Subcategories of Emerging Technology
The Tech Policy Lab is committed to advancing artificial intelligence and robotics in the public interest through research, analysis, and education and outreach.
The Internet of Things (IoT) has brought a wave of new devices into our homes that are always connected. Our work focuses on technical, societal, and policy questions for these devices.
Digital technologies require material resources — such as metals and plastics — to realize form and function, yet, the materiality of digital technologies is all too often invisible. This UW Tech Policy Lab project takes up this issue by investigating the materiality of near and longer-term technology visions and exploring tech policy directions responsive to considerations of materiality.
Computers are now integrating into everyday objects, from medical devices to children’s toys. Most consumers are unaware that these devices are constantly collecting, storing, or disclosing their personal information. Bringing these connected devices into the home particularly raises important issues of privacy and security.
Modern DNA sequencing techniques can sequence hundreds of millions of DNA strands simultaneously. Computers are needed to process, analyze, and store the billions of DNA bases that can be sequenced from a single DNA sample. New and unexpected interactions may be possible at this boundary between electronic and biological systems.
The Lab is interested in the social impacts of the Internet of Things (IoT) and has begun a series of projects focusing on how the introduction of IoT devices impacts individual’s mental models and the psychological environment. Different than the smartphone, these devices are always on, blending into the background until needed by the adult or child user. We are working to develop best practices for toys and devices in the home that are connected to the internet.
New Research: Data Statements
Experts in information science and computational linguistics propose data statements as a design solution and professional practice for natural language processing technologists to help mitigate issues related to exclusion and bias in Data Statements for NLP: Towards Mitigating System Bias and Enabling Better Science. Read the full paper here. This paper was published in the Transactions of the Association for Computational Linguistics.More
New Research: Adversarial Machine Learning
Last fall, a team of researchers with the Lab’s Ivan Evtimov, Earlence Fernandes, and Co-Director Yoshi Kohno shared research on ArXiv showing that malicious alterations to real world objects could cause devices to “misread” the image. Now, the team of researchers presented two papers updating this research – one at Computer Vision and Pattern Recognition (CVPR) 2018 and another at the 12th USENIX Workshop on Offensive Technologies (WOOT) 2018.More