misleading scenarios by triggering vigilance. The findings advocate for explicit intention disclosure in high-risk systems and for encouraging error correction in low-risk educational/rehabilitation settings.
Executive Impact at a Glance
Key metrics demonstrating the tangible benefits of integrating advanced intention disclosure in your AI systems.
0%
Increased SoA with High Disclosure
0%
Improved Decision Quality
0%
Enhanced Cognitive Engagement
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Intent Disclosure & SoA Enhancement
The study found that explicit intention disclosure, especially 'High Disclosure' (multi-step reasoning and long-term goals), significantly increases a user's Sense of Agency (SoA) in human-computer collaboration. This is attributed to fostering deeper cognitive engagement.
Benefits
- Users feel more in control of AI's actions.
- Promotes active evaluation of AI's reasoning.
- Leads to more deliberate decision-making.
- Reduces over-reliance on automation.
Challenges
- Designing AI explanations that are both informative and concise.
- Preventing information overload.
- Maintaining user trust when disclosure is complex or potentially misleading.
Vigilance
Triggered by Misleading Disclosure
Enterprise Process Flow
AI Proposes Move
→
AI Discloses Intention
→
User Approves/Rejects
→
AI Executes / User Manually Moves
→
Game State Updates
| Condition | Description | Impact on SoA | Key Outcome |
|---|---|---|---|
| No Disclosure | Robot states move only, no explanation. | Lowest (Baseline) | Passive acceptance. |
| Low Disclosure | Brief local justification for current move. | Moderately higher | Limited reflection. |
| High Disclosure | Multi-step reasoning, long-term goals. | Significantly higher | Active evaluation, deliberate choices. |
| Misleading Disclosure | Verbal intent contradicts executed move. | Higher than No Disclosure (Surprisingly) | Triggered vigilance, independent thinking. |
Case Study: ChessMate System
ChessMate: A Collaborative Chess System
ChessMate is a robotic chess system where humans team with AI-assisted white pieces against a computer-controlled black opponent. Each turn, AI pieces recommend a move with varying levels of intention disclosure. Users can approve or reject the move, allowing for direct observation and manipulation of factors affecting Sense of Agency in a collaborative task.
- Uses Sony Toio Core Cubes as mobile bases for pieces.
- Custom 3D-printed shells for distinct piece types.
- Unity as central control, integrating LLM for move recommendations and explanations.
- WonderEcho module for voice interaction (speech recognition and playback).
- Supports experimental manipulation of intention disclosure.
Projected ROI: Enhanced AI Collaboration
Estimate the potential annual savings and reclaimed human hours by implementing AI systems with effective intention disclosure, leading to increased efficiency and reduced errors.
Annual Savings
$0
Hours Reclaimed
0 hrs
Your AI Implementation Roadmap
A clear path to integrating intention disclosure for enhanced human-AI collaboration in your enterprise.
Phase 1: Discovery & Strategy
Conduct a thorough analysis of existing workflows and identify key areas where intention disclosure can optimize human-AI collaboration. Define clear objectives and success metrics.
Phase 2: Prototype Development & Testing
Develop and integrate intention disclosure mechanisms into a prototype AI system. Conduct user testing to refine the clarity, timing, and content of disclosures, ensuring they enhance SoA and cognitive engagement.
Phase 3: Pilot Deployment & Iteration
Deploy the enhanced AI system in a pilot program with a subset of users. Gather feedback, monitor performance, and iterate on disclosure strategies based on real-world usage data and user experience. Scale incrementally.
Ready to Transform Your Enterprise AI?
Our experts are ready to guide you through integrating cutting-edge intention disclosure strategies for superior human-AI collaboration.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat