# Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approaches

arXiv: https://arxiv.org/abs/2102.11567 · HTML: https://mlsanity.com/paper/2102.11567/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 106 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Society-in-the-Loop: Programming the Algorithmic Social Contract](https://mlsanity.com/paper/1707.07232/) (2017)
- [Botivist: Calling Volunteers to Action Using Online Bots](https://mlsanity.com/paper/1509.06026/) (2015)
- [AI for the Common Good?! Pitfalls, challenges, and Ethics Pen-Testing](https://mlsanity.com/paper/1810.12847/) (2018)
- [Predicting Financial Crime: Augmenting the Predictive Policing Arsenal](https://mlsanity.com/paper/1704.07826/) (2017)
- [Artificial Intelligence for EU Decision-Making. Effects on Citizens Perceptions of Input, Throughput and Output Legitimacy](https://mlsanity.com/paper/2003.11320/) (2020)

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

- [Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature](https://mlsanity.com/paper/2103.12016/) (2021)

## Abstract (arXiv)

Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens' views, hence putting "society in the loop".