Medical imaging models may encode demographic attributes without violating fairness, depending on the approach.
problem Discrimination in medical imaging models due to encoding demographic attributes.
method Examined marginal and class-conditional representation invariance, traditional fairness notions, and counterfactual fairness.
result Demographically invariant models may not necessarily be fair, and encoding demographic attributes can be advantageous.
Algorithm ensures demographic parity in regression without sensitive attribute data.
problem Performing regression with demographic parity constraints.
method Post-processing algorithm using accurate estimates and sensitive attribute predictor.
result Generates predictions meeting demographic parity constraint.
Most companies utilize demographic information to develop their strategy in a market. However, such information is not available to most retail companies. Several studies have been conducted to predict the demographic attributes of users from their transaction histories, but they have some limitations. First, they focu…
Paper proposes a method to detect fair communities in graphs considering demographic attributes.
problem Inconsistent community detection violates fairness constraints for nodes with demographic attributes.
method Develops an ℓ1-regularized pseudo-likelihood approach for fair graphical model selection. result The method ensures demographic groups are fairly represented within detected communities.
Private release of sensitive data enables fair learning.
problem Learning fair predictors with restricted sensitive data.
method Private release of sensitive demographic data, adapting non-discriminatory learners.
result The approach provides theoretical guarantees on performance for fair predictors.
Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in -- and can be recovered from -- the intermediate representations learned by text-based neural classifiers. The …
Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications. Demographic data is an enabler of personalization, identity security, and other applications. Despite that, this data is sensitive and often hard to obtain. Previous work has shown that purchase …
We find the optimal error for a constrained regression model under a linear model.
problem Minimizing error while adhering to demographic parity constraints.
method Proposed a minimax optimal error analysis for a demographic parity-constrained regression problem within a linear model.
result The minimax optimal error is characterized by $Θ( rac{dM}{n})$.
New method optimizes fairness in predictive models for continuous sensitive attributes.
problem Enforcing full statistical independence on continuous sensitive attributes is too restrictive.
method Functional bilevel optimization (FBO) and ITD algorithms.
result Achieves lowest or near-lowest fairness-accuracy regret on synthetic and real datasets.
Decomposes bias in linear models under demographic parity constraints.
problem Understanding and quantifying bias in linear models under fairness constraints.
method Post-processing framework to decompose bias into direct and indirect components.
result Analytical characterization of how demographic parity reshapes model coefficients.
In this paper, we introduce the Fairness GAN, an approach for generating a dataset that is plausibly similar to a given multimedia dataset, but is more fair with respect to protected attributes in allocative decision making. We propose a novel auxiliary classifier GAN that strives for demographic parity or equality of …
Proposes a new method for fairness in machine learning with multiple protected attributes.
problem Ensuring fairness in machine learning models with continuous and multiple protected attributes.
method Distance covariance regularisation framework to mitigate association between model predictions and protected attributes.
result Demonstrates effectiveness in mitigating fairness gerrymandering in regression tasks.
Proposes a framework to create fair IDRs by enforcing demographic parity constraints.
problem Discrimination in IDRs trained on biased data.
method Incorporates DP and CDP constraints into IDR estimation.
result Theoretically optimal IDRs can be efficiently obtained through perturbations.
Classifiers that achieve demographic balance by explicitly using protected attributes such as race or gender are often politically or culturally controversial due to their lack of individual fairness, i.e. individuals with similar qualifications will receive different outcomes. Individually and group fair decision crit…
We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal model representations not to encode sensitive information, which has the effect of pushing downstream predictors towards exhibiting demograph…
New framework enforces demographic parity on distribution tails.
problem Enforcing demographic parity on entire distribution can degrade accuracy.
method Optimal transport theory, focusing on distribution tails.
result More nuanced and context-sensitive fairness interventions.
