Artificial Intelligence (AI) as a large driver of digitalization is discussed controversially. Since AI is often associated with incredibly high opportunities on one hand and vague fears of losing self-control on the other hand, it might increase the competitive gap between business pioneers and laggards.
DFGE implements new AI methods in the context of CSR and sustainability. This includes a thorough analysis of what is required to implement projects using related technologies, as well as promoting exchange on AI topics and the various possibilities in the field of CSR that came along recently. In this article, we describe the shift in CSR towards a data-driven business unit and further provide a short recap of required technical knowledge needed to understand the potential use of methods from the field of Artificial Intelligence for sustainability-related data. Besides, the article depicts how Artificial Intelligence is already harnessed for the use of scalable, global sustainable initiatives and finally provides an outlook of using the computational power of Machine Learning algorithms for measuring and evaluating CSR Management approaches.
CSR is increasingly becoming data-driven
Over the last decades, CSR has evolved impressively. Most companies are eager to extend their activities beyond philanthropy. Meanwhile, corporate leaders are aligning social impact, employee engagement, and environment-related performance indicators with business objectives to contribute to a sustainable economy.
That indicates that measuring results and ensuring sustainability efforts (on social, environmental, and economic level) really contributes to the company value: it is about meeting business goals while implementing sustainable business practices in the company’s strategy and demonstrating social and environmental commitment at the same time.
There are several possibilities to harness new analyzing techniques from the field of Artificial Intelligence for either implementing effective actions (applying AI for the social good) or tracking and measuring CSR efforts. But first, a definition of Artificial Intelligence.
Artificial Intelligence and Machine Learning – What is it?
Technologies related to the omnipresent terms Big Data, Artificial Intelligence, and Machine Learning (ML) are gaining momentum. Understanding the underlying concepts will help experts from the field of CSR to discover the potential for their own tasks and challenges.
AI generally describes the research field of enabling machines to solve certain tasks as good as humans. e.g., capturing the most relevant topics of a sustainability report. This is amongst other technologies achieved by Machine Learning, as one of the most popular subfields of AI. Machine Learning algorithms computationally “learn” relevant information directly from data without relying on predetermined rules. This includes feeding the algorithms with large amounts of data, i.e., Big Data, which could be thousands of sustainability reports split into several topics. After the learning phase, the newly acquired problem-solving competence, e.g., detecting topics in sustainability reports, is applied on unseen data.
Thus, Machine Learning can be interpreted as a data-driven, highly scalable method to solve problems or gain insights by analyzing large amounts of data.
Applying Artificial Intelligence for the social good
AI has enormous potential to serve the social good. If the scalable power of AI is leveraged effectively, it can accelerate progress on the United Nations’ Sustainable Development Goals (SDGs) by solving global problems. There are already famous examples for using AI on the action level. The Microsoft AI for Earth initiative puts Microsoft cloud and AI tools in the hands of those working to solve global environmental challenges. Google’s AI for Social Good program addresses amongst environmental challenges like flood prediction and intelligent farming, societal challenges, for example risk assessment for heart attacks. The programs contribute through collaborative efforts across all sectors, drawing on the scale of existing AI and software products and services as well as through ongoing investments in AI research.
Implement data-driven CSR analysis
Regarding the potential on the administrative or assessment level, AI allows to overcome restrictions regarding the type as well as the amount of data. Analyzing CSR-related actions can be taken to a new level with Machine Learning. There are various and inexhaustible data sources (textual sustainability reports, news and customers sentiments, technical sensor data, historical business numbers, satellite images etc.) that can be used for this purpose. New – intelligent – analyzing approaches will take this Big Data into account for finding underlying dependencies in data or predicting future developments.
In summary, AI will contribute to make CSR Management measurable, transparent and comparable. Consequently, associated indicators enable to keep track of the companies’ efforts, for example regarding the SDGs. This paradigm shift is in line with the need to consider CSR as business unit with respective business goals to be met.
DFGE can support you in collecting, analyzing and thus leveraging your sustainability-related data to gain valuable insights for your CSR Management. Contact us via firstname.lastname@example.org or via phone: +49 8192 99733 20 to learn more!