Project description
The present project collaboration is a collective endeavor with Ms. Paulina Alvarado-Goldman, who is an adjunct faculty member at Rider University, and myself. The objective of this collaboration is a mapping of the utilization of artificial intelligence (AI) and its implementation in the context of anti-corruption efforts within the public sector. Ms. Goldman's analysis encompasses the utilization of AI, the enhancement of capabilities, the identification of potential risks, and the prospects presented by these emerging technologies in the context of anti-corruption strategies within various departments of the US government. In my analysis, I undertake a similar examination for the German federal ministries, employing a comparative institutional framework to illuminate the similarities and differences between the two nations.
When we refer to AI, we make reference to a broad class of automated technologies that include those based on machine learning, such as generative AI, predictive models, rule-based systems, and optimization algorithms.
Project Purpose
The objective of this study is to comprehend the manner in which algorithmic systems, encompassing generative AI and other nascent technologies, are being employed in anti-corruption initiatives within the public sector.
Background
Emerging technologies are advancing at a pace that challenges government adaptation. Continuous and rapid developments, particularly those powered by large language models and neural networks, are transforming fields from data analysis to automation. Although some governments and public sector organizations have adopted these tools, many public agencies face technical and budgetary constraints that limit their capacity to leverage these technologies fully. As a result, these tools are often adopted at the end of their hype cycle or it is very challenging to pivot towards more innovative solutions when they become outdated. Despite the fact that these technologies hold significant potential for improving transparency, organizational efficiency, and accountability in governance, public agencies struggle to adequately monitor their risks. For instance, data-driven biases may lead to discriminatory outcomes, and blockchain’s decentralized nature could complicate corruption tracing.
This research identifies a data adoption and governance framework to better assess the role of generative AI tools in anti-corruption efforts. We draw on an extensive literature review and interviews. The proposed framework offers indicators to determine when it is appropriate to apply such tools and when their use may be problematic. Given the complexity of organizational crime and corruption, these technologies can play a substantial role in detecting corrupt practices effectively and pivoting when those tools are no longer effective.
Project Objectives
- Getting a baseline of the use of AI, the anti-corruption efforts, and the use of AI in anti-corruption efforts;
- Mapping various obstacles in the usage and implementation of AI (legal, administrative, financial, economic, technical, public trust, political, etc.)
- Risk mapping;
- What oversight and compliance mechanisms are in place?
- How are transparency and accountability managed?
Bildquelle : Pixabay.com
Management
Monika Bancsina
Frau Paulina Alvarado-Goldman
Project duration
2025-