On the public citation graph

Black box fairness testing of machine learning models

0 signed · 195 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-642-19835-9openalex-snapshot

Tools and Algorithms for the Construction and Analysis of Systems

inferredcites →https://doi.org/10.1109/eurosp.2017.29openalex-snapshot

FairTest: Discovering Unwarranted Associations in Data-Driven Applications

inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

inferredcites →https://doi.org/10.1109/iccv37128.2017openalex-snapshot

2017 IEEE International Conference on Computer Vision (ICCV)

inferredcites →https://doi.org/10.1145/1064978.1065036openalex-snapshot

DART

inferredcites →https://doi.org/10.1145/1065010.1065036openalex-snapshot

DART

inferredcites →https://doi.org/10.1145/1081706.1081750openalex-snapshot

CUTE

inferredcites →https://doi.org/10.1145/1180405.1180445openalex-snapshot

EXE

inferredcites →https://doi.org/10.1145/1190215.1190226openalex-snapshot

Compositional dynamic test generation

inferredcites →https://doi.org/10.1145/1190216.1190226openalex-snapshot

Compositional dynamic test generation

inferredcites →https://doi.org/10.1145/2090236.2090255openalex-snapshot

Fairness through awareness

inferredcites →https://doi.org/10.1145/2783258.2783311openalex-snapshot

Certifying and Removing Disparate Impact

inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot

"Why Should I Trust You?"

inferredcites →https://doi.org/10.1145/3106237.3106277openalex-snapshot

Fairness testing: testing software for discrimination

inferredcites →https://doi.org/10.1145/3132747.3132785openalex-snapshot

DeepXplore

inferredcites →https://doi.org/10.1145/3180155.3180220openalex-snapshot

DeepTest

inferredcites →https://doi.org/10.1145/3238147.3238165openalex-snapshot

Automated directed fairness testing

inferredcites →https://doi.org/10.1145/3238147.3238172openalex-snapshot

Concolic testing for deep neural networks

inferredcites →https://doi.org/10.1145/3238147.3238202openalex-snapshot

DeepGauge: multi-granularity testing criteria for deep learning systems

inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot

Intriguing properties of neural networks

inferredcites →https://doi.org/10.48550/arxiv.1611.04967openalex-snapshot

Iterative Orthogonal Feature Projection for Diagnosing Bias in Black-Box Models

inferredcites →https://doi.org/10.48550/arxiv.1703.04730openalex-snapshot

Understanding Black-box Predictions via Influence Functions

inferredcites →https://doi.org/10.48550/arxiv.1803.04792openalex-snapshot

Testing Deep Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1807.10439openalex-snapshot

Symbolic Execution for Deep Neural Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.5246323openalex-extracted

inferred← citeshttps://doi.org/10.1002/widm.1498openalex-snapshot

Bias in human data: A feedback from social sciences

inferred← citeshttps://doi.org/10.1007/978-3-030-64881-7_16openalex-snapshot

Automatic Fairness Testing of Machine Learning Models

inferred← citeshttps://doi.org/10.1007/978-3-030-79474-3_13openalex-snapshot

Coverage-Guided Fairness Testing

inferred← citeshttps://doi.org/10.1007/978-3-030-99429-7openalex-snapshot

Fundamental Approaches to Software Engineering

inferred← citeshttps://doi.org/10.1007/978-3-030-99429-7_14openalex-snapshot

AequeVox: Automated Fairness Testing of Speech Recognition Systems

inferred← citeshttps://doi.org/10.1007/978-3-031-21251-2_3openalex-snapshot

Efficient Fairness Testing Through Hash-Based Sampling

inferred← citeshttps://doi.org/10.1007/978-3-031-21251-2_7openalex-snapshot

Applying Combinatorial Testing to Verification-Based Fairness Testing

inferred← citeshttps://doi.org/10.1007/978-3-031-22064-7_27openalex-snapshot

TPFL: Test Input Prioritization for Deep Neural Networks Based on Fault Localization

