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Abstract AI fairness is tasked with evaluating and mitigating bias in algorithms that may discriminate towards protected groups. This paper examines if bias exists in AI algorithms used in disaster management and in what manner. We consider the 2017 Hurricane Harvey when flood victims in Houston resorted to social media to request for rescue. We evaluate a Random Forest regression model trained to predict Twitter rescue request rates from social-environmental data using three fairness criteria (independence, separation, and sufficiency). The Social Vulnerability Index (SVI), its four sub-indices, and four variables representing digital divide were considered sensitive attributes. The Random Forest regression model extracted seven significant predictors of rescue request rates, and from high to low importance they were percent of renter occupied housing units, percent of roads in flood zone, percent of flood zone area, percent of wetland cover, percent of herbaceous, forested and shrub cover, mean elevation, and percent of households with no computer or device. Partial Dependence plots of rescue request rates against each of the seven predictors show the non-linear nature of their relationships. Results of the fairness evaluation of the Random Forest model using the three criteria show no obvious biases for the nine sensitive attributes, except that a minor imperfect sufficiency was found with the SVI Housing and Transportation sub-index. Future AI modeling in disaster research could apply the same methodology used in this paper to evaluate fairness and help reduce unfair resource allocation and other social and geographical disparities. 

The emergence of soft robots has presented new challenges associated with controlling the underlying fluidics of such systems. Here, we introduce a strategy for additively manufacturing unified soft robots comprising fully integrated fluidic circuitry in a single print run via PolyJet three-dimensional (3D) printing. We explore the efficacy of this approach for soft robots designed to leverage novel 3D fluidic circuit elements—e.g., fluidic diodes, “normally closed” transistors, and “normally open” transistors with geometrically tunable pressure-gain functionalities—to operate in response to fluidic analogs of conventional electronic signals, including constant-flow [“direct current (DC)”], “alternating current (AC)”–inspired, and preprogrammed aperiodic (“variable current”) input conditions. By enabling fully integrated soft robotic entities (composed of soft actuators, fluidic circuitry, and body features) to be rapidly disseminated, modified on demand, and 3D-printed in a single run, the presented design and additive manufacturing strategy offers unique promise to catalyze new classes of soft robots.