AI for social good — Part 2
Artificial intelligence (AI) is the field of study focused on creating intelligent machines. Within AI, machine learning (ML) is a subfield that focuses on the ability of machines to learn and adapt based on data input, without requiring explicit programming. In recent years, there has been a growing interest in applying AI to various industries and sectors, as it has the ability to process and analyze large amounts of data quickly and accurately. Following up on my article on AI for Social good- Part 1, I decided to write a part 2. This article will discuss mainly ESG and mental illness to explore the role of Natural Language Processing in Enhancing ESG Practices and Assessing Mental Illness.
One area where AI can be particularly impactful is in the field of environmental, social, and governance (ESG) [1] initiatives. These initiatives focus on using business strategies and practices to promote positive social and environmental outcomes. ESG initiatives can benefit from the use of natural language processing (NLP) techniques in several ways. NLP can be used to analyze large amounts of data, such as social media posts, news articles, and company reports, to identify patterns and trends related to ESG topics. This can help companies and organizations to better understand the public’s views and concerns about environmental and social issues, and to identify areas where they can improve their performance in these areas [2]. In this regard, democratizing the ESG data could benefit the public’s perception of a company and/or investors. This would mean a lot of big companies could manipulate their data to greenwash public opinion. Greenwashing is the practice of making false or misleading claims about the environmental benefits of a product, service, or company in order to appeal to consumers who are concerned about sustainability.
Another area where AI can be applied for social good is in the detection and prevention of mental illness, for example, suicidal thoughts and behaviors, specifically with the use of natural language processing (NLP). AI can be used to analyze social media posts and other forms of online communication to identify language and other indicators that may suggest someone is at risk of self-harm. AI can also be used to provide personalized mental health support and interventions, such as through the use of virtual assistants or chatbots that can provide counseling and other forms of support to individuals in need. For example, Edtech platforms can offer students access to online counseling services, self-care tools, and other resources that can help to prevent and manage mental health issues. We all need to work together to remove mental illness as a stigma in our society. AI can only do and think as much as we humans do. In other words,
AI is limited by how humans feel and act
For more details on NLP application for social good, please visit my previous story — https://medium.com/lingvo-masino/nlp-for-social-good-part-i-85b2d757bf15
Conclusion
Overall, the application of AI for social good has the potential to make a significant positive impact on a range of issues, from environmental sustainability to mental health. It is important, however, to ensure that these AI systems are developed and used ethically and responsibly, taking into account the potential for unintended consequences and the importance of protecting the privacy and security of individuals.
It would appear that the area of AI that is most analogous to the behavior of economic entities is that of morally good decision-making: if an economic or machine-learning system is to achieve its goals (maximizing some reward), we want it to do so while acting in a way that is consistent with human social norms and moral principles [3].
References
[1] Li, T.T., Wang, K., Sueyoshi, T. and Wang, D.D., 2021. ESG: Research progress and future prospects. Sustainability, 13(21), p.11663.
[3] LaCroix, T. and Bengio, Y., 2019. Learning from learning machines: optimisation, rules, and social norms. arXiv preprint arXiv:2001.00006.
Additional Read:
< — There are parts in this blog generated by ChatGPT and the image is generated by Dall.E2— >