inferred← citeshttps://doi.org/10.1007/978-3-031-40132-9_1openalex-snapshot

On Testing Ethical Autonomous Decision-Making

inferred← citeshttps://doi.org/10.1007/978-3-031-70245-7_3openalex-snapshot

RSUTT: Robust Search Using T-Way Testing

inferred← citeshttps://doi.org/10.1007/978-3-031-70300-3_20openalex-snapshot

Unbiasing on the Fly: Explanation-Guided Human Oversight of Machine Learning Decisions

inferred← citeshttps://doi.org/10.1007/978-3-032-08977-9_48openalex-snapshot

Fairness Testing: Review of the Relevance and Methodologies of Ensuring Software Fairness

inferred← citeshttps://doi.org/10.1007/978-981-95-1786-2_7openalex-snapshot

Responsible DevOps for Cyber-Physical Systems

inferred← citeshttps://doi.org/10.1007/978-981-97-0425-5_19openalex-snapshot

Interpretable White-Box Fairness Testing Through Biased Neuron Identification

inferred← citeshttps://doi.org/10.1007/978-981-99-7184-8_1openalex-snapshot

Testing, Debugging, and Repairing Individual Discrimination in Machine Learning Models

inferred← citeshttps://doi.org/10.1007/978-981-99-7356-9_28openalex-snapshot

Black-Box Fairness Testing with Shadow Models

inferred← citeshttps://doi.org/10.1007/s00778-021-00671-8openalex-snapshot

Managing bias and unfairness in data for decision support: a survey of machine learning and data engineering approaches to identify and mitigate bias and unfairness within data management and analytics systems

inferred← citeshttps://doi.org/10.1007/s10489-020-02002-wopenalex-snapshot

Stacked-autoencoder-based model for COVID-19 diagnosis on CT images

inferred← citeshttps://doi.org/10.1007/s10664-021-09985-1openalex-snapshot

To what extent do DNN-based image classification models make unreliable inferences?

inferred← citeshttps://doi.org/10.1007/s10664-021-10105-2openalex-snapshot

Systematic literature review on software quality for AI-based software

inferred← citeshttps://doi.org/10.1007/s10664-022-10116-7openalex-snapshot

Search-based fairness testing for regression-based machine learning systems

inferred← citeshttps://doi.org/10.1007/s10664-023-10402-yopenalex-snapshot

Fairness-aware machine learning engineering: how far are we?

inferred← citeshttps://doi.org/10.1007/s10664-023-10409-5openalex-snapshot

Detection and evaluation of bias-inducing features in machine learning

inferred← citeshttps://doi.org/10.1007/s10664-023-10419-3openalex-snapshot

Search-based Automatic Repair for Fairness and Accuracy in Decision-making Software

inferred← citeshttps://doi.org/10.1007/s10664-024-10520-1openalex-snapshot

Prioritizing test cases for deep learning-based video classifiers

inferred← citeshttps://doi.org/10.1007/s43681-022-00147-7openalex-snapshot

Fairness Score and process standardization: framework for fairness certification in artificial intelligence systems

inferred← citeshttps://doi.org/10.1007/s43681-023-00266-9openalex-snapshot

A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle

inferred← citeshttps://doi.org/10.1007/s43681-023-00398-yopenalex-snapshot

To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods

inferred← citeshttps://doi.org/10.1007/s43681-024-00536-0openalex-snapshot

Minimum levels of interpretability for artificial moral agents

inferred← citeshttps://doi.org/10.1007/s44443-026-01130-6openalex-snapshot

FIFT: Feature Importance-Guided Fairness Testing for machine learning software

inferred← citeshttps://doi.org/10.1016/j.infsof.2025.107876openalex-snapshot

Representation-based fairness evaluation and bias correction robustness assessment in neural networks

inferred← citeshttps://doi.org/10.1016/j.infsof.2025.107906openalex-snapshot

Prune bias from the root: Bias removal and fairness estimation by pruning sensitive attributes in pre-trained DNN models

inferred← citeshttps://doi.org/10.1016/j.infsof.2025.107980openalex-snapshot

Fair and square? Evaluating fairness of LLM-generated synthetic datasets

inferred← citeshttps://doi.org/10.1016/j.jbusres.2022.113522openalex-snapshot

A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends

inferred← citeshttps://doi.org/10.1016/j.jss.2021.111031openalex-snapshot

A software engineering perspective on engineering machine learning systems: State of the art and challenges

