A sizable proportion of deployed machine learning models make their decisions in a black-box manner. Such decision-making procedures are susceptible to intrinsic biases, which has led to a call for accountability in deployed decision systems. In this work, we investigate mechanisms that help audit claimed mathematical guarantees of the fairness of such systems. We construct AVOIR, a system that reduces the number of observations required for the runtime monitoring of probabilistic assertions over fairness metrics specified on decision functions associated with black-box AI models. AVOIR provides an adaptive process that automates the inference of probabilistic guarantees associated with estimating a wide range of fairness metrics. In addition, AVOIR enables the exploration of fairness violations aligned with governance and regulatory requirements. We conduct case studies with fairness metrics on three different datasets and demonstrate how AVOIR can help detect and localize fairness violations and ameliorate the issues with faulty fairness metric design.

Digital agriculture, hailed as the fourth great agricultural revolution, employs software-driven autonomous agents for in-field crop management. Edge computing resources deployed near crop fields support autonomous agents with substantial computational needs for tasks such as AI inference. In large fields, using multiple autonomous agents, called swarms, can speed up crop management tasks if sufficient edge resources are provisioned. However, to use swarms today, farmers and software developers craft their own standalone solutions that are either simple and ineffective or complicated and hard-to-reproduce. We present MARbLE, a platform for developing and managing swarms. MARbLE provides an easy-to-use programming paradigm that helps users build swarm workloads using multi-agent reinforcement learning. Developers supply just two functions Map() and Eval(). The platform automatically compiles and deploys swarms and continuously updates the reinforcement learning models that govern their actions. Developers can experiment with multiple swarm and edge resource configurations both in simulation and with actual in-field runs. We studied real UAV swarms conducting digital agriculture missions. We observe that swarms demanded edge computing resources in bursts; the ratio of average to peak demand was 2.9X. MARbLE uses energy-saving load balancing policies to duty cycle machines during workload demand troughs, leveraging workload patterns to save edge energy. Using MARbLE, we found that four-agent swarms with load balancing techniques sped up missions by 2.1X and reduced edge energy usage by up to 2X compared to state of the art autonomous swarms.