A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study examines fairness in machine learning for credit scoring.
Fairness-aware classification is receiving increasing attention in the machine learning fields. Recently research proposes to formulate the fairness-aware classification as constrained optimization problems. However, several limitations exist in previous works due to the lack of a theoretical framework for guiding the …
Enhances fairness in predictions without sacrificing accuracy.
Automated decision making based on big data and machine learning (ML) algorithms can result in discriminatory decisions against certain protected groups defined upon personal data like gender, race, sexual orientation etc. Such algorithms designed to discover patterns in big data might not only pick up any encoded soci…
The paper provides CI for test unfairness of group-fairness-aware classifiers trained with online SGD.
FanG-HPO optimizes machine learning models for fairness and low energy consumption.
New study shows how adversaries can bias fair machine learning models even with corrupted data.
This work introduces a fair learning method for diverse sensitive attributes.
Work proposes CLAIRE to achieve counterfactual fairness from observational data without causal models.
Introduces privilege scores to measure and interpret protected attribute-related privilege in machine learning models.
Fairness-aware learning is a novel framework for classification tasks. Like regular empirical risk minimization (ERM), it aims to learn a classifier with a low error rate, and at the same time, for the predictions of the classifier to be independent of sensitive features, such as gender, religion, race, and ethnicity. …
Fairness-aware learning is increasingly important in data mining. Discrimination prevention aims to prevent discrimination in the training data before it is used to conduct predictive analysis. In this paper, we focus on fair data generation that ensures the generated data is discrimination free. Inspired by generative…
Paper studies fair classification of functional data.
Propensity score matching improves fairness in machine learning models.
Proposes a method to enforce fairness in machine learning models without sensitive data.
FLEA makes fair classifiers robust against unreliable training data.
The development of fair machine learning models that effectively avert bias and discrimination is an important problem that has garnered attention in recent years. The necessity of encoding complex relational dependencies among the features and variables for competent predictions require the development of fair, yet ex…
Study shows how online personalization can lead to unfair models due to biased user responses.
This paper studies fairness and privacy in federated learning, proposing algorithms to balance both.
A novel multi-objective optimization framework improves insurance pricing fairness.
DQ4FairIM uses RL to maximize influence while ensuring fairness across all groups.
FIFA improves fairness in imbalanced datasets by encouraging both classification and fairness generalization.
Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is a growing concern about the accounta…
FairUDT uses uplift decision trees to detect and mitigate discrimination in training data.
New method learns fair representations by separating out protected attributes.
The paper proves statistical consistency and fairness guarantees for a plug-in algorithm.
Bayesian data selection framework ensures fairness in machine learning models.
Methodology explores fairness limits in decision tree classifiers.
Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context…
Algorithms learned from data are increasingly used for deciding many aspects in our life: from movies we see, to prices we pay, or medicine we get. Yet there is growing evidence that decision making by inappropriately trained algorithms may unintentionally discriminate people. For example, in automated matching of cand…
Building machine learning models that are fair with respect to an unprivileged group is a topical problem. Modern fairness-aware algorithms often ignore causal effects and enforce fairness through modifications applicable to only a subset of machine learning models. In this work, we propose a new definition of fairness…
The potential for learned models to amplify existing societal biases has been broadly recognized. Fairness-aware classifier constraints, which apply equality metrics of performance across subgroups defined on sensitive attributes such as race and gender, seek to rectify inequity but can yield non-uniform degradation in…
Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive …
New algorithm reduces discrimination in predictions.
FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data
The paper evaluates the importance of monotonicity in AI fairness across various fields.
This work optimizes model performance while ensuring fairness through AUC constraints.
Machine learning algorithms are now frequently used in sensitive contexts that substantially affect the course of human lives, such as credit lending or criminal justice. This is driven by the idea that `objective' machines base their decisions solely on facts and remain unaffected by human cognitive biases, discrimina…
Fairness-aware diffusion for graph neural networks
Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation b…
Study evaluates fairness of machine learning models on Kaggle and finds some optimization techniques can induce unfairness.
FAML addresses biased evidence learning in multi-view learning, improving fairness and prediction reliability.
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. In particular, in certain cases it has been observed that ML algorithms can provide different decisions based on sensitive a…
Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discriminat…
The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. While plenty of fair algorithms have been proposed recently to tackle this challenge for discrete variables, only a few ideas exist for continuous ones. The objective in this paper is to ensure some independence lev…
Machine learning models are extensively being used to make decisions that have a significant impact on human life. These models are trained over historical data that may contain information about sensitive attributes such as race, sex, religion, etc. The presence of such sensitive attributes can impact certain populati…
Unified framework for fair regression in aware and unaware settings.
Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation and synchronization steps. To reduce the communication overhead and improve the overall efficiency of FL, gradient sparsification (GS) can …