Check out the paper AI Art is Theft: Labour, Extraction, and Exploitation Or, On the Dangers of Stochastic Pollocks Trystan S. Goetze Abstract Since the launch of applications such as DALL•E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. Some writers have presente... >

Check out the paper Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez and Been Kim Abstract As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively ... >

Check out the paper Machine Unlearning Abstract Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because any model trained with said data may have memorized it, putting users at risk of a successful privacy attack ... >

Check out the paper AI Art and its Impact on Artists Abstract The last 3 years have resulted in machine learning (ML)-based image generators with the ability to output consistently higher quality images based on natural language prompts as inputs. As a result, many popular commercial “generative AI Art” products have entered the market, mak... >

Check out the paper Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions Abstract How to properly set the privacy parameter in differential privacy (DP) has been an open question in DP research since it was first proposed in 2006. In this work, we demonstrate the ability of influence functions to offer... >

Check out the paper Probabilistic Dataset Reconstruction from Interpretable Models Check out the slides PDF here. >

Check out the paper Risks From Learned Optimization in Advanced Machine Learning Systems Check out the slides >

Check out the paper Causal Fairness Field Guide: Perspectives from Social and Formal Sciences Causality Chapter from the Fair ML Book >

Check out the paper MIT Technology Review Article Underspecification Presents Challenges for Credibility in Modern Machine Learning Check out the slides PDF here. >

Check out the paper Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification - A. Feder Cooper, K. Lee, M. Zahrah Choksi, S. Barocas, C. De Sa, J. Grimmelmann, J. Kleinberg, S. Sen, B. Zhang Check out the slides PDF here. >

Check out the Story and Short Runaround - Isaac Asimov Code 8 (Youtube) Short Film note: Code 8 was turned into a full length feature (now on Netflix). >

Check out the paper Runaround by Isaac Asimov Check out the slides PDF here. >

Check out the paper Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training - Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, J... >

Check out the paper Model Explanations with Differential Privacy - N. Patel, R. Shokri, and Y. Zick. 2022 Check out the slides PDF here. >

Check out the paper Causal parrots: Large language models may talk causality but are not causal - Zečević, M., Willig, M., Dhami, D. S., & Kersting, K. (2023) Check out the slides PDF here. >

Check out the paper Differentially Private Fair Learning - Matthew Jagielski, Michael Kearns, Jieming Mao, Aaron Oprea, Alina Roth, Saeed Sharifi-Malvajerdi, and Jonathan Ullman Check out the slides PDF here. >

This week we covered basic expectations and topics we hope to cover. Nothing big here! >