# Explaining Results of Multi-Criteria Decision Making

arXiv: https://arxiv.org/abs/2209.04582 · HTML: https://mlsanity.com/paper/2209.04582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 40 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [A Core Calculus for Provenance](https://mlsanity.com/paper/1310.6299/) (2013)

## Abstract (arXiv)

We introduce a method for explaining the results of various linear and hierarchical multi-criteria decision-making (MCDM) techniques such as WSM and AHP. The two key ideas are (A) to maintain a fine-grained representation of the values manipulated by these techniques and (B) to derive explanations from these representations through merging, filtering, and aggregating operations. An explanation in our model presents a high-level comparison of two alternatives in an MCDM problem, presumably an optimal and a non-optimal one, illuminating why one alternative was preferred over the other one. We show the usefulness of our techniques by generating explanations for two well-known examples from the MCDM literature. Finally, we show their efficacy by performing computational experiments.