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Found 35 papers, 14 papers with code
Sum of Group Error Differences: A Critical Examination of Bias Evaluation in Biometric Verification and a Dual-Metric Measure
Biometric Verification (BV) systems often exhibit accuracy disparities across different demographic groups, leading to biases in BV applications.
Effector: A Python package for regional explanations
Effector implements well-established global effect methods, assesses the heterogeneity of each method and, based on that, provides regional effects.
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
Conventional fair graph clustering methods face two primary challenges: i) They prioritize balanced clusters at the expense of cluster cohesion by imposing rigid constraints, ii) Existing methods of both individual and group-level fairness in graph partitioning mostly rely on eigen decompositions and thus, generally lack interpretability.
FairBranch: Fairness Conflict Correction on Task-group Branches for Fair Multi-Task Learning
The generalization capacity of Multi-Task Learning (MTL) becomes limited when unrelated tasks negatively impact each other by updating shared parameters with conflicting gradients, resulting in negative transfer and a reduction in MTL accuracy compared to single-task learning (STL).
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Consequently, RAMs fit one component per subregion of each feature instead of one component per feature.
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
RHALE quantifies the heterogeneity by considering the standard deviation of the local effects and automatically determines an optimal variable-size bin-splitting.
Affinity Clustering Framework for Data Debiasing Using Pairwise Distribution Discrepancy
This paper presents MASC, a data augmentation approach that leverages affinity clustering to balance the representation of non-protected and protected groups of a target dataset by utilizing instances of the same protected attributes from similar datasets that are categorized in the same cluster as the target dataset by sharing instances of the protected attribute.
Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview
AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age.
Explaining text classifiers through progressive neighborhood approximation with realistic samples
The importance of neighborhood construction in local explanation methods has been already highlighted in the literature.
A review of clustering models in educational data science towards fairness-aware learning
These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.
AdaCC: Cumulative Cost-Sensitive Boosting for Imbalanced Classification
Class imbalance poses a major challenge for machine learning as most supervised learning models might exhibit bias towards the majority class and under-perform in the minority class.
Power of Explanations: Towards automatic debiasing in hate speech detection
Hate speech detection is a common downstream application of natural language processing (NLP) in the real world.
Evaluation of group fairness measures in student performance prediction problems
Predicting students' academic performance is one of the key tasks of educational data mining (EDM).
Context matters for fairness -- a case study on the effect of spatial distribution shifts
With the ever growing involvement of data-driven AI-based decision making technologies in our daily social lives, the fairness of these systems is becoming a crucial phenomenon.
Multiple Fairness and Cardinality constraints for Students-Topics Grouping Problem
Group work is a prevalent activity in educational settings, where students are often divided into topic-specific groups based on their preferences.
Learning to Teach Fairness-aware Deep Multi-task Learning
We introduce the L2T-FMT algorithm that is a teacher-student network trained collaboratively; the student learns to solve the fair MTL problem while the teacher instructs the student to learn from either accuracy or fairness, depending on what is harder to learn for each task.
Attention Mechanism based Cognition-level Scene Understanding
Given a question-image input, the Visual Commonsense Reasoning (VCR) model can predict an answer with the corresponding rationale, which requires inference ability from the real world.
Parity-based Cumulative Fairness-aware Boosting
Data-driven AI systems can lead to discrimination on the basis of protected attributes like gender or race.
A survey on datasets for fairness-aware machine learning
For a deeper understanding of bias in the datasets, we investigate the interesting relationships using exploratory analysis.
XPROAX-Local explanations for text classification with progressive neighborhood approximation
The importance of the neighborhood for training a local surrogate model to approximate the local decision boundary of a black box classifier has been already highlighted in the literature.
Online Fairness-Aware Learning with Imbalanced Data Streams
Data-driven learning algorithms are employed in many online applications, in which data become available over time, like network monitoring, stock price prediction, job applications, etc.
Interpretable Visual Understanding with Cognitive Attention Network
While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge.
A Survey on Bias in Visual Datasets
Hence, this work aims to: i) describe the biases that might manifest in visual datasets; ii) review the literature on methods for bias discovery and quantification in visual datasets; iii) discuss existing attempts to collect bias-aware visual datasets.
Multi-fairness under class-imbalance
Recent studies showed that datasets used in fairness-aware machine learning for multiple protected attributes (referred to as multi-discrimination hereafter) are often imbalanced.
Fair-Capacitated Clustering
To this end, we introduce the fair-capacitated clustering problem that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities.
Consequence-aware Sequential Counterfactual Generation
Recently, methods have been proposed that also consider the order in which actions are applied, leading to the so-called sequential counterfactual generation problem.
Data augmentation for dealing with low sampling rates in NILM
An important step towards improving the performance of these energy disaggregation methods is to improve the quality of the data sets.
Drift-Aware Multi-Memory Model for Imbalanced Data Streams
Online class imbalance learning deals with data streams that are affected by both concept drift and class imbalance.
FairNN- Conjoint Learning of Fair Representations for Fair Decisions
In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning.
FAE: A Fairness-Aware Ensemble Framework
In the post-processing step, we tackle the problem of class overlapping by shifting the decision boundary in the direction of fairness.
AdaFair: Cumulative Fairness Adaptive Boosting
The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential discrimination.
Fairness-enhancing interventions in stream classification
The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision.
FAHT: An Adaptive Fairness-aware Decision Tree Classifier
However, there is a growing concern about the accountability and fairness of the employed models by the fact that often the available historic data is intrinsically discriminatory, i. e., the proportion of members sharing one or more sensitive attributes is higher than the proportion in the population as a whole when receiving positive classification, which leads to a lack of fairness in decision support system.
Incremental Active Opinion Learning Over a Stream of Opinionated Documents
In the experiments, we evaluate the classifier performance over time by varying: (a) the class distribution of the opinionated stream, while assuming that the set of the words in the vocabulary is fixed but their polarities may change with the class distribution; and (b) the number of unknown words arriving at each moment, while the class polarity may also change.
