Recycling privileged learning and distribution matching for fairness
N Quadrianto, V Sharmanska - Advances in neural …, 2017 - proceedings.neurips.cc
Equipping machine learning models with ethical and legal constraints is a serious issue;
without this, the future of machine learning is at risk. This paper takes a step forward in this …
without this, the future of machine learning is at risk. This paper takes a step forward in this …
On the advantages of distinguishing between predictive and allocative fairness in algorithmic decision-making
F Beigang - Minds and Machines, 2022 - Springer
The problem of algorithmic fairness is typically framed as the problem of finding a unique
formal criterion that guarantees that a given algorithmic decision-making procedure is …
formal criterion that guarantees that a given algorithmic decision-making procedure is …
機械学習における多様性と公平性に関する一考察
金城敬太 - 人工知能学会研究会資料知識ベースシステム研究会123 …, 2021 - jstage.jst.go.jp
Diversity has been attracting attention in many fields, including artificial intelligence.
Moreover, fairness in machine learning has been widely investigated. Diversity and fairness …
Moreover, fairness in machine learning has been widely investigated. Diversity and fairness …
Fair machine learning through constrained stochastic optimization and an -constraint method
A strategy for fair supervised learning is proposed. It involves formulating an optimization
problem to minimize loss subject to a prescribed bound on a measure of unfairness (eg …
problem to minimize loss subject to a prescribed bound on a measure of unfairness (eg …
Accuracy and fairness trade-offs in machine learning: A stochastic multi-objective approach
S Liu, LN Vicente - Computational Management Science, 2022 - Springer
In the application of machine learning to real-life decision-making systems, eg, credit scoring
and criminal justice, the prediction outcomes might discriminate against people with …
and criminal justice, the prediction outcomes might discriminate against people with …
Evolutionary Algorithms for Fair Machine Learning
A Freitas, J Brookhouse - Handbook of Evolutionary Machine Learning, 2023 - Springer
At present, supervised machine learning algorithms are ubiquitously used to learn predictive
models that have a major impact on people's lives. However, the vast majority of such …
models that have a major impact on people's lives. However, the vast majority of such …
Facing many objectives for fairness in machine learning
D Villar, J Casillas - International Conference on the Quality of Information …, 2021 - Springer
Fairness is an increasingly important topic in the world of Artificial Intelligence. Machine
learning techniques are widely used nowadays to solve huge amounts of problems, but …
learning techniques are widely used nowadays to solve huge amounts of problems, but …
Profit Sharing に基づく強化学習の理論と応用(< 特集> 計算学習理論の進展と応用可能性)
宮崎和光， 木村元， 小林重信 - 人工知能, 1999 - jstage.jst.go.jp
抄録 1・1 工学の視点からみた強化学習 強化学習とは, 報酬という特別な人力を手がかりに環境に
適応した行動決定戦略を追求する機械学習システムである. 強化学習の重要な特徴に, 1) …
適応した行動決定戦略を追求する機械学習システムである. 強化学習の重要な特徴に, 1) …
The general utility problem in machine learning
LB Holder - Machine Learning Proceedings 1990, 1990 - Elsevier
Experiments have revealed that uncontrolled application of the analytical learning paradigm
results in knowledge having low utility. Because the performance element must consider low …
results in knowledge having low utility. Because the performance element must consider low …