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Found 10 papers, 1 papers with code
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II
In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk.
Mitigating Label Bias via Decoupled Confident Learning
While the presence of labeling bias has been discussed conceptually, there is a lack of methodologies to address this problem.
Learning Complementary Policies for Human-AI Teams
We then extend our approach to leverage opportunities and mitigate risks that arise in important contexts in practice: 1) when a team is composed of multiple humans with differential and potentially complementary abilities, 2) when the observational data includes consistent deterministic actions, and 3) when the covariate distribution of future decisions differ from that in the historical data.
Learning to Advise Humans in High-Stakes Settings
The human's reconciliation costs and imperfect discretion behavior introduce the need to develop AI systems which (1) provide recommendations selectively, (2) leverage the human partner's ADB to maximize the team's decision accuracy while regularizing for reconciliation costs, and (3) are inherently interpretable.
Algorithmic Fairness in Business Analytics: Directions for Research and Practice
The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies.
More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias
We then empirically show that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem.
A Machine Learning Framework Towards Transparency in Experts' Decision Quality
We first formulate the problem of estimating experts' decision accuracy in this setting and then develop a machine-learning-based framework to address it.
Cost-Accuracy Aware Adaptive Labeling for Active Learning
Moreover, a given labeler may exhibit different labeling accuracies for different instances.
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness
We formulate a multi-objective optimization for building a surrogate model, where we simultaneously optimize for both predictive performance and bias.
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation
In this work we present DJ-MC, a novel reinforcement-learning framework for music recommendation that does not recommend songs individually but rather song sequences, or playlists, based on a model of preferences for both songs and song transitions.
