How to Innovate AI Procurement?

Guest post by Dr. Mona Sloane, New York University

This Success Story is a report on the results of the Northeast Big Data Innovation Hub’s 2020 Seed Fund program.

Artificial intelligence (AI) systems are increasingly deployed in the public sector. As these technologies can harm citizens and pose 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. 

Our AI Procurement Roundtables Project brought together leading experts in the public sector, data science, civil society, policy, social science, and the law to generate a structured understanding of existing public procurement processes and identify how they can best mitigate risk and support community needs. Three separate conversations focused on mapping data science solutions used by public institutions; algorithmic justice and responsible AI and governance innovation and procurement in the context of AI. 

The lively discussions form the basis of our “Innovating AI Procurement Primer” primer, which will be launched in a public event on June 28, 2021, 12-1pm EST — you can sign up here

The report 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 report also outlines five narrative traps that can hinder equitable innovation in AI procurement:

  1. “We must engage the public.”
  2. “We must find simple definitions of ‘X’.”
  3. “The main threat is the government use of data.”
  4. “One incentive shared across all actors can initiate change.”
  5. “We can create change in AI design and deployment through procurement alone.” 

Each narrative trap is presented with ways and strategies to avoid said trap.

The report 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.

If you are interested in joining a network of professionals who are interested in sharing insight and experience on AI procurement on an ongoing basis, you can sign up here!

We extend our sincere gratitude to the roundtable participants for bringing their expertise and critical voice to the table. We are also grateful for the funding that we have received for this project from the Northeast Big Data Innovation Hub Seed Fund Program of 2020. 

Final Project DOI: AI and Procurement: A Primer

Mona Sloane is a sociologist working on inequality in the context of AI design and policy. She frequently publishes and speaks about AI, ethics, equitability and policy in a global context. Mona is a Fellow with NYU’s Institute for Public Knowledge (IPK), where she convenes the Co-Opting AI series and co-curates the The Shift series. She also is an Adjunct Professor in the Department of Technology, Culture and Society at NYU’s Tandon School of Engineering, a Senior Research Scientist at the NYU Center for Responsible AI, and is part of the inaugural cohort of the Future Imagination Collaboratory (FIC) Fellows at NYU’s Tisch School of the Arts.

Mona is also affiliated with The GovLab in New York and works with Public Books as the editor of the Technology section. Her most recent project is Terra Incognita: Mapping NYC’s New Digital Public Spaces in the COVID-19 Outbreak which she leads as principal investigator. Mona currently also serves as principal investigator of the Procurement Roundtables project, a collaboration with Dr. Rumman Chowdhury (Director of Machine Learning Ethics, Transparency & Accountability at Twitter, Founder of Parity), and John C. Havens (IEEE Standards Association) that is focused on innovating AI procurement to center equity and justice. Mona also works with Emmy Award-winning journalist and NYU journalism professor Hilke Schellmann on hiring algorithms, auditing, and new tools for investigative journalism and research on AI. With Dr. Matt Statler (NYU Stern), Mona is also leading the PIT-UN Career Fair project that looks to bring together students and organizations building up the public interest technology space. Mona is also affiliated with the Tübingen AI Center in Germany where she leads a 3-year federally funded research project on the operationalization of ethics in German AI startups. She holds a PhD from the London School of Economics and Political Science and has completed fellowships at the University of California, Berkeley, and at the University of Cape Town.