Back to search

Article

Achieving Demographic Parity Across Multiple Artificial Intelligence Applications: A new approach for Real-Time Bias Mitigation

2024-12-05

Abstract excerpt

Through quantitative analysis of three datasets, this study examines the efficacy of synthetic data generation in mitigating demographic bias within artificial intelligence (AI) systems across multiple sectors. It evaluates approaches based on Generative Adversarial Networks for creating demographically balanced synthetic data whilst maintaining data fidelity and model performance. The findings demonstrate signifi...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
b8f826da-5dec-5b78-ab76-099d1b38b102
DOI
10.20944/preprints202412.0468.v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Achieving Demographic Parity Across Multiple Artificial Intelligence Applications: A new approach for Real-Time Bias MitigationDOI 10.20944/preprints202412.0468.v1
Synthetic Data Generation for Bias Mitigation in AI: A Literature Review on Generative AI and Knowledge-Driven MethodsSemantic proximity
Generative AI Mitigates Representation Bias and Improves Model Fairness Through Synthetic Health DataSemantic proximity
Exploring the Role of Synthetic Data in the Future of AI in Healthcare: A Scoping Review of Frameworks, Challenges, and ImplicationsSemantic proximity
SynthCraft: an AI partner for synthetic data generation to support data access and augmentation in healthcareSemantic proximity
Actionability of Synthetic Data in a Heterogeneous and Rare Healthcare Demographic; Adolescents and Young Adults (AYAs) with CancerSemantic proximity
Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-hoc Interpretability of the Risk FactorsSemantic proximity
The Synthetic Fidelity–Stability Framework (SFSF): A Systematic Multi-Dimensional Benchmark of Synthetic Clinical Laboratory Data GeneratorsSemantic proximity
A Method for Machine Learning Generation of Realistic Synthetic Datasets for Validating Healthcare ApplicationsSemantic proximity
Data Quality Verification Metrics in Medicine: Experiments and Evaluations from the Perspectives of Safety and UtilitySemantic proximity
Synthetic data generation for a longitudinal cohort study – evaluation, method extension and reproduction of published data analysis resultsSemantic proximity
Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial IntelligenceSemantic proximity
FairPlay: Improving Medical Machine Learning Models with Generative Balancing for Equity and ExcellenceSemantic proximity
Select a neighboring publication to make it the new centre.