A new method uses model gradients to improve fairness without relying on demographic data.
problem Algorithmic fairness issues due to missing demographic information and complex interactions.
method Learning a graph of gradients to identify and improve group fairness robustly to noise.
result Significantly improves fairness without sacrificing overall accuracy.
Proposes a sequential framework for fairness in multiple sensitive attributes.
problem Fairness in the presence of multiple sensitive attributes.
method Sequential framework using multi-marginal Wasserstein barycenters.
result Closed-form solution for sequentially fair predictor.
Develops a method to ensure fairness across multiple sensitive attributes in machine learning.
problem Ensuring fairness among demographic groups formed by multiple sensitive attributes.
method Formulates intersectional fairness as a mutual information minimization problem and proposes a generic end-to-end algorithmic framework.
result Demonstrates effective debiasing of classification results with minimal impact to accuracy.
Study uses synthetic data to estimate credit risk for underbanked consumers in Istanbul.
problem Estimating credit risk for underbanked consumers lacking formal credit records.
method Created synthetic dataset, used retrieval augmented generation, trained CatBoost, LightGBM, and XGBoost models.
result Alternative financial data improves credit risk estimation, raising AUC by 13%.
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled representation learning literature, we propose an algorithm for learning compact representations of datasets that are useful for reconstruc…
New method mitigates bias without sensitive data using causal graph and variational autoencoder.
problem Lack of fairness strategies when sensitive attributes are not collected.
method SRCVAE framework based on causal graph for inferring a proxy sensitive attribute.
result Significant improvements in fairness metrics over existing methods.
A non-trivial predictor satisfies demographic parity and equalizes group risks in regression.
problem Achieving fairness in regression models while maintaining equal risks across groups.
method Provided an explicit example of a non-constant predictor satisfying Demographic Parity and Equal Group-Wise Risks.
result First explicit construction of a non-constant predictor satisfying both fairness notions.
Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations de…
A fair PCA method using JEVD ensures balanced data representation.
problem PCA's bias in data with demographic characteristics.
method Joint Eigenvalue Decomposition (JEVD) for fair PCA.
result JEVD optimally balances fairness and PCA's data structure.
Proposes a method to achieve quantile fairness in predictions.
problem Lack of research on quantile fairness in socially sensitive domains.
method Introduces a framework to learn a real-valued quantile function under Demographic Parity fairness.
result Demonstrates superior empirical performance and uncovering fairness-accuracy trade-offs.
The paper develops a method to achieve fairness in predictions using Wasserstein barycenters.
problem Learning a fair real-valued function independent of sensitive attributes.
method Establishing a connection between fair regression and optimal transport theory, deriving a close form expression for the optimal fair predictor as the Wasserstein barycenter of sensitive groups.
result The optimal fair predictor's distribution is the Wasserstein barycenter of sensitive groups' distributions, offering an intuitive interpretation and a simple post-processing algorithm.
Synthetic data mimics real-world demographics for fairness testing.
problem Lack of complete, representative datasets for fairness testing.
method Construct synthetic datasets using overlapping real and separate datasets.
result Synthetic data yields consistent fairness metrics with real data.
Calendar graph neural networks model user behavior with location and time data.
problem Modeling user behavior with location and time information for demographic prediction.
method Graph neural networks with a tripartite network of items, sessions, and locations, and a hierarchical calendar network.
result User embeddings preserve spatial and temporal patterns of various periodicity.
The paper tackles fair representation learning by smoothing feature mappings.
problem Legal liability for discriminatory use of data by organizations.
method Mapping features to a fair representation space, certifying fairness through chi-squared mutual information.
result Smoothing representation distribution provides generalization guarantees of fairness and maintains accuracy for downstream tasks.
Paper detects biases in medical imaging ML models using counterfactual analysis.
problem Bias in medical imaging ML models negatively impacts generalization performance.
method Counterfactual invariance framework combining conditional latent diffusion models and statistical hypothesis testing.
result The method identifies and quantifies biases without direct access to counterfactual data.
