馃敩 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
Design
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
馃敩 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
The Moral Machine Experiment on Large Language Models
馃敩 Research Summary by Kazuhiro Takemoto, Professor at Kyushu Institute of Technology. [Original paper by Kazuhiro Takemoto] Overview: Large Language Models (LLMs) are increasingly integrated into autonomous … [Read more...] about The Moral Machine Experiment on Large Language Models
Bias and Fairness in Large Language Models: A Survey
馃敩 Research Summary by Isabel O. Gallegos, a Ph.D. student in Computer Science at Stanford University, researching algorithmic fairness to interrogate the role of artificial intelligence in equitable … [Read more...] about Bias and Fairness in Large Language Models: A Survey
On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise
馃敩 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鈥橞rien, James Rea, Gareth Rees, and Lauren … [Read more...] about On the Challenges of Deploying Privacy-Preserving Synthetic Data in the Enterprise