Using Natural Language Processing and machine learning to meet EU Taxonomy reporting requirements at scale
After you have proven that your investment engages in activities that are included in the technical criteria of the EU Taxonomy regulation then we turn our attention towards proving that the activities “Do No Significant Harm” and meet “social safeguards” through controversies.
Clarity AI has a unique approach to the process, using Natural Language Processing and machine learning to detect what controversies might be at play, what type of controversy and the severity. In order to do that we have built a machine learning algorithm. There are two primary benefits of this approach, scale and minimizing biases. We are able to process vast volumes of information, more than 100,000 articles per day from over 30,000 sources. The scale at which we can process data is greater than we could ever have done traditionally. In the past, you would have had teams of people reading articles, analyzing and assessing each piece for controversies.
The second advantage is minimizing bias, by thoughtfully utilizing the human element in data analysis. Leveraging the use of technology helps us to reduce biases from people’s personal backgrounds and skill sets. Even though we are a technology company, it is important to underscore that we haven’t taken the human element out of the equation.
We make sure that all solutions and products are anchored in human expertise in the sustainability process. These experts are how we codify our algorithm rules and prepare the datasets for the algorithm inputs. An important part of our human expertise is used to validate the system and monitor its performance. Overall this process means that we can handle data volumes more efficiently while also utilizing our human capital in a more efficient manner.
