# Trustworthy and Explainable Decision-Making for Workforce allocation

arXiv: https://arxiv.org/abs/2412.10272 · HTML: https://mlsanity.com/paper/2412.10272/

## Key facts

- Cited by 1 papers 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 (most cited first, 3 of 3)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Machine Reasoning Explainability](https://mlsanity.com/paper/2009.00418/) (2020)
- [Finding Counterfactual Explanations through Constraint Relaxations](https://mlsanity.com/paper/2204.03429/) (2022)

## Built on by (most cited first, 1 of 1)

- [Exploring Plan Space through Conversation: An Agentic Framework for LLM-Mediated Explanations in Planning](https://mlsanity.com/paper/2603.02070/) (2026)

## 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.