Behavioral Models, Methods & Metrics for Trust Establishment, Maintenance, and Repair in Human-Machine Co-Training Pitch Day for Trusted AI

TECH FOCUS AREAS: Autonomy; Artificial Intelligence/Machine Learning TECHNOLOGY AREAS: Information Systems; Battlespace OBJECTIVE: The objective of this topic is to explore the development of simultaneously training humans and machine partners to facilitate the establishment of trust, its maintenance, and its repair when failures occur. In particular, the proposed solutions should present some examples of failures and successes in human-machine trust through co-training. This topic will reach companies that can complete a feasibility study and prototype validated concepts in accelerated Phase II schedules. This topic is specifically aimed at development beyond basic science and research. DESCRIPTION: Warfighters will continue to be partnered with increasingly capable intelligent systems to outwit and outpace adversaries. Rather than have warfighters and their autonomous partners independently acquire their needed skills and then come together to accomplish a mission, they will train together over time with increasingly complexity to emerge as tightly coupled cooperative agents with developed bi-directional human-machine trust. In this example, co-training involves partnering with an intelligent system throughout the individual and team skill acquisition processes, exposing team members to each other's strengths, weaknesses, communication styles, and how to maintain trust and repair it when needed. To achieve this goal, developers must provide intelligent systems with some human-like cognitive capacities to facilitate the dynamic processes associated with the establishment, maintenance, and repair of trust. Example human-level capacities include acquiring mutual knowledge, beliefs, and assumptions to facilitate efficient communication (i.e., common ground), skill acquisition at the human-partner's pace, and human-level situation representations to facilitate interaction, team problem solving, responsibility shifting and sharing, et cetera. To achieve these goals, several facets of this emphasis need to be tackled (in no particular order): Behavioral/cognitive model development and evaluation of the required human-level capacities theorized to be important in trust establishment, maintenance, and repair; Methods developed and tested for co-training between humans and their autonomous counterparts to demonstrate the establishment of bi-directional trust and performance benefits; Metrics (objective and subjective) must be developed and validated to capture the dynamics of trust establishment, maintenance, and repair to better assess trust's relationship to, and influence on, task performance. PHASE I: Phase I should completely document 1) the AI-driven explainability requirements the proposed solution addresses; 2) the approach to model, quantify and analyze the representation, effectiveness, and efficiency of the explainable decision-making solution; and 3) the feasibility of developing or simulating a prototype architecture. PHASE II: Develop and demonstrate a prototype system determined to be the most feasible solution during the Phase I feasibility study. This demonstration should focus specifically on: 1. Evaluating the proposed solution against the objectives and measurable key results as defined in the Phase I feasibility study. 2. Describing in detail how the solution can be scaled to be adopted widely (i.e. how can it be modified for scale). 3. A clear transition path for the proposed solution that takes into account input from all affected stakeholders including but not limited to: end users, engineering, sustainment, contracting, finance, legal, and cyber security. 4. Specific details about how the solution can integrate with other current and potential future solutions. 5. How the solution can be sustainable (i.e. supportability). 6. Clearly identify other specific DoD or governmental customers who want to use the solution. PHASE III DUAL USE APPLICATIONS: The contractor will pursue commercialization of the various technologies developed in Phase II for transitioning expanded mission capability to a broad range of potential government and civilian users and alternate mission applications. Direct access with end users and government customers will be provided with opportunities to receive Phase III awards for providing the government additional research & development, or direct procurement of products and services developed in coordination with the program. PROPOSAL PREPARATION AND EVALUATION: Please follow the Air Force-specific Direct to Phase II instructions under the Department of Defense 21.2 SBIR Broad Agency Announcement when preparing proposals. Proposals under this topic will have a maximum value of $1,500,000 SBIR funding and a maximum performance period of 18 months, including 15 months technical performance and three months for reporting. Phase II proposals will be evaluated using a two-step process. After proposal receipt, an initial evaluation will be conducted IAW the criteria DoD 21.2 SBIR BAA, Sections 6.0 and 7.4. Based on the results of that evaluation, Selectable companies will be provided an opportunity to participate in the Air Force Trusted AI Pitch Day, tentatively scheduled for 26-30 July 2021 (possibly virtual). Companies' pitches will be evaluated using the initial proposal evaluation criteria. Selectees will be notified after the event via email. Companies must participate in the pitch event to be considered for award. REFERENCES: 1. Kirk, J. R., & Laird, J. E. (2016). Learning General and Efficient Representations of Novel Games Through Interactive Instruction. Advances in Cognitive Systems, 4 2. Laird, J. E., Anderson, J., Forbus, K. D., Lebiere, C., Salvucci, D., Scheutz, M., ... Kirk, J. R. (2017). Interactive Task Learning. IEEE Intelligent Systems, 32(4), 6-21 (invited). https://doi.org/10.1109/MIS.2017.3121552 3. van den Bosch, K., Schoonderwoerd, T., Blankendaal, R., & Neerincx, M. (2019). Six Challenges for Human-AI Co-learning. In International Conferenceon Human-Computer Interaction (pp. 572-589). Springer, Cham.