A Call to Action: The Future of Artificial Intelligence and Public Service

This call to action is based in part on a recent National Academy of Public Administration Standing Panel discussion of Technology Leadership for Generative Artificial Intelligence. New and disruptively more powerful AI growth and maturity requires new approaches for educating, convening, and engaging the public, policymakers, and government leaders in "co-creating the future." We must recognize and respond to this fundamentally new world of digital capabilities and impacts.

This blog post is the first of several planned for 2023-2024 on the "Future of Artificial Intelligence and Public Service." We plan to explore the following:

1. AI Services to Citizens
2. AI-Driven Decision Making
3. Ethical Considerations and AI Governance
4. Generative AI in Government Communications
5. AI, Chatbots, Comments, and Public Rulemaking
6. AI-Enabled Cybersecurity
7. AI Technologies and the Elections

Our history of active civil discourse, pluralistic participation, and complex mixing of activities by government and the private sector (including for-profit businesses, non-profit organizations, and educational institutions) makes these issues both essential and quite challenging for open societies like ours in the United States. Complex governmental systems can reduce impulsive mistakes, but they can also make it harder to make significant changes quickly. Forewarned of coming challenges, we can be better prepared for what needs to be done.

A Call to Action

Generative AI is pushing us rapidly to a new digital reality. The future of Artificial Intelligence and public administration cannot simply be relegated to the "technology staff" for resolution because the power and pervasiveness of new technologies affect every function of government at every level. Unfortunately, the kind of public attention and participation we need to keep up with the nature and pace of change is hard to come by.

Until about 2010, public service digital technologies focused on electronic communications and the automation of many accounting, data management, and record-keeping tasks. Improved decision-making analytics became a key goal for some parts of government, especially in the national security domain.

During this era, success depended on technologies for computer-oriented organizations and specialists; for programming languages, databases, and networking; for standards supporting economies of scale; and -- most importantly – for a strong and continuing growth in technology productivity. With processor productivity doubling every two years under Moore's Law, computing productivity rose a million times between 1970 to 2010. While this was amazing, public service digital technologies focused primarily on bureaucratic record-keeping and communications.

However, from roughly 2010-2020, a critical new phase began. Digital programs with feedback loops that could do human-like activities on the web – in speed and volumes far greater than human capacity– began to impact government services. Computers learned to understand not only numbers and text but highly complex data patterns.

Computers learned to understand spoken words. They could speak words that were written. They could translate spoken words reasonably well from one language to another. They became the world's best players of chess and go. They learned to identify people from pictures. They made impressive progress with autonomous driving. By 2020, computers had improved enough to handle a rapidly growing number of tasks that had previously required people.

Where will we be by 2030?

As we look to the future, it is essential to consider and decide where we want generative AI -- the cutting edge of digital analysis and innovation – to take us.

In the broad context, computers by 2030 should be a billion times more cost-effective than in 1970. As many as half of our current jobs will change enough to require new work skills. The impacts will be disruptively, not just incrementally, transformational. In response, we must shift from our old ways of managing IT development, value chains, change management, and governance.

For our call to action, we seek informed stakeholder engagement for service "co-creation" across multiple backgrounds and disciplines. If future solutions are to be successful and sustainable, we will need both executive and legislative expertise; members of private industry, academia, and non-profits; and participants from the public willing to share insights and perspectives.

To improve public services and the nation overall, decisions using AI must be made "of the people, by the people, and for the people." Effective decisions will require strong and adaptable partnerships across the sectors. We must recognize we cannot rely so narrowly on our "technology problem" lenses to solve what are now more significant questions of public service transformation. Specifically, we must:

1. Move beyond viewing emerging technologies as "just" technology problems. They pose critical and more profound questions requiring organizational adaptation and societal transformation.
2. Avoid delegating the coming AI challenges to technologists alone. Solutions will require deeper engagement with multiple disciplines both inside and outside of public service.
3. Leverage the mindset of "get there fast" (mainly from business) with "get there safely" (primarily due to the public values at risk). The public needs the transformational power of AI to meet the best combination of public and private needs.

Read our next blog in the series, Artificial Intelligence and Public Service: Key New Challenges.

If you are interested in this journey, please sign up on our mailing list. Together, we can clarify issues and build support for actionable AI. Through education, convenings, and focus, we can avoid the risks and harvest the returns as we co-create the future of AI and public service.

Onwards and upwards, together.

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Watch the Video Recording of the May 4th Standing Panel: Generative AI and Time for a Time Out?

Alan R. Shark, Co-Chair, NAPA Standing Panel on Technology Leadership; Associate Professor, Schar School of Policy and Government, George Mason University; Executive Director for Public Technology Institute; NAPA Fellow

Theresa Pardo, Co-Chair, NAPA Standing Panel on Technology Leadership; Associate Vice President for Research, University of Albany and Special Assistant to University President, and full professor; NAPA Fellow

Alan P. Balutis, Managing Partner, The CIO Collective; NAPA Fellow

David Bray, Distinguished Fellow, Henry L. Stimson Center & Business Executives for National Security; Co-Chair, Loomis Council; Adjunct Professor, Carnegie Mellon University; Senior Fellow, Institute for Human-Machine Cognition

Dan Chenok, Executive Director, IBM Center for The Business of Government; NAPA Fellow

Jerry Mechling, former Fellow of the Institute of Politics; Lecturer in Public Policy, Harvard Kennedy School of Government; Founder/Director of the Program on Strategic Computing in the Public Sector; NAPA Fellow