Inferring The World's Gender and Ethnic Diversity using Personal Names
The world of artificial intelligence (AI) is evolving at a breakneck pace, transforming industries from healthcare to finance. But as AI systems become more integrated into our daily lives, questions about fairness, representation, and bias are more important than ever. Who is shaping the future of AI research? Are diverse voices and perspectives being included in the conversation?
This study examines the relationship between country-level corruption and audit pricing decisions across EU27 countries. Using a sample of 29,607 firm-year observations for the period 2011–2021, I find that firms headquartered in more corrupt countries pay higher audit fees and have longer reporting lags. I also show that this relationship is stronger after the EU audit reform came into force in 2016. The results suggest that auditors respond to corruption risk stemming from the broader macroeconomic environment by increasing audit fees and audit effort. […]
Mixity has partnered with Namsor to enable organizations to measure the perceived ethnic diversity of their employees using artificial intelligence (AI) and machine learning. This makes it possible to identify and resolve inequalities linked to cultural and ethnic origins. According to French statistical office (INSEE), these inequalities are manifested by higher unemployment rates, less durable and qualified jobs and lower remuneration for immigrant populations.
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