SBIR/STTR Research Award
Explainable Machine Learning: Training and Trust
Award amount
$150,000Program and phase
SBIR · Phase IAward year
2017Award details
- Awarding agency
- Department of Defense
- Branch
- Defense Advanced Research Projects Agency
- Recipient company
- INFERLINK CORP
- Proposal award date
- 2017-02-27
- Contract end date
- 2017-11-26
- UEI
- M8KCZYW54M17
- Solicitation number
- 2016.3
- Topic code
- SB163-007
Research abstract
Machine learning algorithms can learn large, complex models for a wide variety of applications. These models often perform well, but practitioners often find them mysterious. Not only does this create questions for end-users, who may not know whether to...
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- GENESIS: Generative AI for Educational Systems and Instructional Support
- Graph Reliability and Integrity Management System (GRIMS)
- Adaptive Translation Engine for Low Resource Language
- A Next Generation Publication Platform for Open Science
- Using Generative LLMs to Extract Pathways from the Scientific Literature
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
- Scalable Language Association Models for Domain-specific Utterances in Novel Contexts (SLAMDUNK)
- Explainable AI to support Veterans Transition Assistance Programs (XAI-VTAP)
- Deep Explainer of Neural Networks
- An Interpretation Framework for Explanation of Machine Learning Model Decisions
- X-GRID: Learning Explainable Recommendations and Taxonomical Grids for Resource Allocation
Research funding history
48 SBIR/STTR records share this UEI (2012–2026). Reported award amounts total $24,575,096. Reported amounts are not federal obligations.
Phases: Phase I, Phase II, Phase II – Direct to Phase II
Agencies: Department of Defense, Department of Homeland Security, National Science Foundation, Department of Commerce, Environmental Protection Agency, Department of Health and Human Services