La Era de la Inteligencia Artificial Responsable
Reflexión sobre los desafíos éticos de la IA en la vida cotidiana, abordando riesgos como sesgos algorítmicos y noticias falsas, y la necesidad de un enfoque responsable en su desarrollo y aplicación.
Beyond Bias Audits: Bringing Equity to the Machine Learning Pipeline
Explorez les approches pour intégrer l'équité dans l'apprentissage automatique appliqué aux soins de santé, en abordant les biais algorithmiques et les disparités en matière de santé.
Race to the Future? Reimagining the Default Settings of Technology and Society
Explores how technology can perpetuate discrimination through biased designs. Examines the "New Jim Code" and challenges us to critically assess tech promises and their societal impacts.
Privacy-Preserving, Fair, and Robust Machine Learning Models in Online Dating
Explore privacy-preserving, fair, and robust machine learning models in online dating apps. Learn about data privacy challenges and innovative solutions for protecting personal information while ensuring effective matchmaking.
Keynote on Computing and Social Justice - FAccT 2024
Explore the intersection of technology, ethics, and society with Seeta Peña Gangadharan's thought-provoking keynote address on algorithmic fairness and accountability in the digital age.
Storytelling for a More Equitable Open Science Enterprise
Exploring storytelling's role in open science communication, emphasizing trust-building, public engagement, and addressing ethical implications of emerging technologies for more equitable scientific practices.
Fairness in Machine Learning with Tulsee Doshi
Explore ML fairness principles, concerns, and mitigation strategies. Learn about Google's approach to inclusive AI development and tools for evaluating and improving fairness in machine learning models.
Bias in Big Data and Artificial Intelligence - Protecting Against Algorithmic Discrimination
Explore the impact of algorithmic bias in AI and big data on marginalized communities, discussing ethical standards, regulation, and strategies for protecting against discrimination.
Fintech - Great Enabler or Great Divider?
Explore fintech's impact on financial inclusion, examining innovations, risks, and strategies to bridge the digital divide and empower the unbanked in an increasingly digital economy.
Fintech - Great Enabler or Great Divider?
Explore fintech's impact on financial inclusion, examining innovations, risks, and strategies to bridge the digital divide and empower the unbanked in an increasingly digital economy.
Designing Machine Learning Processes for Equitable Health Systems
Explore machine learning processes for equitable health systems with MIT professor Dr. Marzyeh Ghassemi. Learn about innovative approaches to improve healthcare through AI and data science.
Artificial Intelligence in Forensic Sciences - Boon or Curse
Exploring AI's impact on forensic sciences: opportunities, challenges, and ethical considerations. Discusses potential benefits, risks, and the need for responsible development and implementation.
Algorithmic Bias in Human Resources: Responsible AI and Business Strategy
Explore algorithmic bias in HR, responsible AI, and business strategy. Unpack root causes, identify symptoms, and develop strategies for responsible HR algorithms and AI projects.
Trustworthy Deep Learning
Explore robust and trustworthy deep learning, covering algorithmic bias, uncertainty estimation, and innovative approaches to create risk-aware AI systems for more reliable and ethical machine learning applications.
Doing Good by Fighting Fraud - Ethical Anti-Fraud Systems for Mobile Payments
Explore ethical anti-fraud systems for mobile payments, addressing algorithmic bias and user challenges while examining key metrics and design principles for effective fraud detection.
AI Explainability 360 - First Half of the Tutorial
Explore AI Explainability 360 toolkit: understand machine learning models, enhance transparency, and gain insights into AI decision-making processes for more responsible and ethical AI applications.
What Public Interest AI Auditors Can Learn from Security Testing - Legislative and Practical Wins
Exploring legal and practical strategies for AI auditors to overcome barriers in testing algorithmic bias, drawing lessons from security testing practices and advocating for policy changes.
Counterfactual Fairness
Explore causal inference tools for modeling fairness in machine learning, addressing bias and ensuring equitable decisions across demographic groups.
Machines and Morality - Ethical Implications of AI in Society
Explore ethical implications of AI, examining biases in algorithms, potential harmful outcomes, and the importance of instilling proper values in machine learning systems.
Emotions and Technology - The Impact of AI on Human Interaction and Mental Health
Explore the intersection of emotions and technology, including AI emotion recognition, mental health applications, and the impact of digital communication on social connections.
Fairness in Commercial Face Recognition Algorithms - Session 3
Explore fairness issues in commercial facial recognition systems, examining biases and ethical implications for diverse populations.