At the crossroads of technology, innovation, and sustainability, artificial intelligence has the ability to make a dramatic impact on ESG investing—that is, accounting for environmental, social, and governance risks and opportunities in investing. While AI can unearth key data for investors seeking sustainable investments, discerning unreliable information will be a key challenge and humans will not be replaced any time soon.

Advances in AI, which encompasses a variety of emerging computer technologies, have made it easier than ever before for computers to automate complex tasks at incredible speeds and volumes, thus revolutionizing how companies work with data. As AI, along with machine learning and robotics, have become commonplace and essential to the operations of mainstream organizations, leadership teams have found that failure to harness and leverage AI puts them behind the competition. Repeatable tasks are carried out by bots in a fraction of the time, and algorithms and computer programs can read information that might have previously been unusable due to its size or amount of garbage data.

These AI capabilities will prove useful for ESG investing, which reflects the growing sensitivity of consumers to how companies operate as factors in their buying decisions and is of increasing interest to investors who are concerned about companies adopting practices that will mitigate risk and ensure their long-term sustainability.  Investment managers are coming under increasing pressure to measure ESG criteria in their portfolios. However, a lack of data is making it hard for banks to assess long-term risks and rewards. Here, AI is the answer: technologies will filter essential data that investors currently lack, acting as the catalyst for sustainable investing at scale.

Much of the potential for artificial intelligence in ESG investing comes from sentiment analysis algorithms. These algorithms allow computers to analyze the tone of a conversation, a task that code could not as effectively do. Sentiment analysis programs might be trained to read a certain type of conversation and analyze the tone by comparing the words used to a reference set of existing information. For example, a program trained to read the transcripts of a company’s quarterly earnings calls could determine the tone of the words when the CEO speaks, use natural language processing to easily identify in which parts of the conversation the CEO talks about ESG-related topics, and then infer from those words used how committed a company appears to be about mitigating environmental risks.

If ESG investing involves considering the material opportunities and risks of sustainable decision-making, AI provides both tremendous benefits and risks to watch for. In short, while giving ESG investing the opportunity to grow and expand, AI can itself be an ESG risk for companies that aim to undertake the effort.

Adopting AI for any purpose can pose a significant environmental impact. The process for creating and training AI algorithms requires large amounts of computing power, which in turn consumes large amounts of electrical energy.

In addition to enhancing investors’ abilities to analyze companies in general, AI provides companies more power to analyze everything for which they can collect data. This expansiveness is expected to create some challenges for companies that wish to perform well on ESG metrics as they grapple with how to manage this evolving technology. Google created an ethics advisory board in 2019 to guide its research into and use of AI, but had to quickly disband the board due to controversy over some of the board members. New algorithms can also replicate existing problems in society if the dataset that teaches programs is itself biased. For example, some facial-recognition systems are reportedly better at recognizing white men than black women, because existing image datasets tend to include more men and white people. General ethical concerns about the use of data by AI technologies might be especially relevant for banks and other financial companies, since they have vast troves of highly personal data.

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