Zobrazeno 1 - 10
of 160
pro vyhledávání: '"Hullman, Jessica"'
Differential privacy (DP) is a mathematical definition of privacy that can be widely applied when publishing data. DP has been recognized as a potential means of adhering to various privacy-related legal requirements. However, it can be difficult to
Externí odkaz:
http://arxiv.org/abs/2409.11680
Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model check
Externí odkaz:
http://arxiv.org/abs/2408.16702
Autor:
Musslick, Sebastian, Bartlett, Laura K., Chandramouli, Suyog H., Dubova, Marina, Gobet, Fernand, Griffiths, Thomas L., Hullman, Jessica, King, Ross D., Kutz, J. Nathan, Lucas, Christopher G., Mahesh, Suhas, Pestilli, Franco, Sloman, Sabina J., Holmes, William R.
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery,
Externí odkaz:
http://arxiv.org/abs/2409.05890
Research in Responsible AI has developed a range of principles and practices to ensure that machine learning systems are used in a manner that is ethical and aligned with human values. However, a critical yet often neglected aspect of ethical ML is t
Externí odkaz:
http://arxiv.org/abs/2408.10239
The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between real photographs and AI-generated images. To address this problem, we de
Externí odkaz:
http://arxiv.org/abs/2406.08651
Autor:
Nanayakkara, Priyanka, Kim, Hyeok, Wu, Yifan, Sarvghad, Ali, Mahyar, Narges, Miklau, Gerome, Hullman, Jessica
Publikováno v:
in 2024 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2024 pp. 231-231
Differential privacy (DP) has the potential to enable privacy-preserving analysis on sensitive data, but requires analysts to judiciously spend a limited ``privacy loss budget'' $\epsilon$ across queries. Analysts conducting exploratory analyses do n
Externí odkaz:
http://arxiv.org/abs/2406.01964
Data sonification-mapping data variables to auditory variables, such as pitch or volume-is used for data accessibility, scientific exploration, and data-driven art (e.g., museum exhibitions) among others. While a substantial amount of research has be
Externí odkaz:
http://arxiv.org/abs/2402.00156
Humans frequently make decisions with the aid of artificially intelligent (AI) systems. A common pattern is for the AI to recommend an action to the human who retains control over the final decision. Researchers have identified ensuring that a human
Externí odkaz:
http://arxiv.org/abs/2401.15356
How well people use information displays to make decisions is of primary interest in human-centered AI, model explainability, data visualization, and related areas. However, what constitutes a decision problem, and what is required for a study to est
Externí odkaz:
http://arxiv.org/abs/2401.15106
As deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging. We investigate the presentation of conformal prediction sets--a distribution-free class of methods for gen
Externí odkaz:
http://arxiv.org/abs/2401.08876