Race, Technology, and Algorithmic Bias - Vision and Justice
Explore the intersection of race, technology, and algorithmic bias with experts discussing AI's limitations in recognizing dark-skinned faces and the role of arts in addressing these issues.
Unethical Algorithms of Massive Scale
Exploring the impact of biased search algorithms on opinions and voting behavior, with insights on new tracking systems to detect online manipulations and unethical practices.
Stanford CS109 - Fairness in Probability for Computer Scientists - Lecture 26
Explore fairness in probability and computer science, examining ethical considerations and algorithmic bias in data-driven decision-making systems.
Artificial Intelligence by CrashCourse
In 20 episodes, Jabril will teach you about Artificial Intelligence and Machine Learning!
Reasons Why Social Networks Make Us Vulnerable to Misinformation
Explore how cognitive, social, and algorithmic biases in social networks make us susceptible to misinformation, and learn about efforts to counter online opinion manipulation.
Machine Learning and Social Norms - Balancing Fairness and Accuracy
Machine learning's impact on social decisions: Exploring fairness, privacy, and accountability in algorithmic decision-making across various domains. Discusses recent research and challenges in embedding social norms into predictive algorithms.
Algorithmic Bias in AI: Implications and Challenges - A Discussion with Cristopher Moore and Melanie Moses
Explore algorithmic bias in AI, its societal impacts, and the quest for transparency in decision-making systems. Delve into facial recognition flaws and implications for algorithmic justice.
Transparency in Algorithms for Risk Assessment, Housing, and Pretrial Detention
Explore the impact of algorithms on risk assessment, housing, and pretrial detention, focusing on transparency issues and their societal implications.
AI and Society: Oppression or Innovation?
Experts discuss AI's impact on society, exploring its potential to amplify biases or drive innovation in health, housing, justice, and more. Insights on governing AI and building a hopeful future with emerging technologies.
Configurable Fairness for New Item Recommendation Considering Entry Time - SIGIR 2024
Explore configurable fairness in recommender systems, focusing on new item recommendations and entry time considerations for enhanced algorithmic equity.
Fair Recommendations with Limited Sensitive Attributes - A Distributionally Robust Optimization Approach
Explore distributionally robust optimization for fair recommendations with limited sensitive attributes in RecSys, enhancing fairness in machine learning applications.
Adaptive Fair Representation Learning for Personalized Fairness in Recommendations - Lecture 7
Explore adaptive fair representation learning for personalized fairness in recommendations through information alignment techniques.
CaDRec: Contextualized and Debiased Recommender Model - Fairness in RecSys
Explore a contextualized and debiased recommender model for fair and accurate recommendations in information retrieval systems.
Fair Sequential Recommendation without User Demographics - SIGIR 2024
Explore fair sequential recommendation techniques without relying on user demographics, enhancing ethical AI practices in recommender systems.
Can We Trust Recommender System Fairness Evaluation? The Role of Fairness and Relevance - Evaluation M1.5
Explore the reliability of fairness evaluations in recommender systems, examining the interplay between fairness and relevance metrics for more trustworthy assessments.
Taking Aim at Bias in AI - Workshop Sessions
Explore strategies to identify and mitigate bias in AI systems, enhancing fairness and ethical decision-making in machine learning applications.
MLOps for Fairness - Creating Comprehensive Fairness Workflows
Explore MLOps techniques for identifying, measuring, and mitigating bias in ML systems at scale. Learn to create comprehensive fairness workflows using Python tools for industry-level challenges.
A Taxation Perspective for Fair Re-ranking - SIGIR 2024
Explore a taxation perspective for fair re-ranking in information retrieval, examining fairness considerations and algorithmic approaches.
Counterfactual Ranking Evaluation with Flexible Click Models - Lecture 3
Explore counterfactual ranking evaluation techniques using flexible click models for improved CTR and ad performance in search systems.
Building Fairer AI Systems with Fairlearn - AI Show
Explore Fairlearn, an open-source toolkit for assessing and mitigating fairness issues in AI systems, focusing on potential negative impacts on different demographic groups.
Comment les moteurs de recherche nous manipulent
Découvrez comment les moteurs de recherche influencent nos choix et perceptions. Un expert en référencement révèle les mécanismes cachés et les enjeux de notre dépendance à ces outils omniprésents.