Taking a Byte Out of Corruption: A Data Analytic Framework for Cities to Fight Fraud, Cut Costs, and Promote Integrity
- Taking a Byte Out of Corruption: A Data Analytic Framework for Cities to Fight Fraud, Cut Costs, and Promote Integrity
- Columbia University. Center for the Advancement of Public Integrity
- Center for the Advancement of Public Integrity
- Persistent URL:
- Geographic Area:
- New York (State)--New York
- Center for the Advancement of Public Integrity, Columbia University
- Publisher Location:
- New York, NY
- In recent years, the emerging science of data analytics has equipped law enforcement agencies and urban policymakers with game-changing tools. Many leaders and thinkers in the public integrity community believe such innovations could prove equally transformational for the fight against public corruption. However, corruption control presents unique challenges that must be addressed before city watchdog agencies can harness the power of big data. City governments need to improve data collection and management practices and develop new models to leverage available data to better monitor corruption risks.
To bridge this gap and pave the way for a potential data breakthrough in anti-corruption oversight, the Center for the Advancement of Public Integrity (CAPI), with the support of the Laura and John Arnold Foundation, convened an expert working group of leading practitioners, scholars, engineers, and civil society members to identify key issues, obstacles, and knowledge gaps, and map a path forward in this promising area. CAPI supplemented the deliberations of this working group with further research and more than forty field interviews in New York and Chicago.
- Municipal government
Municipal government--Data processing
Local government--Data processing--Management
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- Suggested Citation:
- Columbia University. Center for the Advancement of Public Integrity, 2017, Taking a Byte Out of Corruption: A Data Analytic Framework for Cities to Fight Fraud, Cut Costs, and Promote Integrity, Columbia University Academic Commons, https://doi.org/10.7916/D88W3KVV.