mlsanityData to 14 Oct 2026

Trustworthy and Explainable Decision-Making for Workforce allocation

arXiv 2412.10272 ↗First posted 13 Dec 2024cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

In industrial contexts, effective workforce allocation is crucial for operational efficiency. This paper presents an ongoing project focused on developing a decision-making tool designed for workforce allocation, emphasising the explainability to enhance its trustworthiness. Our objective is to create a system that not only optimises the allocation of teams to scheduled tasks but also provides clear, understandable explanations for its decisions, particularly in cases where the problem is infeasible. By incorporating human-in-the-loop mechanisms, the tool aims to enhance user trust and facilitate interactive conflict resolution. We implemented our approach on a prototype tool/digital demonstrator intended to be evaluated on a real industrial scenario both in terms of performance and user acceptability.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 39 references that are in the map. First posted on arXiv 13 Dec 2024.

Builds on

3 of 39 references.

References · 3

  1. 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,416
  2. 2020Machine Reasoning Explainabilitycited by 4
  3. 2022Finding Counterfactual Explanations through Constraint Relaxationscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.