inferred← citeshttps://doi.org/10.1016/j.jss.2024.112090openalex-snapshot

Distribution-aware fairness test generation

inferred← citeshttps://doi.org/10.1016/j.jss.2026.113066openalex-snapshot

A systematic exploration of fairness requirements, modeling, and operationalization in engineering AI-based software systems

inferred← citeshttps://doi.org/10.1016/j.scs.2026.107472openalex-snapshot

Unraveling the coupling-driven patterns of urban carbon emissions and urban heat islands: An LCZ-based interpretable machine learning approach

inferred← citeshttps://doi.org/10.1016/j.tbench.2023.100137openalex-snapshot

Algorithmic fairness in social context

inferred← citeshttps://doi.org/10.1017/dsj.2024.23openalex-snapshot

Modular oversight methodology: a framework to aid ethical alignment of algorithmic creations

inferred← citeshttps://doi.org/10.1038/s41562-025-02331-1openalex-snapshot

A large-scale comparison of divergent creativity in humans and large language models

inferred← citeshttps://doi.org/10.1093/jamiaopen/ooae149openalex-snapshot

Algorithmic individual fairness and healthcare: a scoping review

inferred← citeshttps://doi.org/10.1109/access.2024.3502164openalex-snapshot

Beyond the Seeds: Fairness Testing via Counterfactual Analysis of Non-Seed Instances

inferred← citeshttps://doi.org/10.1109/access.2025.3618868openalex-snapshot

FairPrompt: Efficient Multi-Objective Prompt Optimization for Fairness Testing in Conversational AI Systems

inferred← citeshttps://doi.org/10.1109/apsec66846.2025.00041openalex-snapshot

Latent Search-Based Boundary Aware Fairness Testing for Deployed Deep Models

inferred← citeshttps://doi.org/10.1109/apsec66846.2025.00044openalex-snapshot

Cobweb: Enhanced Generation Diversity for Black-box Fairness Testing

inferred← citeshttps://doi.org/10.1109/ase63991.2025.00049openalex-snapshot

Provable Fairness Repair for Deep Neural Networks

inferred← citeshttps://doi.org/10.1109/ase63991.2025.00141openalex-snapshot

Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations

inferred← citeshttps://doi.org/10.1109/chase52884.2021.00016openalex-snapshot

Ideology in Open Source Development

inferred← citeshttps://doi.org/10.1109/compsac61105.2024.00163openalex-snapshot

Boundary-Guided Black-Box Fairness Testing

inferred← citeshttps://doi.org/10.1109/esem64174.2025.00040openalex-snapshot

Is Diversity a Meaningful Metric in Fairness Testing?

inferred← citeshttps://doi.org/10.1109/fg61629.2025.11099137openalex-snapshot

Gender Fairness of Machine Learning Algorithms for Pain Detection

inferred← citeshttps://doi.org/10.1109/iccece58074.2023.10135360openalex-snapshot

Fair Machine Learning-An Analytical Study Based on CiteSpace

inferred← citeshttps://doi.org/10.1109/icmla52953.2021.00123openalex-snapshot

MLCHECK– Property-Driven Testing of Machine Learning Classifiers

inferred← citeshttps://doi.org/10.1109/icoco59262.2023.10397906openalex-snapshot

Search-Based Fairness Testing: An Overview

inferred← citeshttps://doi.org/10.1109/icse-companion52605.2021.00041openalex-snapshot

Testing Framework for Black-box AI Models

inferred← citeshttps://doi.org/10.1109/icse-seip55303.2022.9793864openalex-snapshot

Software engineering for Responsible AI: An empirical study and operationalised patterns

