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Title: 

AI and Procurement - A Primer

Authors: Sloane, Mona
Chowdhury, Rumman
Havens, John C.
Lazovich, Tomo
Rincon Alba, Luis
Keywords: Artificial Intelligence, AI, Data Science, Algorithms, Public Interest Technology, Government, Public Sector, Digital Service, Procurement, Purchasing
Issue Date: 28-Jun-2021
Abstract: Artificial intelligence (AI) systems are increasingly deployed in the public sector. As these technologies can harm citizens and pose a risk to society, existing public procurement processes and standards are in urgent need of revision and innovation. This issue is particularly pressing in the context of recession-induced budget constraints and increasing regulatory pressures. The AI Procurement Primer sets out to equip individuals, teams, and organizations with the knowledge and tools they need to kick-off procurement innovation as it is relevant to their field and circumstances. To do so, it first sets the scene by examining the histories and current issues related to procurement and AI. It then outlines six tension points that emerge in the context of procurement and AI - definitions, process, incentives, institutional structures, technology infrastructure, and liabilities - each of which are paired with a set of questions that can help address these tension points. The primer also outlines five narrative traps that can hinder equitable innovation in AI procurement, alongside strategies to avoid these traps. The primer closes with four calls for action as concrete steps that can be taken to create environments in which AI procurement innovation can happen, namely to re-define the process, create meaningful transparency, build a network, and cultivate talent.
URI: http://hdl.handle.net/2451/62255
Appears in Collections:Mona Sloane’s Collection

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