  Employers are adopting algorithmic hiring technology throughout the
recruitment pipeline. Algorithmic fairness is especially applicable in this
domain due to its high stakes and structural inequalities. Unfortunately, most
work in this space provides partial treatment, often constrained by two
competing narratives, optimistically focused on replacing biased recruiter
decisions or pessimistically pointing to the automation of discrimination.
Whether, and more importantly what types of, algorithmic hiring can be less
biased and more beneficial to society than low-tech alternatives currently
remains unanswered, to the detriment of trustworthiness. This multidisciplinary
survey caters to practitioners and researchers with a balanced and integrated
coverage of systems, biases, measures, mitigation strategies, datasets, and
legal aspects of algorithmic hiring and fairness. Our work supports a
contextualized understanding and governance of this technology by highlighting
current opportunities and limitations, providing recommendations for future
work to ensure shared benefits for all stakeholders.