inferred← citeshttps://doi.org/10.1109/icse43902.2021.00043openalex-snapshot

AUTOTRAINER: An Automatic DNN Training Problem Detection and Repair System

inferred← citeshttps://doi.org/10.1109/icse43902.2021.00046openalex-snapshot

Prioritizing Test Inputs for Deep Neural Networks via Mutation Analysis

inferred← citeshttps://doi.org/10.1109/icse43902.2021.00129openalex-snapshot

"Ignorance and Prejudice" in Software Fairness

inferred← citeshttps://doi.org/10.1109/icse48619.2023.00133openalex-snapshot

Towards Understanding Fairness and its Composition in Ensemble Machine Learning

inferred← citeshttps://doi.org/10.1109/icse48619.2023.00134openalex-snapshot

Fairify: Fairness Verification of Neural Networks

inferred← citeshttps://doi.org/10.1109/icse48619.2023.00136openalex-snapshot

Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks

inferred← citeshttps://doi.org/10.1109/icse55347.2025.00016openalex-snapshot

Fairquant: Certifying and Quantifying Fairness of Deep Neural Networks

inferred← citeshttps://doi.org/10.1109/icse55347.2025.00159openalex-snapshot

FairSense: Long-Term Fairness Analysis of ML-Enabled Systems

inferred← citeshttps://doi.org/10.1109/icsme58846.2023.00034openalex-snapshot

Characterizing the Complexity and Its Impact on Testing in ML-Enabled Systems : A Case Sutdy on Rasa

inferred← citeshttps://doi.org/10.1109/icsme64153.2025.00012openalex-snapshot

Software Fairness Testing in Practice

inferred← citeshttps://doi.org/10.1109/icst69053.2026.00077openalex-snapshot

Individual Fairness Testing in Fairness through Unawareness

inferred← citeshttps://doi.org/10.1109/icstw55395.2022.00030openalex-snapshot

A Combinatorial Approach to Fairness Testing of Machine Learning Models

inferred← citeshttps://doi.org/10.1109/icstw64639.2025.10962484openalex-snapshot

Fairness Testing of Machine Learning Models using Combinatorial Testing in Latent Space

inferred← citeshttps://doi.org/10.1109/mci.2023.3327834openalex-snapshot

Forward Composition Propagation for Explainable Neural Reasoning

inferred← citeshttps://doi.org/10.1109/saner-c66551.2025.00008openalex-snapshot

Contextual Fairness-Aware Practices in ML: A Cost-Effective Empirical Evaluation

inferred← citeshttps://doi.org/10.1109/saner-c66551.2025.00029openalex-snapshot

Towards User-Focused Cross-Domain Testing: Disentangling Accessibility, Usability, and Fairness

inferred← citeshttps://doi.org/10.1109/satml54575.2023.00011openalex-snapshot

Explainable Global Fairness Verification of Tree-Based Classifiers

inferred← citeshttps://doi.org/10.1109/trustcom50675.2020.00029openalex-snapshot

Fairness Testing of Machine Learning Models Using Deep Reinforcement Learning

inferred← citeshttps://doi.org/10.1109/tse.2020.3034721openalex-snapshot

Towards Security Threats of Deep Learning Systems: A Survey

inferred← citeshttps://doi.org/10.1109/tse.2021.3101478openalex-snapshot

Automatic Fairness Testing of Neural Classifiers Through Adversarial Sampling

inferred← citeshttps://doi.org/10.1109/tse.2022.3141758openalex-snapshot

ASTRAEA: Grammar-based Fairness Testing

inferred← citeshttps://doi.org/10.1109/tse.2023.3330982openalex-snapshot

Robust Test Selection for Deep Neural Networks

inferred← citeshttps://doi.org/10.1109/tse.2025.3581402openalex-snapshot

Boosting Generalizable Fairness With Mahalanobis Distances Guided Boltzmann Exploratory Testing

inferred← citeshttps://doi.org/10.1111/exsy.13670openalex-snapshot

Facial emotion recognition: A comprehensive review

inferred← citeshttps://doi.org/10.1145/1122445.1122456openalex-snapshot

Data mining and compression : where to apply it and what are the effects?

