Team Contribution Attribution Ledger (TCAL): A Framework for Distributed Human Contribution Aggregation and Attribution in AI-Augmented Systems

Abstract

As collaborative work increasingly incorporates artificial intelligence, accurately identifying and attributing human contributions across teams has become a critical challenge. Traditional attribution methods rely on output-based metrics or narrative reconstruction, both of which fail to capture the underlying intellectual contributions that drive outcomes.

This work introduces the Team Contribution Attribution Ledger (TCAL), a framework for aggregating distributed Human Conception Ledger (HCL) records to generate structured, evidence-based attribution across collaborators. TCAL synthesizes individual human-origin contribution events into team-level attribution models, enabling quantifiable yet human-reviewed determination of contribution.

The framework provides a scalable system for attribution in research, engineering, and organizational environments, supporting applications in intellectual property, authorship, compensation, and governance.

1. Introduction

Modern collaborative work is increasingly mediated by AI systems, creating ambiguity in contribution attribution across teams. Existing approaches rely on:

• output-based metrics (e.g., commits, document length, task completion)

• narrative reconstruction of contribution

• informal or subjective assessment

These methods fail to capture:

• upstream reasoning and conceptual contributions

• human decision-making and rejection of incorrect outputs

• distributed and asynchronous contributions across team members

As AI systems generate increasing portions of output, these limitations become more pronounced.

2. Relationship to Human Conception Ledger (HCL)

The Human Conception Ledger (HCL) provides a framework for capturing human-origin contribution events at the individual level, including:

• Human Insight Nodes (HIN)

• Human Decision Overrides (HDO)

• Rejection Events (REJ)

TCAL builds directly upon HCL by: aggregating individual HCL records into a unified, team-level attribution system.

While HCL establishes what constitutes a human contribution, TCAL addresses:

how contributions are combined, evaluated, and attributed across multiple individuals. (https://doi.org/10.5281/zenodo.19198703)

3. TCAL Framework

The Team Contribution Attribution Ledger (TCAL) is designed to:

• aggregate distributed HCL records

• synthesize contribution events across individuals

• generate structured attribution outputs

Each contributor maintains an individual HCL, which records their human-origin contribution events. These records are then ingested by the TCAL aggregation system.

As shown in Figure 1, TCAL functions as a central aggregation layer that integrates multiple HCL streams into a unified model of contribution.

Figure 1.  Team Contribution Attribution Ledger (TCAL) FrameworkFigure 1 illustrates the TCAL aggregation framework. Individual contributors maintain separate Human Conception Ledger (HCL) records, which are aggregated into a centralized TCAL engine.

The system produces structured outputs including contribution percentages, attribution reports, and timeline views. Attribution remains human-reviewed and non-automated, ensuring interpretability and fairness in collaborative environments.

4. Aggregation Architecture

TCAL operates through:

• ingestion of individual HCL records

• normalization of contribution event types

• temporal alignment of contributions

• aggregation into unified contribution models

The system produces structured outputs including:

• Contribution Percentages 

• Attribution Reports 

• Timeline Views 

Importantly, TCAL is designed as a human-in-the-loop system, where final attribution decisions are reviewed and validated by humans rather than assigned automatically.

5. Non-Automated Attribution and Governance

A key design principle of TCAL is: attribution should not be fully automated.

While the system provides structured analysis, final decisions regarding:

• inventorship

• authorship

• contribution weighting

remain subject to human review.

This ensures:

• legal compliance

• fairness in attribution

• adaptability across contexts

6. Impact on Collaborative Work and Organizations

TCAL enables a shift from activity-based metrics to evidence-based attribution systems.

In traditional team environments, contribution is often inferred from:

• number of commits

• task completion counts

• visible output

However, these metrics fail to capture:

• conceptual breakthroughs

• architectural decisions

• debugging insight

• rejection of incorrect approaches

By aggregating HCL records, TCAL enables organizations to:

• quantify contribution based on reasoning events

• distinguish high-impact contributions from high-volume activity

• evaluate contributions across asynchronous and distributed teams

This has implications for:

• compensation and equity allocation

• authorship and credit assignment

• performance evaluation

• hiring and promotion decisions

TCAL supports a transition toward provenance-aware organizations, where contribution is measured through structured evidence rather than proxy metrics.

7. Applications

TCAL supports:

• patent inventorship determination

• academic authorship attribution

• corporate R&D contribution tracking

• grant and funding attribution

• distributed team governance

8. Conclusion

The Team Contribution Attribution Ledger (TCAL) provides a scalable framework for aggregating human-origin contributions across collaborative environments.

By integrating individual HCL records into structured attribution systems, TCAL enables:

• evidence-based contribution analysis

• improved attribution fidelity

• human-reviewed governance

Together, HCL and TCAL establish a unified framework for individual and team-level attribution in AI-augmented systems. This is also available at: https://doi.org/10.5281/zenodo.19198821