162 citations · 630 across the 36 of their papers we have counts for
40 papers
Unbiased Binning for Fairness-aware Attribute Representation
Abolfazl Asudeh, Zeinab Asoodeh, Bita Asoodeh +1
Discretizing raw features into bucketized attribute representations is a popular step before sharing a dataset. It is, however, evident that this step can cause significant bias in…
Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor
Nima Shahbazi, Stavros Sintos, Abolfazl Asudeh
Frequency estimation in streaming data often relies on sketches like Count-Min (CM) to provide approximate answers with sublinear space. However, CM sketches introduce additive err…
FairDeFace: Evaluating the Fairness and Adversarial Robustness of Face Obfuscation Methods
Seyyed Mohammad Sadegh Moosavi Khorzooghi, Poojitha Thota, Mohit Singhal +3
The lack of a common platform and benchmark datasets for evaluating face obfuscation methods has been a challenge, with every method being tested using arbitrary experiments, datas…
Fair Set Cover
Mohsen Dehghankar, Rahul Raychaudhury, Stavros Sintos +1
The potential harms of algorithmic decisions have ignited algorithmic fairness as a central topic in computer science. One of the fundamental problems in computer science is Set Co…
REQUAL-LM: Reliability and Equity through Aggregation in Large Language Models
Sana Ebrahimi, Nima Shahbazi, Abolfazl Asudeh
The extensive scope of large language models (LLMs) across various domains underscores the critical importance of responsibility in their application, beyond natural language proce…
FairEM360: A Suite for Responsible Entity Matching
Nima Shahbazi, Mahdi Erfanian, Abolfazl Asudeh +2
Entity matching is one the earliest tasks that occur in the big data pipeline and is alarmingly exposed to unintentional biases that affect the quality of data. Identifying and mit…
AXOLOTL: Fairness through Assisted Self-Debiasing of Large Language Model Outputs
Sana Ebrahimi, Kaiwen Chen, Abolfazl Asudeh +2
Pre-trained Large Language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading…
Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities
Mahdi Erfanian, H. V. Jagadish, Abolfazl Asudeh
The potential harms of the under-representation of minorities in training data, particularly in multi-modal settings, is a well-recognized concern. While there has been extensive e…
Auditing Yelp's Business Ranking and Review Recommendation Through the Lens of Fairness
Mohit Singhal, Javier Pacheco, Seyyed Mohammad Sadegh Moosavi Khorzooghi +4
Auditing is critical to ensuring the fairness and reliability of decision-making systems. However, auditing a black-box system for bias can be challenging due to the lack of transp…
Efficient Strongly Polynomial Algorithms for Quantile Regression
Suraj Shetiya, Shohedul Hasan, Abolfazl Asudeh +1
Linear Regression is a seminal technique in statistics and machine learning, where the objective is to build linear predictive models between a response (i.e., dependent) variable…
Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching
Nima Shahbazi, Nikola Danevski, Fatemeh Nargesian +2
Entity matching (EM) is a challenging problem studied by different communities for over half a century. Algorithmic fairness has also become a timely topic to address machine bias…
Online Maximum Independent Set of Hyperrectangles
Rishi Advani, Abolfazl Asudeh
The maximum independent set problem is a classical NP-hard problem in theoretical computer science. In this work, we study a special case where the family of graphs considered is r…
A Fair and Memory/Time-efficient Hashmap
Abolfazl Asudeh, Nima Shahbazi, Stavros Sintos
Hashmap is a fundamental data structure in computer science. There has been extensive research on constructing hashmaps that minimize the number of collisions leading to efficient…
Data Coverage for Detecting Representation Bias in Image Datasets: A Crowdsourcing Approach
Melika Mousavi, Nima Shahbazi, Abolfazl Asudeh
Existing machine learning models have proven to fail when it comes to their performance for minority groups, mainly due to biases in data. In particular, datasets, especially socia…
Maximizing Neutrality in News Ordering
Rishi Advani, Paolo Papotti, Abolfazl Asudeh
The detection of fake news has received increasing attention over the past few years, but there are more subtle ways of deceiving one's audience. In addition to the content of news…
PopSim: An Individual-level Population Simulator for Equitable Allocation of City Resources
Khanh Duy Nguyen, Nima Shahbazi, Abolfazl Asudeh
Historical systematic exclusionary tactics based on race have forced people of certain demographic groups to congregate in specific urban areas. Aside from the ethical aspects of s…
FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairness
Francesco Di Carlo, Nazanin Nezami, Hadis Anahideh +1
Despite the potential benefits of machine learning (ML) in high-risk decision-making domains, the deployment of ML is not accessible to practitioners, and there is a risk of discri…
Maximizing Fair Content Spread via Edge Suggestion in Social Networks
Ian P. Swift, Sana Ebrahimi, Azade Nova +1
Content spread inequity is a potential unfairness issue in online social networks, disparately impacting minority groups. In this paper, we view friendship suggestion, a common fea…
Reliability Evaluation of Individual Predictions: A Data-centric Approach
Nima Shahbazi, Abolfazl Asudeh
Machine learning models only provide probabilistic guarantees on the expected loss of random samples from the distribution represented by their training data. As a result, a model…
Representation Bias in Data: A Survey on Identification and Resolution Techniques
Nima Shahbazi, Yin Lin, Abolfazl Asudeh +1
Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately. Representation Bias in d…
Finding Representative Group Fairness Metrics Using Correlation Estimations
Hadis Anahideh, Nazanin Nezami, Abolfazl Asudeh
It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling…
OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning
Hantian Zhang, Xu Chu, Abolfazl Asudeh +1
Machine learning (ML) is increasingly being used to make decisions in our society. ML models, however, can be unfair to certain demographic groups (e.g., African Americans or femal…
MithraDetective: A System for Cherry-picked Trendlines Detection
Yoko Nagafuchi, Yin Lin, Kaushal Mamgain +5
Given a data set, misleading conclusions can be drawn from it by cherry-picking selected samples. One important class of conclusions is a trend derived from a data set of values ov…
Maximizing coverage while ensuring fairness: a tale of conflicting objective
Abolfazl Asudeh, Tanya Berger-Wolf, Bhaskar DasGupta +1
Ensuring fairness in computational problems has emerged as a topic during recent years, buoyed by considerations for equitable resource distributions and social justice. It $…