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Jatinder Singh

Jatinder Singh appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
cs.CYArtificial IntelligenceCryptography and SecurityHuman-Computer InteractionNetworking and Internet ArchitectureComputer Science and Game TheoryGeneral LiteratureMachine LearningOperating Systems

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Published work

8 published item(s)

preprint2026arXiv

To Build or Not to Build? Factors that Lead to Non-Development or Abandonment of AI Systems

Responsible AI research typically focuses on examining the use and impacts of deployed AI systems. Yet, there is currently limited visibility into the pre-deployment decisions to pursue building such systems in the first place. Decisions taken in the earlier stages of development shape which systems are ultimately released, and therefore represent potential, but underexplored, points for intervention. As such, this paper investigates factors influencing AI non-development and abandonment throughout the development lifecycle. Specifically, we first perform a scoping review of academic literature, civil society resources, and grey literature including journalism and industry reports. Through thematic analysis of these sources, we develop a taxonomy of six categories of factors contributing to AI abandonment: ethical concerns, stakeholder feedback, development lifecycle challenges, organizational dynamics, resource constraints, and legal/regulatory concerns. Then, we collect data on real-world case of AI system abandonment via an AI incident database and a practitioner survey to evidence and compare factors that drive abandonment both prior to and following system deployment. While academic responsible AI communities often emphasize ethical risks as reasons to not develop AI, our empirical analysis of these cases demonstrates the diverse, and often non-ethics-related, levers that motivate organizations to abandon AI development. Synthesizing evidence from our taxonomy and related case study analyses, we identify gaps and opportunities in current responsible AI research to (1) engage with the diverse range of levers that influence organizations to abandon AI development, and (2) better support appropriate (dis)engagement with AI system development.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits

Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML practitioners actually use these toolkits in practice. In this paper, we conducted the first in-depth empirical exploration of how industry practitioners (try to) work with existing fairness toolkits. In particular, we conducted think-aloud interviews to understand how participants learn about and use fairness toolkits, and explored the generality of our findings through an anonymous online survey. We identified several opportunities for fairness toolkits to better address practitioner needs and scaffold them in using toolkits effectively and responsibly. Based on these findings, we highlight implications for the design of future open-source fairness toolkits that can support practitioners in better contextualizing, communicating, and collaborating around ML fairness efforts.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Advancing Data Justice Research and Practice: An Integrated Literature Review

The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a broader understanding of what equitable, freedom-promoting, and rights-sustaining data collection, governance, and use should look like in increasingly dynamic and global data innovation ecosystems. In this integrated literature review we hope to lay the conceptual groundwork needed to support this aspiration. The introduction motivates the broadening of data justice that is undertaken by the literature review which follows. First, we address how certain limitations of the current study of data justice drive the need for a re-location of data justice research and practice. We map out the strengths and shortcomings of the contemporary state of the art and then elaborate on the challenges faced by our own effort to broaden the data justice perspective in the decolonial context. The body of the literature review covers seven thematic areas. For each theme, the ADJRP team has systematically collected and analysed key texts in order to tell the critical empirical story of how existing social structures and power dynamics present challenges to data justice and related justice fields. In each case, this critical empirical story is also supplemented by the transformational story of how activists, policymakers, and academics are challenging longstanding structures of inequity to advance social justice in data innovation ecosystems and adjacent areas of technological practice.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems

This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning. We draw on an understanding of ADM as a socio-technical process involving both human and technical elements, beginning before a decision is made and extending beyond the decision itself. While explanations and other model-centric mechanisms may assist some accountability concerns, they often provide insufficient information of these broader ADM processes for regulatory oversight and assessments of legal compliance. Reviewability involves breaking down the ADM process into technical and organisational elements to provide a systematic framework for determining the contextually appropriate record-keeping mechanisms to facilitate meaningful review - both of individual decisions and of the process as a whole. We argue that a reviewability framework, drawing on administrative law's approach to reviewing human decision-making, offers a practical way forward towards more a more holistic and legally-relevant form of accountability for ADM.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Monitoring Misuse for Accountable 'Artificial Intelligence as a Service'

