SOAR TECHNOLOGY, LLC — Department of Defense SBIR Phase II: DHA17B-002
SOAR TECHNOLOGY, LLC — SBIR Phase II award from Department of Defense.
Phase II SBIR prototype / development signal
- Phase II is where Department of Defense funds deeper R&D after feasibility. Incumbents with Phase II history are serious competitors on adjacent topics.
- Use this award as past-performance context and to map customer organizations for STRATFI/TACFI-style transition planning.
- At $2,985,840, this is a large obligation for typical SBIR Phase sizing — worth reviewing for scope breadth and teaming opportunity.
- Topic code DHA17B-002 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $2,985,840
- Agency
- Department of Defense · Defense Advanced Research Projects Agency
- Program / Phase
- SBIR · Phase II
- Topic
- DHA17B-002
- Solicitation
- 17.B
- NAICS
- —
- Place of performance
- MI
- Period
- 2023-06-07 → 2026-07-08
Description
Current artificial intelligence (AI) algorithms are built to align with ground truth or consensus of trusted human decision-makers. This is not effective for difficult decision domains, such as patient triage, where even experts disagree on the best course of action. A key reason human experts disagree is that their individual attributes influence their decision-making. To develop human-off-the-loop triage systems that are trusted by humans, understanding and alignment with these attributes is critical. This will enable effective communication of system decisions, based on these attributes may further support trust in the system. During this effort, the team will use proven interview-based approaches and machine learning tools to identify attributes that predict communication and decision-making styles used during high-stakes situations such as patient triage, and will develop tools that give insights into alignment scores and communicate AI-based decisions to human users. This effort will produce five unique products: 1) KDMAs / KCAs grounded in data gathered from SME interviews and identified using machine learning models; 2) triage scenarios that elicit decisions and corresponding attributes; 3) a model for cue utilization that will inform scenarios; 4) visualizations to give insight into human-AI attribute alignment; 5) tools to help communicate AI-based decisions to humans.