publications

2025

1. Working Paper

   Scoring is Not Enough: Addressing Gaps in Fairness-Utility Tradeoff for Ranking

   Shubham Singh, Ian A. Kash, and Mesrob I. Ohannessian

   2025

   Working Paper
2. Under Review

   To Bid or Not to Bid: Using Auctions to Understand User Valuation of Digital Accounts

   Shubham Singh, Jackie Hu, Cormac Herley, Elissa Redmiles, Siddharth Suri, and Oshrat Ayalon

   2025

   Under Review
3. Under Review

   Effect of Resources on the Efficiency-Fairness Tradeoff for Allocation Problems

   Shubham Singh, Chris Kanich, and Ian A. Kash

   2025

   Under Review

2024

1. arXiv

   Evaluating the Social Impact of Generative AI Systems in Systems and Society

   Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Canyu Chen, Hal Daumé III, Jesse Dodge, Isabella Duan, Ellie Evans, Felix Friedrich, Avijit Ghosh, Usman Gohar, Sara Hooker, Yacine Jernite, Ria Kalluri, Alberto Lusoli, Alina Leidinger, Michelle Lin, Xiuzhu Lin, Sasha Luccioni, Jennifer Mickel, Margaret Mitchell, Jessica Newman, Anaelia Ovalle, Marie-Therese Png, Shubham Singh, Andrew Strait, Lukas Struppek, and Arjun Subramonian

   2024

   Bib HTML PDF

   @misc{solaiman2024evaluatingsocialimpactgenerative,
     title = {{Evaluating the Social Impact of Generative AI Systems in Systems and Society}},
     author = {Solaiman, Irene and Talat, Zeerak and Agnew, William and Ahmad, Lama and Baker, Dylan and Blodgett, Su Lin and Chen, Canyu and III, Hal Daumé and Dodge, Jesse and Duan, Isabella and Evans, Ellie and Friedrich, Felix and Ghosh, Avijit and Gohar, Usman and Hooker, Sara and Jernite, Yacine and Kalluri, Ria and Lusoli, Alberto and Leidinger, Alina and Lin, Michelle and Lin, Xiuzhu and Luccioni, Sasha and Mickel, Jennifer and Mitchell, Margaret and Newman, Jessica and Ovalle, Anaelia and Png, Marie-Therese and Singh, Shubham and Strait, Andrew and Struppek, Lukas and Subramonian, Arjun},
     year = {2024},
     url = {https://arxiv.org/abs/2306.05949},
     eprint = {2306.05949},
     archiveprefix = {arXiv},
     primaryclass = {cs.CY},
   }

2023

1. GenLaw

   AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI

   Ayse Gizem Yasar, Andrew Chong, Evan Dong, Thomas Krendl Gilbert, Sarah Hladikova, Roland Maio, Carlos Mougan, Xudong Shen, Shubham Singh, Ana-Andreea Stoica, Savannah Thais, and Miri Zilka

   In Workshop on Generative AI + Law 2023 (GenLaw ’23), Co-located with the 40th International Conference on Machine Learning (ICML 2023), Jul 23, 2023, Honolulu, HI, USA, Jul 2023

   Bib HTML PDF

   @inproceedings{yasar-genlaw2023-dma,
     title = {{AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI}},
     author = {Yasar, Ayse Gizem and Chong, Andrew and Dong, Evan and Gilbert, Thomas Krendl and Hladikova, Sarah and Maio, Roland and Mougan, Carlos and Shen, Xudong and Singh, Shubham and Stoica, Ana-Andreea and Thais, Savannah and Zilka, Miri},
     year = {2023},
     month = jul,
     booktitle = {Workshop on Generative AI + Law 2023 (GenLaw '23), Co-located with the 40th International Conference on Machine Learning (ICML 2023), Jul 23, 2023, Honolulu, HI, USA},
   }

2. ConPro

   Tracking, But Make It Offline: The Privacy Implications of Scanning QR Codes Found in the World

   Rayaan Siddiqi^*, Shubham Singh^*, Lenore Zuck, and Chris Kanich

   In 7th Workshop on Technology and Consumer Protection (ConPro ’23), Co-located with the 44th IEEE Symposium on Security and Privacy, May 25, 2023, San Francisco, CA, USA, May 2023

