Transparent Peer Review By Scholar9
Revolutionizing Robotic Process Automation (RPA) with Autonomous AI Agents: How GPT and AI Models are Shaping the Next Generation of Process Automation
Abstract
Robotic Process Automation (RPA) powered by Generative Artificial Intelligence (GAI) has emerged as a critical component of the digital ecosystem. The emergence of Chat GPT, a publicly available tool created by Open GAI, and its underlying technology, Generative Pretrained Transformer (GPT), are expected to significantly boost the growth of generative AI in the upcoming years. GAI-enabled RPA mimics human interactions with applications and enables direct access to systems via APIs. When compared to human execution, RPA offers greater benefits including scalability, perpetual lifetime, and 24x7 execution. Process automation is not a new technology, but due to significant advancements in GAI, which RPA utilises, it has emerged as its own solution category. An overall strategy for resolving the matter is to enhance transparency. The study suggests using technology to improve data accessibility and readability while using artificial intelligence. "Transparency technology XBRL (eXtensible Business Reporting Language)" is integrated with this goal in mind. Sunstein (2013) states that XBRL is a component of the regulatory choice architecture used by governments. XBRL has a taxonomy associated with it. The study creates a taxonomy for RPA in order to make the use of artificial intelligence more transparent to the public, while also incorporating ethical considerations. Selected as a business case is the rapidly expanding RPA sector. The paper focusses on improving GAI in a way that is consistent with human values. How may incentives be offered to prevent GAI systems from becoming potential items that raise ethical questions. The paper's key finding is that, while transparency technologies simultaneously offer ways to reduce such dangers, they also highlight moral concerns related to GAI-enabled RPA. This is a human-written paper, devoid of any AI-generated text.
Shyamakrishna Siddharth Chamarthy Reviewer
11 Oct 2024 03:42 PM
Approved
Relevance and Originality
The research article addresses a highly relevant topic in the growing field of Robotic Process Automation (RPA) powered by Generative Artificial Intelligence (GAI), particularly with the rise of technologies like Chat GPT. The study is original in its approach to enhancing transparency and ethical considerations in GAI-enabled RPA through the use of technologies like XBRL. The idea of developing a taxonomy for RPA that aligns with human values is a unique contribution, providing a structured way to incorporate transparency and ethics into AI-powered automation. This focus on both technological advancement and ethical responsibility makes the research significant for both academic and practical applications.
Methodology
The study suggests an innovative methodology by integrating XBRL with RPA to enhance transparency and accessibility of AI technologies. The creation of a taxonomy for RPA is a well-thought-out strategy, aiming to improve public understanding and accountability. However, the article could provide more details on how the taxonomy was constructed, the criteria used for its classification, and the practical steps for its implementation in real-world systems. Additionally, offering examples or case studies that demonstrate the practical application of this methodology would give readers a clearer understanding of its effectiveness and scalability.
Validity & Reliability
The study establishes its validity by referencing established transparency technologies like XBRL and incorporating ethical considerations in GAI systems. While the approach to improving transparency and ethical standards is sound, more empirical evidence would be beneficial to substantiate the study’s claims. Including data from pilot tests or industry case studies where XBRL is applied to GAI-enabled RPA could enhance the reliability of the findings. More information on how the proposed taxonomy addresses specific ethical concerns, such as data privacy and algorithmic bias, would also strengthen the reliability of the study.
Clarity and Structure
The article is well-structured, moving logically from the rise of RPA and GAI to the challenges and solutions related to transparency and ethics. The integration of transparency technologies like XBRL is explained clearly, though certain sections discussing technical components, such as the taxonomy and regulatory frameworks, could benefit from further elaboration. Simplifying the more technical parts and providing visual aids, such as diagrams or flowcharts, would make the content more accessible to a wider audience, particularly those unfamiliar with RPA or XBRL technology.
Result Analysis
The analysis highlights the importance of transparency and ethical considerations in the deployment of GAI-enabled RPA. The study’s key finding—that transparency technologies like XBRL can mitigate ethical risks while raising moral concerns—adds depth to the discussion. However, the paper could provide more detailed analysis on specific ethical issues, such as potential misuse of GAI or the societal impact of automating certain jobs. Offering concrete examples of how the proposed taxonomy could address these ethical challenges would strengthen the result analysis, making it more practical and actionable for policymakers and businesses adopting RPA technologies.
IJ Publication Publisher
done sir
Shyamakrishna Siddharth Chamarthy Reviewer