FAIR AI. Identifying and mitigating bias in machine learning by Ruta Binkyte
The concerns about Fairness in AI are growing as the new technology becomes increasingly pertinent in healthcare, job hiring, loan granting, and other domains. This talk reviews well-known AI discrimination cases and introduces AI Fairness concepts and notions. The following discussion examines the types of bias in the data contributing to discrimination and suggests strategies for overcoming or mitigating them.
Decoding Business Choices: Causal Inference for Informed Decision-Making by Dzidas Martinaitis
In both business and the public sector, a lot of decisions are made on the fly or based on gut feelings, which can be pretty random. What's more, these choices aren't usually measured for their effects, either due to a lack of understanding or accountability. However, with causal inference and the data we've got these days, we can precisily estimate the impact of these decisions. Causal inference methods let us dive into the whole cause-and-effect thing using solid statistics and clever experiments. So now, instead of being in the dark, we can make decisions backed up by facts, helping us navigate the wild world of business way better. The talk will dive into a few real-world examples and the latest causal inference techniques, including the Double ML method.