   Bib HTML PDF

   @inproceedings{conpro-2023-qrcode,
     title = {Tracking, But Make It Offline: The Privacy Implications of Scanning QR Codes Found in the World},
     author = {Siddiqi, Rayaan and Singh, Shubham and Zuck, Lenore and Kanich, Chris},
     year = {2023},
     month = may,
     booktitle = {7th Workshop on Technology and Consumer Protection (ConPro ’23), Co-located with the 44th IEEE Symposium on Security and Privacy, May 25, 2023, San Francisco, CA, USA},
   }

2022

1. EAAMO

   Fair Decision-Making for Food Inspections

   Shubham Singh, Bhuvni Shah, Chris Kanich, and Ian A. Kash

   In Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), Arlington, VA, USA, Oct 2022

   Bib HTML PDF

   @inproceedings{singh-fairfood-eaamo2022,
     title = {Fair Decision-Making for Food Inspections},
     author = {Singh, Shubham and Shah, Bhuvni and Kanich, Chris and Kash, Ian A.},
     year = {2022},
     month = oct,
     booktitle = {Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO)},
     location = {Arlington, VA, USA},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     series = {EAAMO '22},
     doi = {10.1145/3551624.3555289},
     isbn = {9781450394772},
     url = {https://doi.acm.org?doi=3551624.3555289},
     articleno = {5},
     numpages = {11},
     keywords = {food inspections, fairness, scheduling},
   }

2. RDMDE

   Open Problems in (Un)fairness of the Retail Food Safety Inspection Process

   Tanya Berger-Wolf, Allison Howell, Chris Kanich, Ian A. Kash, Moniba Keymanesh, Barbara Kowalcyk, Gina Nicholson Kramer, Andrew Perrault, and Shubham Singh

   In Responsible Decision Making in Dynamic Environments Workshop, Held in Conjunction with ICML 2022, July 23, 2022, Baltimore MD, USA, Jul 2022

   Bib HTML PDF

   @inproceedings{rdmde-2022-openproblems,
     title = {Open Problems in (Un)fairness of the Retail Food Safety Inspection Process},
     author = {Berger-Wolf, Tanya and Howell, Allison and Kanich, Chris and Kash, Ian A. and Keymanesh, Moniba and Kowalcyk, Barbara and Kramer, Gina Nicholson and Perrault, Andrew and Singh, Shubham},
     year = {2022},
     month = jul,
     booktitle = {Responsible Decision Making in Dynamic Environments Workshop, Held in Conjunction with {ICML} 2022, July 23, 2022, Baltimore MD, USA},
   }

2021

1. USENIX

   Helping Users Automatically Find and Manage Sensitive, Expendable Files in Cloud Storage

   Mohammad Taha Khan, Christopher Tran, Shubham Singh, Dimitri Vasilkov, Chris Kanich, Blase Ur, and Elena Zheleva

   In 30th USENIX Security Symposium (USENIX Security 21), Aug 2021

   Abs Bib HTML PDF

   With the ubiquity of data breaches, forgotten-about files stored in the cloud create latent privacy risks. We take a holistic approach to help users identify sensitive, unwanted files in cloud storage. We first conducted 17 qualitative interviews to characterize factors that make humans perceive a file as sensitive, useful, and worthy of either protection or deletion. Building on our findings, we conducted a primarily quantitative online study. We showed 108 long-term users of Google Drive or Dropbox a selection of files from their accounts. They labeled and explained these files’ sensitivity, usefulness, and desired management (whether they wanted to keep, delete, or protect them). For each file, we collected many metadata and content features, building a training dataset of 3,525 labeled files. We then built Aletheia, which predicts a file’s perceived sensitivity and usefulness, as well as its desired management. Aletheia improves over state-of-the-art baselines by 26% to 159%, predicting users’ desired file-management decisions with 79% accuracy. Notably, predicting subjective perceptions of usefulness and sensitivity led to a 10% absolute accuracy improvement in predicting desired file-management decisions. Aletheia’s performance validates a human-centric approach to feature selection when using inference techniques on subjective security-related tasks. It also improves upon the state of the art in minimizing the attack surface of cloud accounts.

