In an interview with IFLR, Mini vandePol and Manh Hung Tran discuss how AI is an enabler in ESG reporting, but also exposes organizations to a plethora of risks.
Companies are experiencing increased pressure to integrate ESG into their business strategies and offer sufficient transparency of their ESG performance. This is driven by a collective push by regulators, investors, shareholders, consumers and employees for companies to adopt more sustainable practices.
Mini notes that integrating AI into ESG practice can help to "enhance efficiencies in reporting processes by streamlining the workflow and generating quality information. It also reduces manual processes that can typically be prone to error to make room for more precision in ESG reporting — ensuring the company can more effectively communicate to stakeholders with quality information about its ESG performance."
As AI systems highly depend on a large amount of data, risks can arise from the processing, use, analysis and storage of personal data, among other risks. As jurisdictions have been taking different approaches in AI oversight with both voluntary guidance and mandatory rules, Hung recommends that: "in the absence of a uniform regulatory framework, companies and organizations are encouraged to follow the OECD principles, which have been a global benchmark for AI governance. Companies can also participate in dialogue with policymakers to get a grasp of the evolving legal and enforcement landscape so as to prepare strategic and workable AI governance policies."
Read the full article here (subscription access required).
Companies are experiencing increased pressure to integrate ESG into their business strategies and offer sufficient transparency of their ESG performance. This is driven by a collective push by regulators, investors, shareholders, consumers and employees for companies to adopt more sustainable practices.
Mini notes that integrating AI into ESG practice can help to "enhance efficiencies in reporting processes by streamlining the workflow and generating quality information. It also reduces manual processes that can typically be prone to error to make room for more precision in ESG reporting — ensuring the company can more effectively communicate to stakeholders with quality information about its ESG performance."
As AI systems highly depend on a large amount of data, risks can arise from the processing, use, analysis and storage of personal data, among other risks. As jurisdictions have been taking different approaches in AI oversight with both voluntary guidance and mandatory rules, Hung recommends that: "in the absence of a uniform regulatory framework, companies and organizations are encouraged to follow the OECD principles, which have been a global benchmark for AI governance. Companies can also participate in dialogue with policymakers to get a grasp of the evolving legal and enforcement landscape so as to prepare strategic and workable AI governance policies."
Read the full article here (subscription access required).
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