The FHIBE Team: Data, Dignity, and the People Who Made It Possible
AI runs on data, but too often, that data has been scraped without consent, assembled without care, and used without accountability. The consequences...
Introducing FHIBE: A Consent-Driven Benchmark for AI Fairness Evaluation
Why Fairness Needs a Better Approach Building AI that works fairly across people, places, and contexts globally requires data that represents real...
What If Fairness Started at the Dataset Level?
At Sony AI, we believe ethical AI starts with the inputs. And that means reexamining how datasets are collected and shared. Our research has...
Sony AI Ethics Flagship: Reflecting on Our Progress and Purpose
Since Sony AI’s inception, ethics has been at the heart of everything we do. The establishment of our AI Ethics Research Flagship at the beginning of...
Exploring the Challenges of Fair Dataset Curation: Insights from NeurIPS 2024
Sony AI’s paper accepted at NeurIPS 2024, "A Taxonomy of Challenges to Curating Fair Datasets," highlights the pivotal steps toward achieving...
Mitigating Bias in AI Models: A New Approach with TAB
Artificial intelligence models, especially deep neural networks (DNNs), have proven to be powerful tools in tasks like image recognition and natural...
Ushering in Needed Change in the Pursuit of More Diverse Datasets
Sony AI, Research Scientist, Jerone Andrews’ paper, "Measure Dataset Diversity, Don't Just Claim It", has won a Best Paper Award at ICML 2024. This...
When Privacy and Fairness Collide: Reconciling the Tensions Between Privacy and Representation in the Age of AI
Invisibility is sometimes thought of as a superpower. People often equate online privacy with selective invisibility, which sounds desirable because...
Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color
Advancing Fairness in Computer Vision: A Multi-Dimensional Approach to Skin Color Analysis