In this paper, we review algorithmic bias in education, discussing the causes of that bias and
reviewing the empirical literature on the specific ways that algorithmic bias is known to have …

Algorithmic decision-making is becoming increasingly common as a new source of advice in
HR recruitment and HR development. While firms implement algorithmic decision-making to …

Automated speech recognition (ASR) systems, which use sophisticated machine-learning
algorithms to convert spoken language to text, have become increasingly widespread …

This article introduces algorithmic bias in machine learning (ML) based marketing models.
Although the dramatic growth of algorithmic decision making continues to gain momentum in …

How has recent AI Ethics literature addressed topics such as fairness and justice in the
context of continued social and structural power asymmetries? We trace both the historical …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

Research in machine learning (ML) has argued that models trained on incomplete or biased
datasets can lead to discriminatory outputs. In this commentary, we propose moving the …

It has been the historic responsibility of the social sciences to investigate human societies.
Fulfilling this responsibility requires social theories, measurement models and social data …

A recent normative turn in computer science has brought concerns about fairness, bias, and
accountability to the core of the field. Yet recent scholarship has warned that much of this …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …