A Scalable and Ethical Framework for AI-Augmented Robotic Process Automation
A SCALABLE AND ETHICAL FRAMEWORK FOR AI-AUGMENTED ROBOTIC PROCESS AUTOMATION
This academic paper proposes a scalable and ethical framework for AI-augmented robotic process automation (RPA), combining machine learning, process mining, and built-in ethical governance. The model is evaluated across multiple industries through surveys and simulations to demonstrate improvements in operational efficiency, error reduction, scalability, and workforce acceptance. It offers practical guidance for organizations seeking to implement responsible, transparent intelligent automation.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, automation engineers, and process managers looking to implement scalable and ethically governed intelligent automation systems.
What the document contains
The paper covers the design of a unified AI-augmented RPA framework featuring AI-driven decision support, real-time process mining, and built-in ethical governance with audit trails. It evaluates these components across finance, healthcare, IT services, and logistics through mixed-methods research including professional surveys and comparative performance metrics.
The study lacks long-term social and environmental impact analyses, public sector evaluations, and specific legal compliance mechanisms tailored to individual regional jurisdictions.
- Integrating AI, process mining, and ethical governance significantly improves traditional robotic process automation.
- The proposed framework achieves higher scores in operational efficiency, error reduction, and compliance alignment.
- Survey results across industries indicate strong overall workforce acceptance and reliable scalability.
- Healthcare professionals reported lower relative workforce acceptance compared to finance and logistics, highlighting the need for careful integration.
· I. Introduction · II. Related Work · III. Research Hypothesis & Problem Statement · IV. Proposed Framework · V. Methodology · VI. Result · VII. Comparative Performance Table · VIII. Diagrams · IX. Discussion · X. Limitations · XI. Future Work · XII. Conclusion · XIII. Conflict of Interest · XIV. Funding · REFERENCES
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
1/5Clear responsibility for AI outcomes
Finance respondents emphasized compliance and auditability as key strengths.
VI. Result
screening score · 2026-10-23
Fairness
1/5Non-discrimination and equitable treatment
Future research should: - Conduct long-term studies on workforce impact - Implement the framework in the public sector, education, and supply chains - Include fairness-aware and explainable AI models
XI. Future Work
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-23
Human Oversight
4/5Humans remain in control of consequential decisions
Built-in ethical governance with audit trails and human oversight.
IV. Proposed Framework
screening score · 2026-10-23
Privacy
2/5Protection of personal data and private life
Healthcare professionals raised concerns about data privacy and integration into clinical workflows.
VI. Result
screening score · 2026-10-23
Transparency
3/5Openness about how AI systems work and are used
The uniqueness lies in incorporating governance and transparency at the center of intelligent automation
IX. Discussion
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-23
Impact Assessment
1/5Required assessment of an AI system's risks/impacts before or during use
Future research should: - Conduct long-term studies on workforce impact - Implement the framework in the public sector, education, and supply chains - Include fairness-aware and explainable AI models
XI. Future Work
screening score · 2026-10-23