ai-act
The promise and perils of using artificial intelligence to fight corruption - Nature Machine Intelligence
Corruption presents one of the biggest challenges of our time, and much hope is placed in artificial intelligence (AI) to combat it. Although the growing number of AI-based anti-corruption tools (AI-ACT) have been summarized, a critical examination of their promises and perils is lacking. Here we argue that the success of AI-ACT strongly depends on whether they are implemented top–down (by governments) or bottom–up (by citizens, non-governmental organizations or journalists). Top–down use of AI-ACT can consolidate power structures and thereby pose new corruption risks. Bottom–up use of AI-ACT has the potential to provide unprecedented means for the citizenry to keep their government and bureaucratic officials in check. We outline the societal and technical challenges that need to be overcome to harness the potential for AI to fight corruption. Despite the growing number of initiatives that employ AI to counter corruption, few studies empirically tackle the political and social consequences of embedding AI in anti-corruption efforts. The authors outline the societal and technical challenges that need to be overcome for AI to fight corruption.
Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approaches
Köbis, Nils, Starke, Christopher, Rahwan, Iyad
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".
- Europe > United Kingdom > England > Oxfordshire > Oxford (0.14)
- South America > Brazil (0.14)
- Europe > Ukraine (0.04)
- (13 more...)
- Media > News (1.00)
- Law > Criminal Law (1.00)
- Law Enforcement & Public Safety > Fraud (1.00)
- (4 more...)
- Information Technology > Communications > Social Media (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Applied AI (0.94)
- Information Technology > Artificial Intelligence > Machine Learning > Performance Analysis > Accuracy (0.69)