The AI Privacy 360 Toolbox includes several tools to support the assessment of privacy risks of AI-based solutions, and to help them adhere to any relevant privacy requirements. Tradeoffs between privacy, accuracy, and performance can be explored at different stages in the ML lifecycle.
Source: IBM
Despite recent efforts by the Artificial Intelligence (AI) community to move towards standardised procedures for documenting models, methods, systems or data...
The first 100 Brilliant Women in AI Ethics list was published in 2018 and since then, we’ve published a list every year to recognize the diverse voices toili...
The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its v...
Trained machine learning models are increasingly used to perform high-impact tasks in areas such as law enforcement, medicine, education, and employment. In ...
