In today's competitive and fast-evolving business environment, it is a critical time for
organizations to rethink how to make talent-related decisions in a quantitative manner …

AI models are increasingly applied in high-stakes domains like health and conservation.
Data quality carries an elevated significance in high-stakes AI due to its heightened …

Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine
Learning (ML) applications to inform how to best combine both human domain expertise and …

Analysts often clean dirty data iteratively--cleaning some data, executing the analysis, and
then cleaning more data based on the results. We explore the iterative cleaning process in …

Predictive models based on machine learning can be highly sensitive to data error. Training
data are often combined with a variety of different sources, each susceptible to different …

Abstract AutoML (Automated Machine Learning) is an emerging field that aims to automate
the process of building machine learning models. AutoML emerged to increase productivity …

Reinforcement learning (RL) is broadly employed in human-involved systems to enhance
human outcomes. Off-policy evaluation (OPE) has been pivotal for RL in those realms since …

The ubiquitous use of machine learning algorithms brings new challenges to traditional
database problems such as incremental view update. Much effort is being put in better …

The analyst effort in data cleaning is gradually shifting away from the design of hand-written
scripts to building and tuning complex pipelines of automated data cleaning libraries. Hyper …

The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife
preservation, autonomous driving and criminal justice system calls for a data-centric …