# AI-powered mechanisms as judges: Breaking ties in chess

arXiv: https://arxiv.org/abs/2210.08289 · HTML: https://mlsanity.com/paper/2210.08289/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 15 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Aligning Superhuman AI with Human Behavior: Chess as a Model System](https://mlsanity.com/paper/2006.01855/) (2020)
- [On the ranking of a Swiss system chess team tournament](https://mlsanity.com/paper/1507.05045/) (2015)
- [Fairness in penalty shootouts: Is it worth using dynamic sequences?](https://mlsanity.com/paper/2004.09225/) (2020)
- [Fairer Chess: A Reversal of Two Opening Moves in Chess Creates Balance Between White and Black](https://mlsanity.com/paper/2108.02547/) (2021)

## Abstract (arXiv)

Recently, Artificial Intelligence (AI) technology use has been rising in sports to reach decisions of various complexity. At a relatively low complexity level, for example, major tennis tournaments replaced human line judges with Hawk-Eye Live technology to reduce staff during the COVID-19 pandemic. AI is now ready to move beyond such mundane tasks, however. A case in point and a perfect application ground is chess. To reduce the growing incidence of ties, many elite tournaments have resorted to fast chess tiebreakers. However, these tiebreakers significantly reduce the quality of games. To address this issue, we propose a novel AI-driven method for an objective tiebreaking mechanism. This method evaluates the quality of players' moves by comparing them to the optimal moves suggested by powerful chess engines. If there is a tie, the player with the higher quality measure wins the tiebreak. This approach not only enhances the fairness and integrity of the competition but also maintains the game's high standards. To show the effectiveness of our method, we apply it to a dataset comprising approximately 25,000 grandmaster moves from World Chess Championship matches spanning from 1910 to 2018, using Stockfish 16, a leading chess AI, for analysis.