Nandhini Swaminathan

Nandhini Swaminathan, David Danks

As quantum machine learning (QML) emerges as a promising field at the intersection of quantum computing and artificial intelligence, it becomes crucial to address the biases and challenges that arise from the unique nature of quantum systems. This research includes work on identification, diagnosis, and response to biases in Quantum Machine Learning. This paper aims to provide an overview of three key topics: How does bias unique to Quantum Machine Learning look? Why and how can it occur? What can and should be done about it?

Carter Buckner, Jennifer Mickel, Nandhini Swaminathan et al.

Public policies are being developed around the world to address privacy, economic, intellectual property, energy, and other risks that AI technologies pose. Involvement from the general public is essential to governance as an accountability and alignment mechanism. However, participating in and impacting policymaking can be challenging for sections of the public that lack extensive networks, lobbying capabilities, and other forms of power. This challenge is especially acute for marginalized communities. In this paper, we present a case study of our organization's efforts to bring participatory design (PD) principles to AI policymaking in the US. We describe our engagements with several US policy bodies, and our participatory development of AI policy for queer people. We highlight challenges with PD practice with marginalized communities, and offer suggestions to alleviate them. We conclude with actionable recommendations for policymakers and other organizers working in marginalized communities.

Nandhini Swaminathan, David Danks

This study offers an in-depth analysis of the application and implications of the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) within the domain of surveillance technologies, particularly facial recognition technology. Given the inherently high-risk and consequential nature of facial recognition systems, our research emphasizes the critical need for a structured approach to risk management in this sector. The paper presents a detailed case study demonstrating the utility of the NIST AI RMF in identifying and mitigating risks that might otherwise remain unnoticed in these technologies. Our primary objective is to develop a comprehensive risk management strategy that advances the practice of responsible AI utilization in feasible, scalable ways. We propose a six-step process tailored to the specific challenges of surveillance technology that aims to produce a more systematic and effective risk management practice. This process emphasizes continual assessment and improvement to facilitate companies in managing AI-related risks more robustly and ensuring ethical and responsible deployment of AI systems. Additionally, our analysis uncovers and discusses critical gaps in the current framework of the NIST AI RMF, particularly concerning its application to surveillance technologies. These insights contribute to the evolving discourse on AI governance and risk management, highlighting areas for future refinement and development in frameworks like the NIST AI RMF.

Nandhini Swaminathan, David Danks

One strategy in response to pluralistic values in a user population is to personalize an AI system: if the AI can adapt to the specific values of each individual, then we can potentially avoid many of the challenges of pluralism. Unfortunately, this approach creates a significant ethical issue: if there is an external measure of success for the human-AI team, then the adaptive AI system may develop strategies (sometimes deceptive) to compensate for its human teammate. This phenomenon can be viewed as a form of social compensation, where the AI makes decisions based not on predefined goals but on its human partner's deficiencies in relation to the team's performance objectives. We provide a practical ethical analysis of the conditions in which such compensation may nonetheless be justifiable.