arXiv research

Investigates fairness in pipeline models where individuals may drop out.

This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions. In particular, this work studies how fairness propagates through a compound decision-making processes, which we call a pipeline. Prior work in algorithmic fairness only focuses on f…

The paper aims to improve reproducibility of machine learning experiments.

While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this paper, we formalize two notions of stereotyping and show how they manifest in later allocative harms within the machine learning pipeline. …

Proposes a method to achieve quantile fairness in predictions.

New approach to fairness in machine learning models using conformal prediction.

The paper explores fairness in credit scoring using machine learning.

The influence of human judgement is ubiquitous in datasets used across the analytics industry, yet humans are known to be sub-optimal decision makers prone to various biases. Analysing biased datasets then leads to biased outcomes of the analysis. Bias by protected characteristics (e.g. race) is of particular interest …

Integrates differential privacy and demographic parity in multi-class classification.

Benchmark assesses fairness in algorithmic uncertainty, revealing consistent and calibrated estimates improve fairness.

Modyn automates continuous ML model training on growing datasets.

This paper tackles hidden technical debts in fair ML systems for Fintech.

The study diagnoses fairness issues in healthcare models under distribution shifts.

We propose a fair principal component analysis method that balances reconstruction error and subgroup fairness.

ADMM solves constrained CASH problems by breaking them into smaller, manageable pieces.

Unified framework for fair classification with group-blindness/awareness guarantees.

The study quantifies and compares aleatoric and epistemic discrimination in ML models.

Proposes a non-adversarial method for distribution matching.

Improves accuracy and fairness in prediction systems with multiple domain experts.

New fairness criterion for risk-sensitive decisions in regulated industries.

Multiverse analysis helps prevent fairness hacking and evaluate model design decisions.

Machine learning models are widely adopted in scenarios that directly affect people. The development of software systems based on these models raises societal and legal concerns, as their decisions may lead to the unfair treatment of individuals based on attributes like race or gender. Data preparation is key in any ma…

New technique reduces gender discrimination in credit lending models.

Study reveals AI skin cancer classifiers underperform for darker skin phototypes, advocating for fairness auditing.

Now that machine learning algorithms lie at the center of many resource allocation pipelines, computer scientists have been unwittingly cast as partial social planners. Given this state of affairs, important questions follow. What is the relationship between fairness as defined by computer scientists and notions of soc…

This study simulates biases in classifiers to assess fairness.

FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.

MARS-Gym framework for marketplaces to train and evaluate recommender systems.

Open dataset and pipeline for realistic OPE research.

The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisio…

This work addresses fairness in ML models by training and evaluating attribute classifiers under uncertain and incomplete data.

Complex statistical machine learning models are increasingly being used or considered for use in high-stakes decision-making pipelines in domains such as financial services, health care, criminal justice and human services. These models are often investigated as possible improvements over more classical tools such as r…

Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that researchers are hard-pressed to analyze and study them. The complicated procedures for evaluating innovations, along with the lack of standard and efficient ways of specifying and provisioning ML/DL evaluation, is a …

The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline c…

The paper addresses bias amplification in prediction and decision-making using causal analysis.

Hyperparameter tuning of multi-stage pipelines introduces a significant computational burden. Motivated by the observation that work can be reused across pipelines if the intermediate computations are the same, we propose a pipeline-aware approach to hyperparameter tuning. Our approach optimizes both the design and exe…

The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research. They offer a way to get a fair comparison between different algorithms, and the wide range of datasets available allows full control over the complexity of this evaluation. Howev…

Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network models, layers composing the neural models are simultaneously trained using backpropagation. We argue that the isolated training scheme of ML…

Study proposes a statistical testing framework for evaluating clustering pipelines.

Two simple methods learn fair metrics from data to improve fairness in ML tasks.

Study proves convergence of subgradients for optimal transport-based objectives.

New concept of within-group fairness improves AI fairness without sacrificing accuracy.

A new fairness metric for decision-making algorithms, conditioning on known fair variables.

Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for performing a particular function. Each algorithm consists of several hyperparameters. Algorithms and hyperparameters must be optimized as a whole …

Fair Mixup improves fairness in classifiers by interpolating between groups.

Paper proposes a modified fairness constraint to address shortcomings of counterfactual fairness.

DFL framework improves action and outcome fairness in policy learning.

Automates supervised learning pipeline design with matrix and tensor factorization.