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EthicsGrade takes on greenwashing with new ESG predictor technology


Currently, the lack of sufficient and accurate data on the ESG (Environmental, Social, and Governance) practices implemented by companies means exaggeration and greenwashing is rife. EthicsGrade are looking to tackle this with the launch of new machine learning technology.


EthicsGrade, an ESG (Environmental, Social, and Governance) ratings agency specialising in evaluating the ESG risks caused by digitalisation has launched a new machine learning prediction engine set to improve the quality of data on ESG practices, reduce the burden of gathering this data, and increase transparency.


To date, EthicsGrade has rated over 300 companies relating to their governance of technology, in sectors spanning energy, pharmaceuticals, FMCG and transport. By the end of the year they will have graded the full Russell 3000 Index.


The EthicsGrade prediction engine looks at the top 10 indicators of best practice digital governance in order to predict the overall Corporate Digital Responsibility (CDR) rating of a company - lightening the burden of ESG reporting for corporates.


This is a unique approach, unlike other options on the market, which have one set point of view. From a database of over 400, and growing, the prediction engine selects a smaller number of questions which are the best predictors for an ESG rating. This means companies can report high-quality data in a shorter period of time.


EthicsGrade currently focuses on CDR, with the intention to move to wider ESG issues in the future, with the capability to tailor ESG ratings to the specific interests of an investor, focusing on their values and priorities.


Charles Radclyffe, CEO and Co-Founder, of EthicsGrade said: “We have set out to solve the problem facing many corporations when dealing with existing ESG reporting, including having to look at a multitude of issues, with much duplication and, at times, questionable relevance. This creates reluctance to engage, poor quality data, and ultimately, we believe, greenwashing.”


“Developing an ESG rating is complex, and complicated by the need to look behind the headlines to find evidence of real progress. The prediction engine allows us to put multiple different lenses on the same company to provide an ESG rating specific to a stakeholder’s concerns.”


“And for the company, it gives them a strong dataset of digital risks. For example, it’s one thing helping Starbucks understand the level of maturity of the governance of their digital platforms; but they know, and we know, that the risks they face - whether from cybersecurity/ ransomware, data breach, or simple system outages will often originate with their suppliers. And the business case is not just the potential regulatory or operational costs, it's the reputational harm of screwing up. As recently happened with KFC.”


A company’s engagement is not required to predict an ESG rating via the EthicsGrade prediction engine, ensuring investors have the information they need, regardless of whether a company has time or inclination to respond. Initial grading is performed with public data, while the secondary round includes non-public data provided by companies on their platform, InsiderView.



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