Innovation Management¶

January 11, 2024
in AI Strategy, Government Technology, Innovation Management
7 min read

Civil-Rights Risk Lives in the Decision System, Not Only the Model

The Department of Justice’s January 2024 interagency convening on artificial intelligence and civil rights reflected an important federal position: existing civil-rights and consumer-protection laws continue to apply when decisions are mediated by algorithms.

That proposition is necessary. Its technical consequence deserves equal emphasis.

Discrimination rarely resides in a single model parameter or fairness metric. It can enter through the choice of problem, the collection of data, a proxy variable, the design of an interface, unequal access to a digital service, the discretion granted to staff, or the absence of a practical appeal. An AI system can satisfy a narrow statistical test and still participate in an unlawful or inequitable decision process.

Civil-rights analysis must therefore treat the whole decision system as the unit of accountability.

January 11, 2024
in AI Strategy, Defense Technology, Government Technology, Innovation Management
2 min read

Early 2024: Senate Commerce Chair Cantwell to Introduce Comprehensive AI Legislation

A New Stance on AI: Insights into Senate Commerce Chair Cantwell’s Upcoming Legislation

The path to a comprehensive artificial intelligence (AI) legislation is set to take a significant turn, thanks to initiatives by Senate Commerce Committee Chair, Maria Cantwell. According to sources cited by FedScoop, Cantwell, a Democrat from Washington, is expected to introduce an array of bipartisan bills tackling various facets of AI.

January 11, 2024
in AI Strategy, Defense Technology, Government Technology, Innovation Management
2 min read

Reevaluating Strategies: Navy’s 4th Fleet Highlights the Need for Command Center Upgrades for Unmanned Operations

Unmanned systems command has certainly been a focal point for the Navy’s 4th Fleet, but as these technologies advance, the challenge of managing them effectively grows. Rear Adm. Jim Aiken recently shared insights into the progress and pitfalls experienced in combatting this issue.

January 11, 2024
in Government Technology, Defense Technology, Innovation Management
6 min read

A Data Standard Is an Institutional Contract

The Department of Defense’s release of Assistance Data Standard Version 1.0 sounded like a specialized acquisition-system update. It was more consequential than that.

Contracts, grants, cooperative agreements, research transactions, and prototype agreements encode how the Department allocates resources, structures incentives, shares risk, and moves technology toward mission use. When those instruments are represented inconsistently across systems, the Department loses the ability to see its own portfolio, compare outcomes, trace modifications, and learn which acquisition mechanisms work.

A data standard is therefore not merely a technical schema. It is an institutional contract about meaning.

January 11, 2024
in AI Strategy, Government Technology, Innovation Management
6 min read

The Best Government AI Service May Prevent the Contact

Artificial intelligence can translate documents, summarize case histories, route requests, help employees search policy, and provide conversational assistance outside business hours. Those are useful capabilities. They can also make a broken service easier to enter without making it easier to complete.

For public services, the objective should not be a more sophisticated chatbot or a lower call-handle time. It should be a reduction in the effort people must spend obtaining an accurate, lawful, and timely outcome.

The highest-value AI intervention may be the one that prevents the person from needing to contact the agency at all.

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

AI at the Spectrum Edge: The Real Lesson of JCREW

The Navy’s Joint Counter Radio-Controlled Improvised Explosive Device Electronic Warfare system is a useful case study in applied artificial intelligence because it places computation where ambiguity, latency, and consequence converge: the electromagnetic spectrum at the tactical edge.

The practical problem is not simply detecting a signal or generating more jamming power. A protection system must determine which signals matter, allocate constrained sensing and electronic-attack resources, respond quickly enough to defeat a threat, and avoid unnecessarily disrupting friendly communications. The environment changes continuously, and an adversary is actively attempting to deceive or overwhelm it.

AI’s value in this setting is best understood as attention and resource orchestration under uncertainty.

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

AUKUS Needs an Alliance-Native AI Engineering System

AUKUS Pillar II is often described as a portfolio of advanced technologies: artificial intelligence, autonomy, cyber, electronic warfare, quantum technologies, hypersonics, and undersea capabilities. The list is impressive, but the strategic challenge is not to advance each technology independently. It is to make three sovereign defense enterprises capable of learning and operating together at software speed.

AI makes that requirement unusually demanding. A model cannot be separated from the data, software, compute, mission context, human decision process, and evaluation evidence around it. An algorithm that performs well on one nation’s platform may fail when paired with another nation’s sensors, communications, doctrine, or operating environment.

The decisive AUKUS capability is therefore not a shared model. It is an alliance-native AI engineering system through which data, models, platforms, tests, and operational lessons can be combined without erasing national responsibility.

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

Federal AI Talent Is a Capability System, Not a Hiring Surge

Calls for the federal government to recruit more artificial-intelligence talent usually begin with a real constraint: agencies cannot design, evaluate, acquire, and govern consequential AI systems without people who understand the technology deeply enough to exercise independent judgment.

The common response is a hiring surge. Direct-hire authority, pooled certificates, fellowships, special salary rates, and public-service recruiting can all help. They do not solve the problem if highly capable people enter organizations that lack product ownership, modern engineering environments, technical career paths, or durable funding.

Federal AI talent should be treated as a capability system: a set of mechanisms for obtaining expertise, organizing it around mission outcomes, developing it over time, and giving it sufficient authority to change how the institution works.