Rutgers the State University of New Jersey

Generative AI and ESG

Irina Jie Bao

Instructor: Irina Jie Bao

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

9 hours to complete
3 weeks at 3 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand the foundations of ESG and Generative AI, and how they intersect to drive sustainable business practices.

  • Explore advanced AI applications in ESG, ethical considerations, and future trends in AI-driven sustainability efforts.

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Recently updated!

November 2024

Assessments

13 assignments

Taught in English

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There are 6 modules in this course

Welcome to the "GenAI and ESG" course! This comprehensive program is designed to equip you with the knowledge and skills necessary to harness the power of generative AI (GenAI) to address the complexities of environmental, social, and governance (ESG) practices. Throughout the course, you will explore the evolving landscape of ESG reporting, data analysis, and regulatory compliance while discovering how GenAI can enhance transparency, efficiency, and scalability. A learner will be able to define ESG and explain its importance in the context of sustainable business practices. They will be able to identify and describe the three pillars of ESG: Environmental, Social, and Governance. Learners will also understand the key ESG standards, frameworks, and reporting practices, such as GRI, SASB, and TCFD. Additionally, they will be able to recognize and analyze examples of greenwashing and its impact on ESG credibility. Finally, they will evaluate the role of regulatory bodies, such as the SEC, in shaping ESG disclosure requirements.

What's included

5 videos2 readings2 assignments

A learner will be able to define generative AI (GenAI) and differentiate it from traditional AI approaches. They will understand the types of generative models and their capabilities, with a focus on large language models (LLMs). Learners will recognize the scale and impact of modern LLMs and appreciate their potential for transforming various industries. They will be able to identify major GenAI applications across sectors like healthcare, finance, education, and entertainment. Additionally, they will compare and evaluate key players and emerging players in the GenAI landscape.

What's included

4 videos1 reading2 assignments1 discussion prompt

A learner will be able to recognize the challenges in ESG data collection and analysis and understand the need for AI-driven solutions. They will understand the advantages of implementing GenAI in ESG practices, such as enhanced transparency, efficiency, and scalability. Learners will be able to identify key AI technologies enabling ESG transformation and their potential applications. They will explain the limitations of traditional ESG rating agencies and the benefits of using GenAI for automated ESG analysis. Additionally, learners will utilize GenAI for ESG risk assessment across climate, social, and governance dimensions, as well as for improving stakeholder engagement and communication. Finally, they will identify opportunities for leveraging GenAI to drive sustainable product design, resource optimization, and supply chain management.

What's included

5 videos1 reading2 assignments

By the end of this module, learners will master advanced GenAI implementation techniques for ESG applications. They'll be able to craft effective prompts to guide AI models towards producing accurate and relevant ESG outputs, apply retrieval augmented generation (RAG) to enhance models with current ESG information, and fine-tune pre-trained models on specific ESG datasets. Additionally, they'll gain the ability to critically compare and select the most appropriate AI implementation approach—whether RAG, fine-tuning, or prompt engineering—for diverse ESG use cases, ensuring optimal performance in sustainability-related tasks.

What's included

3 videos2 readings2 assignments

By the end of this module, learners will be proficient in applying GenAI techniques to extract and analyze critical ESG data from sustainability reports, including environmental metrics like Scope 1, 2, and 3 emissions, as well as diversity, equity, and inclusion (DEI) information. They will understand major ESG reporting standards such as GRI and use this knowledge to guide AI-driven data extraction processes. Additionally, learners will develop the skills to critically evaluate the accuracy of AI-extracted ESG data through manual verification and error analysis, enabling them to reflect on the benefits and challenges of automating ESG analysis with GenAI. This practical expertise will empower learners to leverage AI effectively in real-world ESG data processing and benchmarking scenarios.

What's included

2 videos2 readings2 assignments

By the end of this module, learners will be equipped to navigate the complex ethical landscape of AI in ESG practices. They'll be able to identify and analyze key ethical considerations such as data privacy, algorithmic bias, and transparency, while understanding the crucial role of explainable AI in building trust and accountability. Learners will gain insight into the current and evolving regulatory landscape surrounding AI governance and ESG standardization. Furthermore, they'll develop a forward-looking perspective on leveraging AI for sustainable impact, balancing the opportunities with potential challenges. This comprehensive understanding will enable learners to make informed, ethical decisions when implementing AI solutions in ESG contexts.

What's included

2 videos1 reading3 assignments

Instructor

Irina Jie Bao
1 Course96 learners

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