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Are Ethical Controls in Artificial Intelligence An ESG Concern?

By: Issuewire

New York City, New York May 24, 2022 ( - Investment in artificial intelligence (AI) has been rising dramatically year on year from US$36 billion in 2020 to US$77 billion in 2021, a massive 115% increase. AI and its ability to interpret unqualified and unstructured data offers enormous potential and is already powering new scientific discoveries, along with robotics will transform how live and work. The positive impact of AI is clear, it gives us tools to better understand the world, but there are also risks and unknowns. We anticipate that AI will change the labor force, white-collar jobs have already been impacted with automation taking over roles in businesses from law firms to accounting companies. AI programs are written by people and people are often subject to what we call 'unconscious bias' as we strive to teach machines to think how we think. 

The interest in AI in relation to environment, society, and governance (ESG) investment focuses on how intelligent machines can analyze data to help investors make better ESG decisions by identifying risk. However, when AI is at risk of bias or when AI invisibly controls more and more aspects of our lives, and when we the subjects of these programs don't understand how these decisions are arrived at, there are serious ethical considerations. 

There are numerous real-world examples of algorithmic bias impacting people's lives, particularly on issues such as ethnicity, where AI has disproportionately harmed people of color when it comes to renting or purchasing homes, or setting bail conditions. AI can affect employment chances as it sorts candidates with the end-users of these systems who are unaware of how the program arrived at a decision, just that it did. 

As an investor in AI and committed to the principles of ESG, when investors make a decision to seed or take a position in an AI research company, we need to ask questions about the algorithms. Investors need to ask questions about how the training data for an AI was selected. They need to consider the experience and diversity of the people building the AI to ensure that the social analysis or assumptions behind the program accurately reflect society or the subject of study. 

Datasets are particularly important for AI training, computer vision programs have been notoriously weak at identifying darker skin tones. There are several instances where facial recognition has resulted in false arrests of African Americans across the United States. As AI becomes more widespread and usage increases, false positives won't cause harm to a few, it can quickly become the many. 

AI can help us spot the risks in a supply chain for ESG and make better decisions to qualify what products or industries meet ethical and sustainable standards. AI can guide fund-managers towards better investments, and high-speed algorithms backed by machine learning generate even greater profits. Computers handle data better than people, but human consciousness' unique quality has been in its ability to take disparate information and connect it. AI is about teaching computers how to do that, and we as human beings are limited by our own experiences, at times our own selfishness, and our own values. If we are going to let AI make decisions for us then it falls within the ESG remit to ask questions about how we are teaching these AI to ensure that we don't cause harm to society.



Gilles Rollet is a banker with several successful investments in the financial services industry. He started his career at Goldman, Sachs & Co. in New York. Subsequently, he held a number of senior management and country management positions at ABN AMRO, throughout Asia, South Africa, San Francisco, Geneva, and Dubai. He later set up and ran Mirabaud & Cie. in Dubai. In 2014, Gilles became an entrepreneur and launched an asset management firm in Dubai, which he successfully sold in 2017. In 2016, he founded a private bank in the US. Today, Mr. Rollet is a passive investor in firms active in asset management, banking, AI, robotics, data banks, and public health.


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This article was originally published by IssueWire. Read the original article here.

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