   @inproceedings{taha_alethia_2021,
     author = {Khan, Mohammad Taha and Tran, Christopher and Singh, Shubham and Vasilkov, Dimitri and Kanich, Chris and Ur, Blase and Zheleva, Elena},
     title = {Helping Users Automatically Find and Manage Sensitive, Expendable Files in Cloud Storage},
     booktitle = {30th USENIX Security Symposium (USENIX Security 21)},
     year = {2021},
     isbn = {978-1-939133-24-3},
     pages = {1145--1162},
     url = {https://www.usenix.org/conference/usenixsecurity21/presentation/khan-mohammad},
     publisher = {{USENIX} Association},
     month = aug,
   }

2020

1. NetSci-X

   NeXLink: Node Embedding Framework for Cross-Network Linkages Across Social Networks

   Rishabh Kaushal, Shubham Singh, and Ponnurangam Kumaraguru

   In Proceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science, Aug 2020

   Abs Bib HTML PDF

   Users create accounts on multiple social networks to get connected to their friends across these networks. We refer to these user accounts as user identities. Since users join multiple social networks, therefore, there will be cases where a pair of user identities across two different social networks belong to the same individual. We refer to such pairs as Cross-Network Linkages (CNLs). In this work, we model the social network as a graph to explore the question, whether we can obtain effective social network graph representation such that node embeddings of users belonging to CNLs are closer in embedding space than other nodes, using only the network information. To this end, we propose a modular and flexible node embedding framework, referred to as NeXLink, which comprises of three steps. First, we obtain local node embeddings by preserving the local structure of nodes within the same social network. Second, we learn the global node embeddings by preserving the global structure, which is present in the form of common friendship exhibited by nodes involved in CNLs across social networks. Third, we combine the local and global node embeddings, which preserve local and global structures to facilitate the detection of CNLs across social networks. We evaluate our proposed framework on an augmented (synthetically generated) dataset of 63,713 nodes & 817,090 edges and real-world dataset of 3338 Twitter-Foursquare node pairs. Our approach achieves an average Hit@1 rate of 98% for detecting CNLs across social networks and significantly outperforms previous state-of-the-art methods.

   @inproceedings{kaushal_nexlink_2020,
     author = {Kaushal, Rishabh and Singh, Shubham and Kumaraguru, Ponnurangam},
     title = {NeXLink: Node Embedding Framework for Cross-Network Linkages Across Social Networks},
     booktitle = {Proceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science},
     year = {2020},
     publisher = {Springer International Publishing},
     address = {Cham},
     pages = {61--75},
     isbn = {978-3-030-38965-9},
   }

2019

1. ACM SAC

   KidsGUARD: Fine Grained Approach for Child Unsafe Video Representation and Detection

   Shubham Singh, Rishabh Kaushal, Arun Balaji Buduru, and Ponnurangam Kumaraguru

   In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, Limassol, Cyprus, Aug 2019

   Abs Bib HTML PDF

   Increasingly more and more videos are being uploaded on video sharing platforms, and a significant number of viewers on these platforms are children. At times, these videos have violent or sexually explicit scenes (referred as child unsafe) to catch children’s attention. To evade moderation, malicious video uploaders typically limit the child unsafe content to only a few frames in the video. Hence, a fine-grained approach, referred as KidsGUARD1, to detect sparsely present child unsafe content is required. Prior approaches to content moderation either flag the entire video as inappropriate or use hand-crafted features derived from video frames. In this work, we leverage Long Short Term Memory (LSTM) based autoencoder to learn effective video representations of video descriptors obtained from using VGG16 Convolutional Neural Network (CNN). Encoded video representations are fed into LSTM classifier for detection of sparse child unsafe video content. To evaluate this approach, we create a dataset of 109,835 video clips curated specifically for child unsafe content. We find that deep learning approach (1) detects fine-grained child unsafe video content with the granularity of 1 second, (2) identifies even sparsely location child unsafe video content by achieving a high recall of 81% at high precision of 80%, and (3) outperforms baseline video encoding approaches based on like Fisher Vector (FV) and Vector of Locally Aggregated Descriptors (VLAD).

   @inproceedings{singh_kidsguard_2019,
     author = {Singh, Shubham and Kaushal, Rishabh and Buduru, Arun Balaji and Kumaraguru, Ponnurangam},
     title = {KidsGUARD: Fine Grained Approach for Child Unsafe Video Representation and Detection},
     year = {2019},
     isbn = {9781450359337},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3297280.3297487},
     doi = {10.1145/3297280.3297487},
     booktitle = {Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing},
     pages = {2104–2111},
     numpages = {8},
     keywords = {child safety, video analysis, social media analysis},
     location = {Limassol, Cyprus},
     series = {SAC ’19},
   }