Pilots can serve as a critical resource for agency insiders as they begin to deploy artificial intelligence, and considerations around built-in bias should be addressed from the earliest of efforts, federal officials with firsthand experience leveraging the technology said in Washington Tuesday. "One beauty of the cloud is you can do a pilot, and if it fails just turn it off--it's not like buying millions in servers and then find that you are going to go in a different direction," Small Business Administration Deputy Chief Information Officer Guy Cavallo said at Nextgov's Tech Talks. "We have actually done three 90-day sprint pilots that have revolutionized the way we work." Cavallo explained how SBA effectively implemented artificial intelligence and the cloud to enhance their cybersecurity capabilities and supplement what human employees are able to do. Elaborating on the success he's seen through various piloting projects related to that work, Cavallo said the agency participated in one with General Services Administration and Homeland Security Department for the Trusted Internet Connections initiative.

We join the goals of two giant and related fields of research in group decision-making that have historically had little contact: fair division, and efficient mechanism design with monetary payments. To do this we adopt the standard mechanism design paradigm where utility is assumed to be quasilinear and thus transferable across agents. We generalize the traditional binary criteria of envy-freeness, proportionality, and efficiency (welfare) to measures of degree that range between 0 and 1. We demonstrate that in the canonical fair division settings under any allocatively-efficient mechanism the worst-case welfare rate is 0 and disproportionality rate is 1; in other words, the worst-case results are as bad as possible. This strongly motivates an average-case analysis. We then set as the goal identification of a mechanism that achieves high welfare, low envy, and low disproportionality in expectation across a spectrum of fair division settings. We establish that the VCG mechanism is not a satisfactory candidate, but the redistribution mechanism of [Bailey, 1997; Cavallo, 2006] is.

We present the design and analysis of the first fully expressive, iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language (TBBL) that is concise and expressive for CEs. Bidders specify lower and upper bounds in TBBL on their value for different trades and refine these bounds across rounds. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bid-trees and to avoid enumerating trades. A proxied interpretation of a revealed-preference activity rule, coupled with simple linear prices, ensures progress across rounds. The exchange is fully implemented, and we give results demonstrating several aspects of its scalability and economic properties with simulated bidding strategies.