Corporate Sustainability Disclosure and Machine Learning

Overview

This PhD project will take a comprehensive approach to exploring how corporate sustainability disclosure practices evolve in response to the integration of Machine Learning (ML) within organizations, with a focus on both transparency and governance. As ML technologies continue to revolutionize industries, there is a growing need for organizations to adapt their disclosure practices to address the complexities and risks associated with ML adoption. This research will examine how firms are incorporating ML into their sustainability reporting practices and assess the informativeness of corporate disclosures, especially concerning the risks and benefits related to ML in relation to sustainability reporting.

Machine Learning is reshaping various sectors, from financial services and healthcare to retail and manufacturing. Financial institutions use ML for critical tasks like risk management, fraud detection, and investment analytics. In healthcare, ML supports diagnostic improvements, predictive analytics, and enhanced patient care. Retailers rely on ML to optimize inventory management, personalize customer experiences, and streamline their supply chains. Manufacturing organizations deploy ML for predictive maintenance, production efficiency, and quality control. While the benefits of ML are significant, its adoption also introduces risks such as data privacy concerns, algorithmic bias, model transparency issues, and increased exposure to cybersecurity threats. These challenges necessitate robust corporate disclosure frameworks to maintain transparency with stakeholders.

Corporate disclosure (particularly, sustainability reporting), a cornerstone of transparency and accountability in response to the call for achieving sustainable development goals, must evolve as ML becomes more integrated into business operations. Firms need to provide stakeholders with clear, accurate, and timely information regarding the implications of ML adoption, particularly in areas such as data security, algorithmic fairness, and compliance with regulatory standards. As the use of ML becomes more widespread, companies will be expected to communicate not only the advantages but also the inherent risks of ML in their operations. Effective corporate governance is crucial to ensure that these disclosures are meaningful, comprehensive, and reflective of the dynamic ML landscape.

This project aims to achieve the following objectives:

1. Assess the informativeness of corporate disclosures
2. Examine how firms are integrating ML into their sustainability reporting practices
3. Explore stakeholder perceptions of sustainability disclosures
4. Analyse the role of corporate governance in overseeing ML implementation

Methodology

The methodology for this project will be flexible and adaptable to the specific focus areas of the research. Several methods will be considered, with the choice depending on the data available and the objectives of the analysis. Potential approaches include employing Machine Learning models (unsupervised, semi-supervised, or supervised) to analyse corporate sustainability disclosures across industries. This will help identify trends, patterns, and anomalies in how firms report on ML activities and whether disclosure practices are keeping pace with ML adoption.

Additionally, Natural Language Processing (NLP), Qualitative content analysis or thematic analysis might be used.

By selecting and combining the most appropriate methodologies based on the research focus and available data, this project aims to offer a comprehensive understanding of how sustainability disclosure practices are adapting to the challenges and opportunities presented by ML. The analysis will provide insights into the effectiveness of corporate governance frameworks in promoting responsible ML adoption and ensuring transparency for stakeholders.

Nottingham Business School is triple crown accredited with EQUIS, AACSB and AMBA – the highest international benchmarks for business education. It has also been ranked by the Financial Times for its Executive Education programmes in 2023 and 2024. NBS is one of only 47 global business schools recognised as a PRME Champion, and held up as an exemplar by the United Nations of Principles of Responsible Management Education (PRME).  

Its purpose is to provide research and education that combines academic excellence with positive impact on people, business and society.  As a world leader in experiential learning and personalisation, joining NBS as a researcher is an opportunity to achieve your potential.

Application deadlines

• Applications for the October 2025 intake close on 1st July 2025
• Applications for the January 2026 intake close on 1st October 2025