inferred← citeshttps://doi.org/10.1145/3324884.3418932openalex-snapshot

Making fair ML software using trustworthy explanation

inferred← citeshttps://doi.org/10.1145/3368089.3409671openalex-snapshot

Correlations between deep neural network model coverage criteria and model quality

inferred← citeshttps://doi.org/10.1145/3368089.3409697openalex-snapshot

Fairway: a way to build fair ML software

inferred← citeshttps://doi.org/10.1145/3368089.3409704openalex-snapshot

Do the machine learning models on a crowd sourced platform exhibit bias? an empirical study on model fairness

inferred← citeshttps://doi.org/10.1145/3377812.3382133openalex-snapshot

EvalDNN

inferred← citeshttps://doi.org/10.1145/3395363.3397352openalex-snapshot

Higher income, larger loan? monotonicity testing of machine learning models

inferred← citeshttps://doi.org/10.1145/3411764.3445604openalex-snapshot

Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits

inferred← citeshttps://doi.org/10.1145/3460319.3464816openalex-snapshot

ModelDiff: testing-based DNN similarity comparison for model reuse detection

inferred← citeshttps://doi.org/10.1145/3460319.3464820openalex-snapshot

Efficient white-box fairness testing through gradient search

inferred← citeshttps://doi.org/10.1145/3468264.3468536openalex-snapshot

Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline

inferred← citeshttps://doi.org/10.1145/3468264.3468537openalex-snapshot

Bias in machine learning software: why? how? what to do?

inferred← citeshttps://doi.org/10.1145/3468264.3468565openalex-snapshot

Fairea: a model behaviour mutation approach to benchmarking bias mitigation methods

inferred← citeshttps://doi.org/10.1145/3475716.3475791openalex-snapshot

An Empirical Examination of the Impact of Bias on Just-in-time Defect Prediction

inferred← citeshttps://doi.org/10.1145/3487043openalex-snapshot

Software Engineering for AI-Based Systems: A Survey

inferred← citeshttps://doi.org/10.1145/3490489openalex-snapshot

NPC: N euron P ath C overage via Characterizing Decision Logic of Deep Neural Networks

inferred← citeshttps://doi.org/10.1145/3493700.3493704openalex-snapshot

Data Synthesis for Testing Black-Box Machine Learning Models

inferred← citeshttps://doi.org/10.1145/3510003.3510091openalex-snapshot

Training data debugging for the fairness of machine learning software

inferred← citeshttps://doi.org/10.1145/3510003.3510123openalex-snapshot

NeuronFair

inferred← citeshttps://doi.org/10.1145/3510003.3510137openalex-snapshot

Explanation-guided fairness testing through genetic algorithm

inferred← citeshttps://doi.org/10.1145/3510003.3510202openalex-snapshot

Fairness-aware configuration of machine learning libraries

inferred← citeshttps://doi.org/10.1145/3514094.3534203openalex-snapshot

Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations

inferred← citeshttps://doi.org/10.1145/3514258openalex-snapshot

Toward Involving End-users in Interactive Human-in-the-loop AI Fairness

inferred← citeshttps://doi.org/10.1145/3524491.3527305openalex-snapshot

Fair-SSL

inferred← citeshttps://doi.org/10.1145/3524491.3527308openalex-snapshot

Privileged and unprivileged groups

inferred← citeshttps://doi.org/10.1145/3529318openalex-snapshot

Testing Feedforward Neural Networks Training Programs

inferred← citeshttps://doi.org/10.1145/3540250.3549093openalex-snapshot

MAAT: a novel ensemble approach to addressing fairness and performance bugs for machine learning software

inferred← citeshttps://doi.org/10.1145/3540250.3549124openalex-snapshot

API recommendation for machine learning libraries: how far are we?

inferred← citeshttps://doi.org/10.1145/3540250.3549169openalex-snapshot

RULER: discriminative and iterative adversarial training for deep neural network fairness

inferred← citeshttps://doi.org/10.1145/3582573openalex-snapshot

QuoTe : Quality-oriented Testing for Deep Learning Systems

inferred← citeshttps://doi.org/10.1145/3583561openalex-snapshot

A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers

inferred← citeshttps://doi.org/10.1145/3585006openalex-snapshot

Fair Enough: Searching for Sufficient Measures of Fairness

inferred← citeshttps://doi.org/10.1145/3597503.3623333openalex-snapshot

Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment

inferred← citeshttps://doi.org/10.1145/3597503.3639181openalex-snapshot

MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search

inferred← citeshttps://doi.org/10.1145/3597926.3598058openalex-snapshot

FairRec: Fairness Testing for Deep Recommender Systems

inferred← citeshttps://doi.org/10.1145/3597926.3598099openalex-snapshot

Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing

inferred← citeshttps://doi.org/10.1145/3607145openalex-snapshot

Does this Explanation Help? Designing Local Model-agnostic Explanation Representations and an Experimental Evaluation Using Eye-tracking Technology

