At present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed... Show moreAt present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed throughout and present studies’ direct usage of eXplainable Artificial Intelligence (XAI) approaches to identify and study bias. To solve the problem of locating bias and then mitigating it has been achieved by Gopher [1]. It generates interpretable top-k explanations for the unfairness of the model and it also identifies subsets of training data that are the root cause of this unfair behavior. We utilize this system to study the effect of pre-processing on bias through provenance. The concept of data lineage through tagging of data points during and after the pre-processing stage is implemented. Our methodology and results provide a useful point of reference for studying the relation of pre-processing data with the unfairness of the machine learning model. Show less
Numeric performance ratings have been a component of performance evaluation for decades (Prowse & Prowse, 2009; Pulakos, Mueller-Hanson & Arad... Show moreNumeric performance ratings have been a component of performance evaluation for decades (Prowse & Prowse, 2009; Pulakos, Mueller-Hanson & Arad, 2019). Yet, in recent years their necessity has been questioned (Adler, Campion, Colquitt, Grubb, Murphy, Ollander-Krane, & Pulakos, 2016), with some organizations going so far as to remove numeric ratings entirely (Capelli & Tavis, 2016; Rock, Davis & Jones, 2014; Burkus, 2016). Unfortunately, this practice has been largely unexamined in an empirical manner. The present study tested whether the claim – that numeric ratings do not matter – holds up in all cases. This is done by exploring whether the presence or absence of numeric ratings, impacts employee perceptions of fairness associated with the appraisal. As numeric ratings are argued to be a mechanism for communicating a fair, standard, and consistent practice, the study aimed to understand if the mere presence of numeric ratings may offset some of the negative reaction employees have toward performance appraisal when they have poor-quality relationships with their supervisors. Findings indicated that while employee-manager relationship quality (assessed via Leader-Member Exchange) has a direct relationship with perceptions of fairness associated with the appraisal, the presence of numeric ratings did not moderate this relationship. Practical implications and future research recommendations are discussed. Show less
