People have more trust in robots than their managers, according to the second annual AI at Work study conducted by Oracle and Future Workplace, a research firm preparing leaders for disruptions in recruiting, development and employee engagement. The study of 8,370 employees, managers and HR leaders across 10 countries, found that AI has changed the relationship between people and technology at work and is reshaping the role HR teams and managers need to play in attracting, retaining and developing talent. Contrary to common fears around how AI will impact jobs, employees, managers and HR leaders across the globe are reporting increased adoption of AI at work and many are welcoming AI with love and optimism. The increasing adoption of AI at work is having a significant impact on the way employees interact with their managers. As a result, the traditional role of HR teams and the manager is shifting.

A recent study conducted by Oracle and research firm Future Workplace found that 64% of people would trust a robot more than their manager. The study included 8,370 employees, managers and HR leaders across 10 countries. Its aim was to see how AI has changed relationships between people and technology at work. It did have some surprising results when comparing human supervisors to potential robot overlords. According to the study, 64 % of people would trust a robot over their manager.

Workers in India (89%) and China (88%) are more trusting of robots over their managers, followed by Singapore (83%), Brazil (78%), Japan (76%), UAE (74%), Australia/New Zealand (58%), the U.S. (57%), the U.K. (54%), and France (56%). More men (56%) than women (44%) have turned to AI over their managers.

On the sidelines of an AI for Good conference in Geneva, where humans and robots mingled and discussed how machine learning could help global development, IRIN rolled up its data science sleeves and deployed a bot on an important new challenge: making up names of aid agencies and aid job titles. It's a new take on some of the jargon and repetitiveness that can be found in the relief and development sector, and hopefully gives a taste of the rudiments of machine learning. The A-Z list goes from "A Call to Serve International" to "Zwanan Development Organization". Then we fed them into a machine learning system running open-source code in the Python language. The aid organisation names were processed by a recurrent neural network built on the TensorFlow system.