Fairness: How AI Creates and Maintains Inequalities

Fairness: How AI Creates and Maintains Inequalities

By Josep Curto Diaz

AI is increasingly used in highly sensitive areas such as medical diagnosis, human resource selection, or criminal justice. Therefore, the attention of professionals, users, and regulators is increasingly focused on the quality of decisions adopted, including the concern to guaranteeing its equitable, non-discriminatory nature.

Inequity can arise in AI through social bias built into training data sets, in decisions made during the machine learning process, and in the complex feedback loops that arise when a machine learning model is implemented in the real world.

In this talk, Josep will speak about the problem of the absence of Fairness in AI systems. The talk will be about some of the ways and tools we have today to measure and mitigate the existence of bias and ensure better decision-making.

About the Speaker

Josep is an advisor, consultant, data scientist, entrepreneur and professor who has helped many companies to create competitive advantages based on data and algorithms. He believes that the combination of data, models, technology, research, ethics and experience is the ultimate blend to help organizations around the world to provide better services and create new products.

In addition to his consultancy, technology and advisory services in Responsible AI and Human-Centered AI through AthenaCore, he is member of the advisory board at L’Escola L’Horitzó. He complements his professional career with a passion for higher education being the Academic Director of the Master in Business Intelligence and Big Data Analytics (MIBA) at Universitat Oberta de Catalunya (UOC), and casual lecturer at the Australian Graduate School of Management of the University of New South Wales where he gives lectures about responsible AI, data science, data governance, big data and data-driven strategies for future managers and developers.

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