🔬 Research Summary by Oana Inel, a Postdoctoral Researcher at the University of Zurich, where she is working on responsible and reliable use of data and investigating the use of explanations to provide transparency for … [Read more...] about Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection
Technical Methods
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
🔬 Research Summary by Edward Small, a Ph.D. candidate in computer science at the Royal Melbourne Institute of Technology with his research focused on fair and explainable artificial intelligence. [Original paper … [Read more...] about Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
From Instructions to Intrinsic Human Values – A Survey of Alignment Goals for Big Models
🔬 Research Summary by Jing Yao, a researcher at Microsoft Research Asia, working on AI value alignment, interpretability and societal AI. [Original paper by Jing Yao, Xiaoyuan Yi, Xiting Wang, Jindong Wang, and … [Read more...] about From Instructions to Intrinsic Human Values – A Survey of Alignment Goals for Big Models
On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise
🔬 Research Summary by Lauren Arthur, Marketing Director at Hazy, a leading synthetic data company. [Original paper by Georgi Ganev, Jason Costello, Jonathan Hardy, Will O’Brien, James Rea, Gareth Rees, and Lauren … [Read more...] about On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise
Listen to What They Say: Better Understand and Detect Online Misinformation with User Feedback
🔬 Research Summary by Hubert Etienne, a researcher in AI ethics, the former Global Generative AI Ethics Lead at Meta and the inventor of Computational philosophy. [Original paper by Hubert Etienne and Onur … [Read more...] about Listen to What They Say: Better Understand and Detect Online Misinformation with User Feedback