inferred← citeshttps://doi.org/10.1145/3611643.3616257openalex-snapshot

Fix Fairness, Don’t Ruin Accuracy: Performance Aware Fairness Repair using AutoML

inferred← citeshttps://doi.org/10.1145/3617168openalex-snapshot

Faire: Repairing Fairness of Neural Networks via Neuron Condition Synthesis

inferred← citeshttps://doi.org/10.1145/3617555.3617871openalex-snapshot

Automated Fairness Testing with Representative Sampling

inferred← citeshttps://doi.org/10.1145/3626234openalex-snapshot

Responsible AI Pattern Catalogue: A Collection of Best Practices for AI Governance and Engineering

inferred← citeshttps://doi.org/10.1145/3630106.3658990openalex-snapshot

A Critical Survey on Fairness Benefits of Explainable AI

inferred← citeshttps://doi.org/10.1145/3639475openalex-snapshot

Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Society

inferred← citeshttps://doi.org/10.1145/3639475.3640103openalex-snapshot

Towards Engineering Fair and Equitable Software Systems for Managing Low-Altitude Airspace Authorizations

inferred← citeshttps://doi.org/10.1145/3643786.3648022openalex-snapshot

Data vs. Model Machine Learning Fairness Testing: An Empirical Study

inferred← citeshttps://doi.org/10.1145/3650212.3680380openalex-snapshot

NeuFair: Neural Network Fairness Repair with Dropout

inferred← citeshttps://doi.org/10.1145/3652155openalex-snapshot

Fairness Testing: A Comprehensive Survey and Analysis of Trends

inferred← citeshttps://doi.org/10.1145/3663533.3664040openalex-snapshot

Predicting Fairness of ML Software Configurations

inferred← citeshttps://doi.org/10.1145/3664608openalex-snapshot

Fairness Testing of Machine Translation Systems

inferred← citeshttps://doi.org/10.1145/3664647.3681545openalex-snapshot

FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework

inferred← citeshttps://doi.org/10.1145/3674805.3686673openalex-snapshot

Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems

inferred← citeshttps://doi.org/10.1145/3680463openalex-snapshot

An Empirical Study of Testing Machine Learning in the Wild

inferred← citeshttps://doi.org/10.1145/3691620.3695300openalex-snapshot

Toward Individual Fairness Testing with Data Validity

inferred← citeshttps://doi.org/10.1145/3691620.3695481openalex-snapshot

Approximation-guided Fairness Testing through Discriminatory Space Analysis

inferred← citeshttps://doi.org/10.1145/3691620.3695486openalex-snapshot

FIPSER: Improving Fairness Testing of DNN by Seed Prioritization

inferred← citeshttps://doi.org/10.1145/3730577openalex-snapshot

Less Is More: Feature Engineering for Fairness and Performance of Machine Learning Software

inferred← citeshttps://doi.org/10.1145/3744916.3773175openalex-snapshot

On the Robustness of Fairness Practices: A Causal Framework for Systematic Evaluation

inferred← citeshttps://doi.org/10.1145/3756681.3757048openalex-snapshot

Testing Individual Fairness in Graph Neural Networks

inferred← citeshttps://doi.org/10.1145/3773088openalex-snapshot

Causally Perturbed Fairness Testing

inferred← citeshttps://doi.org/10.1145/3832783.3834430openalex-snapshot

Towards Verifiable Fairness Testing: Auditable Coverage over Data-Supported Input Domains

inferred← citeshttps://doi.org/10.14569/ijacsa.2023.0140202openalex-snapshot

Adversarial Sampling for Fairness Testing in Deep Neural Network

inferred← citeshttps://doi.org/10.1609/aaai.v35i15.17594openalex-snapshot

Generate Your Counterfactuals: Towards Controlled Counterfactual Generation for Text

inferred← citeshttps://doi.org/10.1609/aaai.v37i7.25994openalex-snapshot

CertiFair: A Framework for Certified Global Fairness of Neural Networks

inferred← citeshttps://doi.org/10.1613/jair.1.16759openalex-snapshot

Bias Mitigation Methods: Applicability, Legality, and Recommendations for Development

