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Found 5 papers, 1 papers with code
IFBiD: Inference-Free Bias Detection
This paper is the first to explore an automatic way to detect bias in deep convolutional neural networks by simply looking at their weights.
ALEBk: Feasibility Study of Attention Level Estimation via Blink Detection applied to e-Learning
The experimental framework is carried out using a public multimodal database for eye blink detection and attention level estimation called mEBAL, which comprises data from 38 students and multiples acquisition sensors, in particular, i) an electroencephalogram (EEG) band which provides the time signals coming from the student's cognitive information, and ii) RGB and NIR cameras to capture the students face gestures.
BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection
Different bot detectors are considered based on several supervised classifiers (Support Vector Machine, Random Forest, Gaussian Naive Bayes and a Long Short-Term Memory network) and a learning framework including human and synthetic samples.
Measuring Bias in AI Models: An Statistical Approach Introducing N-Sigma
This risk assessment should address, among others, the detection and mitigation of bias in AI.
Is my Data in your AI Model? Membership Inference Test with Application to Face Images
This paper introduces the Membership Inference Test (MINT), a novel approach that aims to empirically assess if specific data was used during the training of Artificial Intelligence (AI) models.
