Technology landscape
How reported AI classifications spread across agencies, missions, lifecycle stages and sourcing. Each record carries one reported classification, so categories do not overlap. A classification describes the technique, not a specific commercial model.
Lifecycle by technology
Share of each technology's recordsSourcing by technology
Where reportedDeveloped in-house: 3 (15.0%)Generative AI
Contract and in-house: 4 (20.0%)Generative AI
Purchased from vendor: 13 (65.0%)
Contract and in-house: 3 (42.9%)Classical / predictive ML
Purchased from vendor: 4 (57.1%)
Developed in-house: 3 (12.0%)Natural language processing
Contract and in-house: 9 (36.0%)Natural language processing
Purchased from vendor: 13 (52.0%)
Technology adoption by agency
Row share: each agency's records by classification · 16 largest portfolios in the filterTechnology by topic area
Row share within each topic| Generative AI | Classical / predictive ML | Natural language processing | Computer vision | Reinforcement learning | Other | n | |
|---|---|---|---|---|---|---|---|
| Law Enforcement | 20% | 4% | 56% | 20% | 25 | ||
| Information Technology | 50% | 10% | 30% | 10% | 10 | ||
| Administrative Functions | 38% | 25% | 25% | 13% | 8 | ||
| Other | 67% | 17% | 17% | 6 | |||
| Health and Medical | 25% | 25% | 50% | 4 | |||
| Service Delivery | 67% | 33% | 3 | ||||
| Government Benefits Processing | 100% | 1 | |||||
| International Affairs | 100% | 1 | |||||
| Science | 100% | 1 | |||||
| Transportation | 100% | 1 |
High-impact designation by technology
High-impact: 1 (5.0%)Generative AI
Presumed high-impact, determined not: 1 (5.0%)Generative AI
Not high-impact: 18 (90.0%)
Presumed high-impact, determined not: 2 (28.6%)Classical / predictive ML
Not high-impact: 5 (71.4%)
Governance disclosure by technology
High-impact records: reported in place / scope| Technology | High-impact | Testing | Impact assess. | Indep. review | Monitoring | Training |
|---|---|---|---|---|---|---|
| Generative AI | 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 | 0 / 1 |
| Classical / predictive ML | 0 | – | – | – | – | – |
| Natural language processing | 9 | 2 / 9 | 0 / 9 | 0 / 9 | 0 / 9 | 1 / 9 |
| Computer vision | 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 | 0 / 3 |
| Reinforcement learning | 0 | – | – | – | – | – |
| Other | 0 | – | – | – | – | – |
Shows counts reported in place against all high-impact records of that technology. The remainder includes in-progress, not applicable and unanswered. Full governance view
Systems, vendors and technology are different things
43 of 60 records name a vendor (72%)Vendor: the supplier the agency names in a free-text field. Matched to a standard list of companies; one record may name several.
System name: the agency's name for the system (reported for 75% of records).
AI classification: the technique category the agency selected. A vendor name does not show which model or technique is used, and this platform does not infer it.