In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.
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Spécialisation Ethics in the Age of AILearnQuestÀ propos de ce cours
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Cours 2 sur 4 dans le
Niveau débutant
Approx. 6 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol, Japonais
Compétences que vous acquerrez
- machine learning fairness
- Ethics
- data bias
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 2 sur 4 dans le
Niveau débutant
Approx. 6 heures pour terminer
Anglais
Sous-titres : Français, Portugais (européen), Russe, Anglais, Espagnol, Japonais
Offert par
LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
Programme de cours : ce que vous apprendrez dans ce cours
2 heures pour terminer
Fairness and protections in machine learning
Welcome to the course! In week one, we will be discussing what fairness means in the context of machine learning and what true parity means in different scenarios
2 heures pour terminer
5 vidéos (Total 19 min), 2 lectures, 3 quiz
2 heures pour terminer
Building fair models: theory and practice
This week we will take action against unfairness. Now that we have an understanding of fairness issues, how do we build models that do not violate them?
2 heures pour terminer
5 vidéos (Total 21 min), 2 lectures, 3 quiz
2 heures pour terminer
Human factors: minimizing bias in data
This week, we will tackle the human biases that enter the data collection and attribute selection processes. The goal? Removing bias before the model is built
2 heures pour terminer
5 vidéos (Total 33 min), 2 lectures, 3 quiz
Avis
- 5 stars83,33 %
- 4 stars13,88 %
- 3 stars2,77 %
Meilleurs avis pour ARTIFICIAL INTELLIGENCE DATA FAIRNESS AND BIAS
par RU19 avr. 2022
Really great discussion of algorithms and how their designs make them susceptible to bias.
par CM2 oct. 2021
An excellent reminder that the bias-variance trade-off is not the only trade-off machine learning specialists make.
par SY30 mars 2021
A relatively short and interesting course on data fairness and bias impacting AI models.
À propos du Spécialisation Ethics in the Age of AI
As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations. In this specialization, we will explore the rise of algorithms, fundamental issues of fairness and bias in machine learning, and basic concepts involved in security and privacy of machine learning projects. We'll finish with a study of 3 projects that will allow you to put your new skills into action.
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