Venue
- IJCAI 2023
Date
- 2023
Reducing Communication for Split Learning by Randomized Top-k Sparsification
Fei Zheng*
Chaochao Chen*
Binhui Yao*
* External authors
IJCAI 2023
2023
Abstract
The EU AI Act proposal addresses, among other applications, AI systems that enable facial classification and emotion recognition. As part of previous work, we have investigated how citizens deliberate about the validity of AI-based facial classifications in the advertisement and the hiring contexts (N= 3745). In our current research, we extend this investigation by collecting laypeople’s ethical evaluations of facial analysis AI in Japan, Argentina, Kenya and the United States (N~ 4000). Our project serves as a motivation to ask how such cross-cultural AI ethics perspectives can inform EU policymaking regarding AI systems, which enable facial classification and emotion recognition. We refer to suggestions on achieving policy impact and aim to discuss this topic space with workshop participants.
Related Publications
The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Federated learning (FL) further enhances fine-tuning in a privacy-aware manner by utilizing clients'…
Federated learning, a pioneering paradigm, enables collaborative model training without exposing users’ data to central servers. Most existing federated learning systems necessitate uniform model structures across all clients, restricting their practicality. Several methods …
With increasing privacy concerns in artificial intelligence, regulations have mandated the right to be forgotten, granting individuals the right to withdraw their data from models. Machine unlearning has emerged as a potential solution to enable selective forgetting in model…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.