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Ramayya Krishnan

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IS Journal 2025 Journal Article

From Policy to Practice: Research Directions for Trustworthy and Responsible Artificial Intelligence “By Design”

Ramayya Krishnan
John P. Lalor
Nicolas Prat
Ahmed Abbasi

Rapid advancements in the development and adoption of artificial intelligence (AI) have accelerated the need for trustworthy and responsible AI (TRAI). National/international AI governance and risk management policies and frameworks have identified a core set of tenets for TRAI, including fairness, safety, privacy, security, transparency, explainability, and responsible deployment. Responsible AI processes/tools (RAPs) are solutions designed to operationalize and implement the tenets, serving as a middle layer between the tenets and real-world AI-embedded processes. In recent years, the design of RAPs has emerged as an important avenue for computational and social science researchers, practitioners, and policymakers. We highlight six important research directions for the design of RAPs. Using a real-world case study, we describe the importance of each research direction and illustrate current challenges.

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AAAI Conference 2012 Conference Paper

An Efficient Simulation-Based Approach to Ambulance Fleet Allocation and Dynamic Redeployment

Yisong Yue
Lavanya Marla
Ramayya Krishnan

We present an efficient approach to ambulance fleet allocation and dynamic redeployment, where the goal is to position an entire fleet of ambulances to base locations to maximize the service level (or utility) of the Emergency Medical Services (EMS) system. We take a simulation-based approach, where the utility of an allocation is measured by directly simulating emergency requests. In both the static and dynamic settings, this modeling approach leads to an exponentially large action space (with respect to the number of ambulances). Futhermore, the utility of any particular allocation can only be measured via a seemingly “black box” simulator. Despite this complexity, we show that embedding our simulator within a simple and efficient greedy allocation algorithm produces good solutions. We derive data-driven performance guarantees which yield small optimality gap. Given its efficiency, we can repeatedly employ this approach in real-time for dynamic repositioning. We conduct simulation experiments based on real usage data of an EMS system from a large Asian city, and demonstrate significant improvement in the system’s service levels using static allocations and redeployment policies discovered by our approach.

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