PhD Opportunity: Transparent Textual-Audio Analysis via eXplainable AI (XAI) and Natural Language Processing (NLP) for improved ESG and Impact Measurement

Project summary: The successful candidate will investigate how the use of advanced textual-audio analytics and alternative data may enhance the measurement of corporate environmental, social and governance (ESG) performance. The proposed research will develop and evaluate novel NLP-based eXplainable AI (NLP-XAI) approaches for textual-based measurement of ESG, allowing for explanations to be assigned to ESG scores and the quality of explanations to be assessed.

Deadline: 31 March 2023

Duration: 36 months full-time

Funding details: Fully-funded scholarship for 3 years covers all university tuition fees (at UK level) and an annual tax-free stipend. International students are also eligible to apply, but they will need to find other funding sources to cover the difference between the home and international tuition fees.  Exceptional international candidates may be provided funding for this difference.

Number of places: 1

Eligibility: Applicants should possess a strong academic track record. Specifically, we welcome applicants who have obtained, or are expected to obtain, a postgraduate degree in a data science-related subject area. We also welcome applications from candidates with a finance-related postgraduate degree who can demonstrate strong quantitative and programming skills. In exceptional cases, we will consider those without a postgraduate degree, who can demonstrate outstanding performance at undergraduate level. The successful application will be able to demonstrate a solid understanding of artificial intelligence, machine learning, and some understanding of eXplainable AI (XAI). Proficiency in languages such as Python and/or R would be desirable.

Project details: Environmental, Social and Governance (ESG) credentials are of growing strategic importance for businesses, with ESG investing being increasingly prioritised by investors. Accurate ESG scoring of corporations is of paramount importance. ESG scoring systems in practice, however, have major weaknesses: (1) opaqueness in the proprietary methods used by private ESG scoring agencies; (2) observed inconsistencies in the ESG scores reported by alternative private ESG scoring systems; and (3) the extent of missing ESG scores across the population of corporations. Our research project directly addresses these weaknesses.

The successful candidate will seek to develop cutting-edge methods that offer transparency, consistency, and wide applicability in ESG measurement. Specifically, the proposed research will leverage the power of Artificial Intelligence (AI), while addressing the ‘black-box' constraint of the underlying algorithms. State-of-the-art eXplainable Artificial Intelligence (XAI) techniques will be used, providing explainable outcomes. Such ‘white-box' XAI techniques will lead to transparent measurement of firms’ ESG performance, reconciliation of inconsistencies in existing ESG scoring systems, and wider application to the base of corporations.

The main contribution of this work will be the novel development and application of Natural Language Processing (NLP) based XAI approaches for textual-based measurement of ESG. Further contribution will be made by extending this work through the augmentation of textual analysis with audio characteristics of corporate earnings calls (such as manager pitch, tone and hesitancy), thus establishing an NLP classifier based on multiple modes of communication that may further enhance the reliability of our explainable ESG scoring approaches.

Primary supervisor: Dr James Bowden

Contact: james.bowden@strath.ac.uk

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