Generative AI and ESG

所在平台: Coursera

课程主页: https://www.coursera.org/learn/genai-and-esg

课程评论：没有评论

第一个写评论        关注课程

课程简介

《生成性人工智能与ESG》课程总结 这门中级课程旨在为学习者提供全面的环境、社会和治理（ESG）原则的理解，并掌握如何应用生成性人工智能（GenAI）技术以增强ESG实践的能力。通过将可持续性与前沿人工智能技术结合，学习者将获得在组织和更广泛社会中推动有意义影响的技能。 课程从ESG与GenAI的基础知识开始，深入探讨它们的强大交集，学习大纲如下： 1. **ESG基础**：学习者将能够定义ESG，并解释其在可持续商业实践中的重要性。了解三个ESG支柱：环境、社会和治理，并掌握主要的ESG标准和报告实践，如GRI、SASB和TCFD。同时，他们也将识别绿色洗涤及其对ESG可信度的影响，评估监管机构（如SEC）在塑造ESG信息披露要求中的作用。 2. **GenAI基础**：学习者将能够定义生成性人工智能（GenAI）并与传统人工智能区别开来。理解各种生成模型及其能力，特别是大型语言模型（LLMs）的影响和潜力，以及其在医疗、金融、教育和娱乐等行业的应用。 3. **在ESG中使用GenAI**：学习者将识别ESG数据收集和分析中的挑战，理解AI驱动解决方案的必要性，认识到实施GenAI在ESG实践中的优势，如提升透明度、效率和可扩展性。同时，他们将利用GenAI进行ESG风险评估，并提升利益相关者的沟通与参与。 4. **ESG中的高级实施技术**：学习者将掌握针对ESG应用的GenAI高级实施技术，能够撰写有效的提示，利用检索增强生成（RAG）改善模型，并对特定的ESG数据集进行微调，确保在可持续性相关任务中获得最佳性能。 5. **案例练习：从ESG报告中提取数据**：学习者能够运用GenAI技术提取和分析可持续性报告中的关键ESG数据，包括碳排放和多样性、公平和包容性信息。掌握主要的ESG报告标准，评估AI提取的ESG数据的准确性。 6. **AI在ESG中的未来趋势与伦理问题**：学习者将能够识别和分析AI在ESG实践中的关键伦理考量，如数据隐私、算法偏见和透明度。理解可解释的AI在建立信任和问责中的重要性，并对AI在可持续发展中的应用持前瞻性观点，平衡机遇与潜在挑战。 通过这门课程，学习者将掌握利用生成性AI推动ESG实践所需的知识与技能，为实现可持续发展目标做好准备。

课程大纲

Name:Foundations of ESG

Description: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.

Name:Foundations of GenAI

Description: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.

Name:Using GenAI in ESG

Description: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.

Name:Advanced Implementation Techniques in ESG

Description: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.

Name:Case Exercises - Extracting Data from ESG Reports

Description: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.

Name:Future Trends and Ethical Concerns in AI for ESG

Description: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.

课程评论(0条)