Summary of Research Papers on Automated Video Interview Judgment and Algorithmic Bias
Introduction
This summary provides an overview of several research papers that discuss the topics of automated video interview judgment and algorithmic bias. The papers were published between 2016 and 2019 and cover various aspects of these subjects.
"Automated video interview judgment on a large-sized corpus collected online" (2017)
This paper, authored by Lei Chen, Ru Zhao, Chee Wee Leong, Blair Lehman, Gary Feng, and Mohammed Ehsan Hoque, was presented at the Seventh International Conference on Affective Computing and Intelligent Interaction (ACII) in 2017. The authors discuss the use of automated video interview judgment on a large-sized corpus collected online. The paper explores the potential benefits and challenges of using automated systems to evaluate video interviews.
"The frontiers of fairness in machine learning" (2018)
Authored by Alexandra Chouldechova and Aaron Roth, this paper was published as an arXiv preprint in 2018. The authors delve into the concept of fairness in machine learning and discuss the challenges and frontiers in ensuring fairness in algorithmic decision-making. The paper provides insights into the ethical considerations and potential biases that can arise in machine learning algorithms.
"Bias and productivity in humans and algorithms: Theory and evidence from resume screening" (2018)
This paper by B. Cowgill, a researcher from Columbia Business School, was published in 2018. The author explores the concept of bias and productivity in both humans and algorithms, specifically focusing on resume screening. The paper presents theoretical and empirical evidence to understand the impact of bias on productivity and the potential biases that can arise in algorithmic decision-making.
"Economics, fairness and algorithmic bias" (2019)
Authored by B. Cowgill and C. E. Tucker, this paper was published as an NBER Working Paper in 2019. The authors discuss the intersection of economics, fairness, and algorithmic bias. The paper explores the economic implications of algorithmic bias and provides insights into the challenges and potential solutions to address bias in algorithmic decision-making.
"Amazon scraps secret AI recruiting tool that showed bias against women" (2018)
This article by Jeffrey Dastin was published in 2018 and discusses the case of Amazon scrapping a secret AI recruiting tool that exhibited bias against women. The article highlights the importance of addressing algorithmic bias and the potential consequences of biased algorithms in real-world applications.
Overall, these research papers and article shed light on the topics of automated video interview judgment and algorithmic bias. They provide valuable insights into the challenges, implications, and potential solutions related to bias in algorithmic decision-making.
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