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A hypothesis-driven responsible AI framework for interpretable ESG forecasting with RuleFit.

Tufail Muhammad¹, Rubab Hafeez¹, Waqas Bin Khidmat²

¹Department of Computer Science, Air University Aerospace and Aviation Campus, Kamra, Pakistan.

Scientific Reports

|April 28, 2026

Summary

This summary is machine-generated.

This study introduces a framework using Responsible AI (RAI) and rule extraction to predict Environmental, Social, and Governance (ESG) scores. It ensures ethical and accurate ESG forecasting for sustainable finance decisions.

Keywords:

Corporate governance Ensemble learning Responsible AI RuleFit

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Area of Science:

* Business Analytics
* Artificial Intelligence
* Corporate Social Responsibility

Background:

* Responsible AI (RAI) is integral to corporate social responsibility and business governance.
* Predicting Environmental, Social, and Governance (ESG) scores is crucial for sustainable finance.
* Existing methods may lack interpretability and ethical alignment in ESG forecasting.

Purpose of the Study:

* To develop a framework integrating RAI principles with hypothesis-driven rule extraction for ESG score prediction.
* To enhance the interpretability and ethical alignment of ESG outcome forecasts.
* To facilitate reliable decision-making in sustainable finance and corporate governance.

Main Methods:

* Employed the ensemble-based RuleFit algorithm for hypothesis-driven rule extraction.
* Associated firm-level attributes (size, leverage, digitization, ownership) with decision rules.
* Validated rules using nonparametric tests and assessed subgroup fairness for equity.

Main Results:

* Generated interpretable decision rules linking firm attributes to ESG outcomes.
* Achieved statistically sound and ethically aligned ESG score predictions.
* Demonstrated the robustness of the methodology under non-normal distributions.

Conclusions:

* Rule-based learning offers a powerful approach for interpretable and ethical ESG forecasting.
* The proposed framework supports data-driven, responsible decision-making in corporate governance.
* This research bridges AI, ESG, and sustainable finance through a novel methodology.