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Technological Revolution: The Role of Tech, AI and Machine Learning in Political Corruption

Type
Closed Panel
Language
English
Description

The future of anti-corruption efforts increasingly relies on emerging technologies to enhance transparency, accountability, and fraud detection in public organizations.This panel will explore the intersection of data-driven technologies as powerful tools in the fight against corruption. We will examine recent advancements, challenges, and ethical dilemmas involved in deploying these technologies to detect, prevent, and prosecute corrupt practices. We will also assess their role as complementary tools, rather than standalone solutions, in both advancing and potentially undermining anti-corruption efforts.

The discussion will feature real-world examples, such as the transformative role of emerging technologies in streamlining conflict-of-interest detection and fraud prevention within public procurement. This includes, but is not limited to, the use of advanced computing technologies to predict corruption based on political and economic factors, AI-driven models that detect conflicts of interest in public procurement, and advanced image recognition to detect fraudulent suppliers.

The panel will also explore the risks and ethical concerns associated with the rise of these technologies. Topics will include privacy breaches, surveillance overreach, cyberattacks,the exclusion of vulnerable communities, the weaponization of public opinion, and the possibility of exacerbating corruption through outsourced AI systems. The ultimate goal is to provide a holistic overview of how these tools can be responsibly integrated into anti-corruption frameworks while offering both immediate empirical insights and long-term theoretical exploration for policymakers and researchers.

This panel invites you to a balanced conversation about the double-edged nature of technological advancements that can both combat and complicate the fight against corruption in today’s digital age. Both theoretical contributions and empirical papers are welcome to be submitted.

Onsite Presentation Language
Same as proposal language
Panel ID
PL-9427