Fairness in AI Based Recruitment and Career Pathway Optimization

Work has long been a source of human livelihood, financial security, mental and physical well-being, dignity, and meaning. However, advances in computing, big data, artificial intelligence (AI), robotics, and related technologies are expected to usher in unprecedented and widespread changes in the economy and society. It is estimated that by 2030 up to 14% of the global workforce may need to change occupational categories as the world of work is disrupted by technological advances. Many current and future workers that will enter the workforce lack skills that in-demand and future jobs require. In short, the landscape of work is poised for a major and unprecedentedly rapid transformation and this calls for a variety of strategies to meet the needs of workers, employers, the economy, and broader society. Motivated by these concerns, we investigate two key problems faced by organizations and workers in the future of work. As AI has expanded into human resource applications, organizations are increasingly using AI-based recruitment for sourcing, screening, and selecting talent. We explain how this can lead to biases in decisions and how this bias can be measured, review tools available for bias mitigation, and discuss future challenges for fairness in machine learning specific to recruitment applications. Alongside this, workers are affected not only by biased recruitment, but by the growing automation of tasks in occupations, which will increasingly require job and task transitions. To help workers navigate these transitions effectively, we propose a genetic-algorithm-based optimization engine to search for a worker’s optimal career pathway in a network of occupations, given their current knowledge, skills, abilities, and other work-related characteristics. Overall, this thesis presents strategies for organizations to mitigate bias in AI-based recruitment and for workers to plan their career pathway in the face of unprecedented changes in the world of work.