The prevalence of wearable sensors (e.g., smart wristband) is creating unprecedented opportunities to not only inform health and wellness states of individuals, but also assess and infer personal attributes, including demographic and personality attributes. However, the data captured from wearables, such as heart rate …
Electronic medical record (EMR) data contains historical sequences of visits of patients, and each visit contains rich information, such as patient demographics, hospital utilisation and medical codes, including diagnosis, procedure and medication codes. Most existing EMR embedding methods capture visit-code associatio…
We consider the problem of diversity enhancing clustering, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc. In the context of fair clustering, diversity plays a major role when fairness is understood as demographic…
Paper protects privacy and fairness in deep learning models.
problem Ensuring fairness in deep learning models while protecting sensitive data.
method Uses differential privacy and Lagrangian duality to design fair predictors.
result Demonstrates improved model performance on prediction tasks.
In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM …
Automated decision making systems are increasingly being used in real-world applications. In these systems for the most part, the decision rules are derived by minimizing the training error on the available historical data. Therefore, if there is a bias related to a sensitive attribute such as gender, race, religion, e…
This work addresses fairness in ML models by training and evaluating attribute classifiers under uncertain and incomplete data.
problem Challenges in fairness metrics due to uncertain and incomplete data.
method Developed a theoretical and empirical analysis to understand and improve bias estimation in the data-scarce regime.
result The test accuracy of the attribute classifier is not always correlated with its effectiveness in bias estimation.
Network-assisted regression uses conformal prediction for valid inference.
problem Predicting node attributes using network and conventional covariates with valid statistical inference.
method Network analog of conformal prediction under mild joint exchangeability assumption.
result Achieves finite sample validity and asymptotic conditional validity for various network covariates.
Paper characterizes fairness vs. accuracy tradeoff in classification.
problem Mitigating bias in machine learning models.
method Characterizes the tradeoff between fairness and accuracy, provides a post-processing algorithm.
result Post-processing algorithm yields optimal fair classifier when score is Bayes optimal.
We demonstrate that effortlessly accessible digital records of behavior such as Facebook Likes can be obtained and utilized to automatically distinguish a wide range of highly delicate personal traits including: life satisfaction, cultural ethnicity, political views, age, gender and personality traits. The analysis pre…
The paper tackles multi-level fairness in algorithmic systems, addressing bias at both individual and structural levels.
problem Algorithmic systems can unfairly impact marginalized groups, especially when considering only individual-level bias.
method Formalizes multi-level fairness using causal inference tools, addressing effects of sensitive attributes at multiple levels.
result Illustrates the importance of accounting for macro-level sensitive attributes in fairness assessments.
Unified framework for fair regression in aware and unaware settings.
problem Lack of principled methods for fair regression in unawareness settings.
method Formulated as an optimal transport problem, unifying aware and unaware settings.
result Characterizes optimal prediction functions via optimal transport maps under different penalties.
The paper proposes a method to improve fairness in machine learning models without refitting.
problem Mitigating biases in machine learning models that disadvantage certain groups.
method Infinitesimal jackknife-based approach to drop selected training data points.
result The intervention improves fairness without significantly reducing predictive performance.
Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we…
A new model shows fairness mechanisms can improve selection utility even without implicit bias.
problem Improving selection fairness without introducing a utility trade-off.
method A model with latent quality and group-dependent variance, comparing fairness mechanisms to group-oblivious selection.
result Demographic parity always increases selection utility, while γ-rules weakly increase it. FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data
problem Ensuring fairness in machine learning
method Bayesian Experimental Design
result Improved fairness-accuracy trade-offs
New classifiers ensure fairness by adjusting a base classifier's operating characteristics.
problem Ensuring fairness in binary classification with multiple group constraints.
method Intervening directly on a base classifier's operating characteristics using group-wise ROC convex hulls and post-processing.
result Methods satisfy multiple fairness constraints (DP, EO, PP) with minimal interventions and near-oracle accuracy.