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SUMMARY:How to Train your (artificial) Brain: the IFC’s MALENA case
DESCRIPTION:ON-DEMAND WEBINAR\n			\n				\n				\n				\n				\n				Sensing Clues Wildlife Foundation\n			\n				\n				\n				\n				\n				Machine Learning ESG Analyst \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n					 \n			\n							\n							\n							\n							\n							\n			\n			\n			\n			\n			\n					\n							\n							\n							Blaise Sandwidi\n							Lead Data Scientist at IFC\n							 \n0\n \n						\n						\n				  	\n							\n							\n							\n							\n							\n			\n			\n			\n			\n			\n					\n							\n							\n							Carlos Arias\n							Senior Environmental Specialist at IFC\n							 \n0\n \n						\n						\n				  	\n			\n			\n				4\n				5\n			\n			\n			\n		   \n				\n				\n				\n				\n				\n				\n				\n					\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n			 \n				\n			\n				\n				\n				\n				\n				Fill out the form to watch the webinar\n			\n				\n				\n				\n				\n				\n					\n					\n			\n			\n				\n				\n				\n				\n			\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Using AI to Identify ESG Risk\nMALENA (Machine Learning ESG Analyst) is an Artificial Intelligence model developed by IFC. MALENA uses Natural Language Processing techniques to identify ESG risk and analyze its significance. MALENA uses the IFC’s Performance Standards and Corporate Governance Methodology as a framework to support ESG data analytics. MALENA’s model converts unstructured text into meaningful analysis\, insights\, patterns and themes. \nMALENA leverages long standing corpora of IFC ESG data and institutional knowledge to identify and infer ESG risks. By applying an in-house adapted Sentiment Analysis methodology\, MALENA provides easy-to-access project and portfolio insights. MALENA complements analysis by ESG Specialists and supports better-informed decision making. \nThe MALENA AI was developed using supervised machine learning\, an effective but time and data intensive approach that demands a lot of interaction between the “supervisors” (data analysts and data scientists) and the algorithm. Supervised machine learning requires the creation of large sets of labeled data to train the model to classify data and replicate outcomes on new datasets. The MALENA Data Readiness team is responsible for the curated collection of ESG data\, the creation of an ESG taxonomy and the labeling of over 100k labels. These labeled datasets prepared by the Data Readiness team of ESG analysts support data scientists who train and finetune models. \nIFC’s ESG taxonomy has been created by using the Poolparty application\, an approach we found to be very useful and resourceful. The iterative approaches to improving model performance have enabled the Data Readiness team to develop rules and quality control criteria to ensure high-quality training data. This approach to human-machine interaction\, informed by a set of labeling rules\, with the end goal of creating at-scale analytical capacity\, has proven to be a highly successful approach to maximize MALENA’s performance. This presentation introduces how IFC trained MALENA’s brain. \n			\n				WATCH NOW\n			\n			\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n			\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Speakers\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n					Carlos Arias\n					Senior Environmental Specialist at IFC \n					\n					LinkedIn\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n					Blaise Sandwidi\n					Lead Data Scientist at IFC \n					\n					LinkedIn
URL:https://recomentor.ai/event/how-to-train-your-artificial-brain-the-ifc-malena/
CATEGORIES:On-demand Webinars
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