Innovation Management¶

January 10, 2024
in Government Technology, AI Strategy, Innovation Management
6 min read

Synthetic Data Is a Claim About the World

Synthetic data is frequently presented as a practical escape from real-data constraints. When records are sensitive, scarce, imbalanced, expensive, or difficult to share, an organization can generate artificial observations that resemble the original population and continue developing models, testing systems, or publishing evidence.

That description is attractive and incomplete.

A synthetic dataset is not simply “fake data.” It is the output of a model that claims to preserve some properties of the world while suppressing or altering others. Its value depends on which properties survive, for which population, under which intended use, and with what privacy and disclosure risk.

The Federal Chief Data Officers Council’s 2024 request for information was therefore right to ask not only about applications, but about definitions, limitations, ethics, equity, and evidence. Federal synthetic-data practice should begin with a disciplined principle: every synthetic dataset is a bounded, testable claim.

January 9, 2024
in AI Strategy, Government Technology, Innovation Management
6 min read

Responsible Technology Begins Before the System Exists

Responsible technology is often implemented as a review: a team builds a system, specialists evaluate its risks, and leaders decide whether the remaining concerns are acceptable. That process may catch important problems, but it arrives after many consequential choices have already hardened into architecture, data, contracts, incentives, and user expectations.

The National Science Foundation’s Responsible Design, Development, and Deployment of Technologies (ReDDDoT) program points toward a stronger model. It treats ethical, legal, community, and societal considerations as inputs throughout the technology lifecycle rather than as constraints applied to a nearly finished product.

The central insight is simple: responsibility is most powerful when it changes what gets built.

January 9, 2024
in Government Technology, AI Strategy, Innovation Management
6 min read

ReDDDoT and the Missing Infrastructure of Responsible Innovation

Funding responsible-technology research is necessary. Funding isolated projects is not sufficient.

The more consequential opportunity in the National Science Foundation’s Responsible Design, Development, and Deployment of Technologies (ReDDDoT) program is to build the infrastructure of a field: people, methods, shared evidence, institutional arrangements, and translation pathways that make responsible practice repeatable beyond a single grant.

This distinction matters because the market systematically underproduces that infrastructure. Companies can capture value from products; they struggle to capture the full value of public test methods, community capacity, interoperable documentation, negative results, and long-term study of societal effects. Government and philanthropy can invest where commercial incentives are weak—but only if they treat the portfolio as more than a collection of worthy projects.

January 9, 2024
in AI Strategy, Defense Technology, Government Technology, Innovation Management
7 min read

Responsible Military AI Norms Are an Interoperability Layer

International norms for military artificial intelligence are often discussed as constraints: ethical boundaries intended to prevent unsafe, unlawful, or destabilizing uses of emerging technology. That is an essential function, but it is not the only one.

For allies and partners, shared norms can also operate as an interoperability layer. They establish the minimum assumptions under which states can exchange data, evaluate one another’s systems, coordinate human and machine roles, investigate failures, and employ AI-enabled capabilities without introducing unacceptable uncertainty into combined operations.

This is the underappreciated strategic value of the United States’ effort to build international cooperation around responsible military AI and autonomy. Principles do not become operational merely because many states endorse them. But when principles are translated into compatible engineering evidence, command practices, and assurance processes, responsibility and coalition effectiveness begin to reinforce one another.

January 9, 2024
in AI Strategy, Government Technology, Innovation Management
6 min read

AI Safety Needs Public Measurement Infrastructure

The early debate over funding the U.S. Artificial Intelligence Safety Institute could be read as an ordinary appropriations story: lawmakers sought initial resources so NIST could recruit specialists, convene a consortium, and begin work on AI evaluation and safety standards.

The more consequential question is what kind of institution the country expects to build.

An AI Safety Institute should not be a policy office that comments on company evaluations, nor a consortium whose consensus is defined by its largest members. It should function as public measurement infrastructure: an institution capable of developing independent methods, testing important claims, making results reproducible, and creating a shared technical basis for decisions that markets cannot supply on their own.

January 9, 2024
in AI Strategy, Defense Technology, Innovation Management
7 min read

The Missing Middle Between an AI Experiment and a Mission Capability

The Department of Defense has become increasingly proficient at demonstrating advanced technology. The harder achievement is converting a promising result into a capability on which operators can depend.

The Global Information Dominance Experiments (GIDE) illuminate both sides of that problem. They provide recurring opportunities to integrate data, software, analytics, and artificial intelligence across services, combatant commands, the Joint Staff, and international partners. Yet the strategic value of GIDE will not be determined by what works during an experiment. It will be determined by what survives after the experiment’s temporary concentration of people, access, infrastructure, and senior attention dissolves.

The missing middle is transition: the set of technical, organizational, contractual, financial, security, and operational mechanisms that turn evidence into durable capability.

January 9, 2024
in Government Technology, AI Strategy, Defense Technology, Innovation Management
3 min read

January 9, 2024
in AI Strategy, Defense Technology, Innovation Management
8 min read

Human–AI Teaming Requires a Science of the Team

The phrase human–machine teaming is used so frequently that it can obscure how little it explains. Placing an AI system in a workflow with a human does not create a team. Nor does assigning the human final authority guarantee meaningful control. A team exists only when participants have interdependent roles, exchange information, adapt to one another, and coordinate their actions toward a shared outcome.

That is why DARPA’s work on quantitative models of human–AI teams is more important than the original 2024 solicitation cycle that prompted this essay. The enduring research problem is not simply whether an AI model performs well or whether a person approves its output. It is whether the combined system behaves competently, legibly, and safely under realistic conditions—including conditions neither participant encountered during development.