inferred← citeshttps://doi.org/10.18293/seke2022-064openalex-snapshot

An efficient discrimination discovery method for fairness testing

inferred← citeshttps://doi.org/10.24294/jipd11489openalex-snapshot

A systematic review of algorithm auditing processes to assess bias and risks in AI systems

inferred← citeshttps://doi.org/10.24963/ijcai.2020/64openalex-snapshot

Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models

inferred← citeshttps://doi.org/10.31274/td-20240329-848openalex-snapshot

Understanding unfairness and its mitigation in open-source machine learning models

inferred← citeshttps://doi.org/10.3389/fpsyg.2021.627561openalex-snapshot

Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions

inferred← citeshttps://doi.org/10.3390/app12052404openalex-snapshot

A Deep Learning-Based Password Security Evaluation Model

inferred← citeshttps://doi.org/10.3390/info17100974openalex-snapshot

Artificial Intelligence for Verification and Validation of Safety-Critical Systems: A Bibliometric Science-Mapping Study and an Assurance-Level Framework (AVAF) (2016–2026)

inferred← citeshttps://doi.org/10.3390/land14050979openalex-snapshot

Decoupling Urban Street Attractiveness: An Ensemble Learning Analysis of Color and Visual Element Contributions

inferred← citeshttps://doi.org/10.3390/s21165381openalex-snapshot

Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures

inferred← citeshttps://doi.org/10.3390/software4030018openalex-snapshot

Intersectional Software Engineering as a Field

inferred← citeshttps://doi.org/10.3390/software5020018openalex-snapshot

Software Unfairness Detection in Machine Learning-Based Systems: A Systematic Mapping Study

inferred← citeshttps://doi.org/10.36040/mnemonic.v5i1.4438openalex-snapshot

PENGEMBANGAN APLIKASI SMART-BOOK SEBAGAI MEDIA PEMBELAJARAN BAHASA INGGRIS ANAK BERBASIS AR (AUGMENTED REALITY)

inferred← citeshttps://doi.org/10.37934/araset.40.2.127139openalex-snapshot

BiasTrap: Runtime Detection of Biased Prediction in Machine Learning Systems

inferred← citeshttps://doi.org/10.4000/books.putc.15552openalex-snapshot

Vers une gouvernance responsable des systèmes d’IA organisée autour de la gestion des risques

inferred← citeshttps://doi.org/10.47577/technium.v16i.9984openalex-snapshot

Determining Student Achievements using Multicriteria Approach with Electre Method

inferred← citeshttps://doi.org/10.48550/arxiv.2002.12278openalex-snapshot

Testing Monotonicity of Machine Learning Models

inferred← citeshttps://doi.org/10.48550/arxiv.2006.11737openalex-snapshot

Verifying Individual Fairness in Machine Learning Models

inferred← citeshttps://doi.org/10.48550/arxiv.2011.03751openalex-snapshot

Software engineering for artificial intelligence and machine learning software: A systematic literature review

inferred← citeshttps://doi.org/10.48550/arxiv.2102.00417openalex-snapshot

Priority-based Post-Processing Bias Mitigation for Individual and Group Fairness

inferred← citeshttps://doi.org/10.48550/arxiv.2102.03054openalex-snapshot

Removing biased data to improve fairness and accuracy

inferred← citeshttps://doi.org/10.48550/arxiv.2105.00741openalex-snapshot

MLCheck- Property-Driven Testing of Machine Learning Models

inferred← citeshttps://doi.org/10.48550/arxiv.2111.08856openalex-snapshot

Fairness Testing of Deep Image Classification with Adequacy Metrics

inferred← citeshttps://doi.org/10.53106/160792642022092305019openalex-snapshot

Fairness Measures of Machine Learning Models in Judicial Penalty Prediction

inferred← citeshttps://doi.org/10.5753/kdmile.2023.232900openalex-snapshot

Proposal of a Method for Identifying Unfairness in Machine Learning Models based on Counterfactual Explanations

inferred← citeshttps://doi.org/10.5753/sast.2024.3791openalex-snapshot

Property-based Testing for Machine Learning Models

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.