AI is increasingly being offered 'as a service' (AIaaS). This entails service providers offering customers access to pre-built AI models and services, for tasks such as object recognition, text translation, text-to-voice conversion, and facial recognition, to name a few. The offerings enable customers to easily integrate a range of powerful AI-driven capabilities into their applications. Customers access these models through the provider's APIs, sending particular data to which models are applied, the results of which returned. However, there are many situations in which the use of AI can be problematic. AIaaS services typically represent generic functionality, available 'at a click'. Providers may therefore, for reasons of reputation or responsibility, seek to ensure that the AIaaS services they offer are being used by customers for 'appropriate' purposes. This paper introduces and explores the concept whereby AIaaS providers uncover situations of possible service misuse by their customers. Illustrated through topical examples, we consider the technical usage patterns that could signal situations warranting scrutiny, and raise some of the legal and technical challenges of monitoring for misuse. In all, by introducing this concept, we indicate a potential area for further inquiry from a range of perspectives.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Protection and Deception: Discovering Game Theory and Cyber Literacy through a Novel Board Game Experience

Cyber literacy merits serious research attention because it addresses a confluence of specialization and generalization; cybersecurity is often conceived of as approachable only by a technological intelligentsia, yet its interdependent nature demands education for a broad population. Therefore, educational tools should lead participants to discover technical knowledge in an accessible and attractive framework. In this paper, we present Protection and Deception (P&G), a novel two-player board game. P&G has three main contributions. First, it builds cyber literacy by giving participants "hands-on" experience with game pieces that have the capabilities of cyber-attacks such as worms, masquerading attacks/spoofs, replay attacks, and Trojans. Second, P&G teaches the important game-theoretic concepts of asymmetric information and resource allocation implicitly and non-obtrusively through its game play. Finally, it strives for the important objective of security education for underrepresented minorities and people without explicit technical experience. We tested P&G at a community center in Manhattan with middle- and high school students, and observed enjoyment and increased cyber literacy along with suggestions for improvement of the game. Together with these results, our paper also presents images of the attractive board design and 3D printed game pieces, together with a Monte-Carlo analysis that we used to ensure a balanced gaming experience.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

A MAC Layer Based Defense Architecture for Reduction of Quality (RoQ) Attacks in Wireless LAN

Recently an alternative of DDoS attacks called shrew attacks or Reduction of Quality (RoQ) has been identified which is very much difficult to detect. The RoQ attacks can use source and destination IP address spoofing, and they do not have distinct periodicity, and may not filter the attack packets precisely. In this paper, we propose to design the MAC layer based defense architecture for RoQ attacks in Wireless LAN which includes the detection and response stages. The attackers are detected by checking the RTS CTS packets from the MAC layer and the corresponding attack flows are blocked or rejected. By our simulation results, we show that our proposed technique achieves reduces the attack throughput there by increasing the received bandwidth and reducing the packet loss of legitimate users.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Seamless Flow Migration on Smartphones without Network Support

This paper addresses the following question: Is it possible to migrate TCP/IP flows between different networks on modern mobile devices, without infrastructure support or protocol changes? To answer this question, we make three research contributions. (i) We report a comprehensive characterization of IP traffic on smartphones using traces collected from 27 iPhone 3GS users for three months. (ii) Driven by the findings from the characterization, we devise two novel system mechanisms for mobile devices to sup-port seamless flow migration without network support, and extensively evaluate their effectiveness using our field collected traces of real-life usage. Wait-n-Migrate leverages the fact that most flows are short lived. It establishes new flows on newly available networks but allows pre-existing flows on the old network to terminate naturally, effectively decreasing, or even eliminating, connectivity gaps during network switches. Resumption Agent takes advantage of the functionality integrated into many modern protocols to securely resume flows without application intervention. When combined, Wait-n-Migrate and Resumption Agent provide an unprecedented opportunity to immediately deploy performance and efficiency-enhancing policies that leverage multiple networks to improve the performance, efficiency, and connectivity of mobile devices. (iii) Finally, we report an iPhone 3GS based implementation of these two system mechanisms and show that their overhead is negligible. Furthermore, we employ an example network switching policy, called AutoSwitch, to demonstrate their performance. AutoSwitch improves the Wi-Fi user experience by intelligently migrating TCP flows between Wi-Fi and cellular networks. Through traces and field measurements, we show that AutoSwitch reduces the number of user disruptions by an order of magnitude.

0 citations0 reviewsNo code linkedOpen access