Reflections on trustworthy and ethical technology from a Human Computer Interaction perspective.
The objective to help people flourish has been a part of the agenda of the Human Computer Interaction community since its early days. Current concerns around the impacts of technology and its ethics make these early endeavours even more relevant and prominent. The reasons are many and relate to issues of broad societal concern such as sustainability, work organisation and perpetration of social inequities. In this talk I will first discuss emerging attributes that help to assess a technology as trustworthy and ethical. I will then draw on some examples of projects we have carried out in our industrial lab to explain how we included a value orientation in our research and will propose some concrete methodologies we have found useful.
M. Antonietta Grasso is Principal Scientist at Naver Labs Europe in the AI for Robotics group. Prior to that she has been leading the Work Practice Technology team at the Xerox Research Centre Europe. She has been an early member of the Computer Supported Cooperative Work and Human Computer Interaction communities, studying a variety of collaborative settings, mainly in the work environment. Interested in novel interfaces, her concern has always been on how to design those to mediate and reconcile the various service stakeholder needs.
She has authored more than 50 peer reviewed articles and patents, and serves on a variety of scientific committees in the field.
Towards Distributed Intelligence in Future Edge Computing
The emerging advanced IoT applications in connected healthcare, industrial internet, multi-robot systems, and other areas demand higher intelligence of the connected devices, larger scale of the systems, and better decision making leveraged by analyzing the data being continuously generated and the advancement of AI technologies. In this context, centralized cloud computing would face high data transmission cost, high response time, and data privacy issues. The edge cloud paradigm seeks to alleviate these inefficiencies by moving the computation and analytics tasks closer to the end devices. It facilitates the evolution of IoT from instrumentation and interconnection to distributed intelligence. This talk focuses on future collaborative edge computing where edge nodes share data and computation resources and perform tasks by leveraging distributed intelligence. It covers the major problems in distributed collaboration at the edge we are currently studying, namely collaborative task execution, distributed machine learning, and distributed autonomous cooperation. Solutions need to address the challenging issues such as distributed data sources, conflicting network flows, heterogeneous devices, consistency, and mutual influence during the training.
Dr. Cao is the Otto Poon Charitable Foundation Professor in Data Science and the Chair Professor of Distributed and Mobile Computing in the Department of Computing at The Hong Kong Polytechnic University. He is the Dean of Graduate School, director of Research Institute for AIoT, director of Internet and Mobile Computing Lab and the associate director of University’s Research Facility in Big Data Analytics. He served the department head from 2011 to 2017.
Dr. Cao’s research interests include parallel and distributed computing, wireless networking and mobile computing, big data and machine learning, and cloud and edge computing. He published 5 co-authored and 9 co-edited books, and over 500 papers in major international journals and conference proceedings. He also obtained 13 patents. Dr. Cao received many awards for his outstanding research achievements. He is a member of Academia Europaea, a fellow of IEEE and a distinguished member of ACM. In 2017, he received the Overseas Outstanding Contribution Award from China Computer Federation.
Opportunistic Collaborative Learning in Pervasive Computing Applications
Smartphones, wearable devices, and other computational units that are ubiquitous in our environments are imbued with increasingly more complex sensing, computational, and communication capabilities. These devices can generate (and distribute) vast quantities of data that can be used to build sophisticated machine learning models for a variety of applications, e.g., classification and recommendation. Opportunistic collaborative learning (OppCL) is a framework for individual devices in pervasive computing environments to train a deep learning model that caters to the device’s personalized needs. In OppCL, each device maintains a local, personalized model. When the device encounters another device via peer-to-peer communication, it shares its model parameters and asks the neighbor to train the model using the neighbor’s local data. This talk will present the motivation and use cases behind the creation of OppCL and a basic model for collaboratively training personalized models using opportunistically available neighboring devices (and their data!). The talk will discuss multiple schemes for incorporating encountered model updates as well as techniques for handling heterogeneity in the pervasive computing environment, including bandwidth and latency constrained communication links as well as computationally constrained neighboring devices. The talk will also include presentations of practical implementations for OppCL in both large scale simulation and in real world devices. The talk will close with a look forward into open challenges and opportunities in employing OppCL to diverse pervasive computing applications.
Dr. Christine Julien is a professor in the Department of Electrical and Computer Engineering at the University of Texas at Austin. She is also the Associate Dean for Diversity, Equity, and Inclusion in the Cockrell School of Engineering. She is the director of the Mobile and Pervasive Computing Group, where her research focuses on the intersection of software engineering and dynamic, unpredictable networked environments. Her specific focus is on the development of models, abstractions, tools, and middleware whose goals are to ease the software engineering burden associated with building applications for pervasive and mobile computing environments. Dr. Julien’s research has been supported by the National Science Foundation (NSF), the Air Force Office of Scientific Research (AFOSR), the Department of Defense, and Google.