Public Interest Technology Colloquium Series
Society Policy Engineering Collective (SPEC) at Arizona State University
7.30 am – 9.00 am (AEST); 2.30 pm – 4.00 pm (MST); 5.30 – 7.00 p.m. (EDT)
When: 11th August, AEST; 10th August MST
Moderator: Salah Hamdoun
- Industry Representative and Author of IP Location: James Winterbottom
- Business Statement: Eusebio Scornavacca
- Technology Statement: Katina Michael
- Problem Identification: Farah Najar Arevalo [Diverge]
- Ways Forward: Martin Perez Comisso [Converge]
Katina Michael on IP Location Services and Automated Biometric Recognition
The human face is a type of biometric that is unique. The physical characteristics of a person’s face, can be used to automatically identify an individual through a process of authentication or verification. Increasingly, facial recognition capabilities are being embedded in closed-circuit television infrastructure (CCTV). Independent of the age of the CCTV camera, software upgrades (i.e. firmware) can now be installed in an older camera, rendering the capability to identify people ‘on the go’ a possibility. Importantly, the resolution of that CCTV camera needs to be at an adequate level, although, different types of accuracy levels are acceptable for different applications (e.g. a person count versus identification of a known person, versus the requirement for forensic identification of someone that is not known to a system, and finally identification in poor conditions (e.g. dimly lit areas).
Automated facial recognition systems can generally produce two kinds of errors, false positives and false negatives, and they are not always foolproof in determining a suspect, for example, when degrees of confidence are set below an exact match threshold or the outdoor conditions are not favorable to the capture of adequate facial imagery. The other problem with open systems is that quality images may not always be capturable in the field, rendering further error rates, as people move their head naturally without standing still and front on, as they go freely about their business.
Some might argue that facial recognition has nothing to do with “location” or for that matter “geography”. But increasingly these static IP-based fixtures are internetworked and embedded either unobtrusively or obtrusively in places where people frequent. Cameras at shopping malls, supermarkets, banks, gas stations are being used to not only identify a person but to corroborate their movements. Path dependent systems, that also make use of a person’s cellphone, can now tell us things about human holding patterns (e.g. the amount of time an individual spends in front of a window shopfront), routing information (how people traverse a shopping mall from store to store), and physical social networks.
CCTV cameras on buildings, smart technologies like lampposts, entry and exit gantry points at transportation hubs, all denote someone’s cross-sectional geographic location stamp and if tied to smart city infrastructure enable data-driven analytics. When we consider the potential for tracking and monitoring individuals in the external environment through specific instrumentation, we are performing what is known as automatic location determination through triangulation. This means if a person is already connected to a Wi-Fi network in a public setting, then dedicated instrumentation can be used to validate that person’s IP location with even greater accuracy, e.g., to the nearest camera on a lamppost, further enhancing knowledge about the mobile citizen. We can even denote a direction of travel in near real-time.
Using elements of speed, distance and time, we can create periodic location chronicles that can indicate where someone has been, based on their physical location having crossed an infrastructural element such as a node on a network, and even where they might be going. Each piece of infrastructure has a physical location, geodetic coordinates and/or derived civic address. By identifying someone between internetworked elements at intervals, we know the speed that they are travelling in and could possibly even infer their condition based on how they look, for example whether someone is frowning, sweating, crying, angry, laughing can now be denoted by their facial expressions. These are not physical characteristics but temporary states of affect that can now be measured and analyzed, providing further details about the condition of an individual. Data-driven innovation for citizen and customer management is now being touted as the way to keep our cities safe and secure.
When a facial image is taken we assume that the requirement is merely for authentication purposes, but increasingly that data which was not essential at capture historically, is now collected and analyzed to denote other things about individuals and surrounding contexts in spaces. This is as a result of advances in computer vision where everything might be analogized as maintaining certain physical properties or dedicated characteristics. Machine learning algorithms, at least statistically, grant us the ability to automatically identify a “thing” (living or non-living), allowing for a variety of representations and data integration from disparate information sources. For example, image and video analysis can now detect person density in a field of view, in addition to recurring apparatus held by individuals (e.g. umbrellas or weapons), smoke rising, and much more. This is further amplified when we consider the introduction of additional sensors: audio (measuring noise levels), temperature (identifying fire hazards), air/smoke (identifying emergencies), for odor (e.g. detecting gaseous chemical accidents) etc.
Finally, we must acknowledge that our very location movements are in and unto themselves biometric patterns, and this may indeed be the most important challenge of them all when it comes to being uniquely identified in the context of living in an age of uberveillance. Corroborated with automated facial recognition systems, one is left wondering what the possibilities might mean for our human rights and long-term freedoms as individuals.
ASU PIT Colloquium / IEEE SSIT Students / AAG Pre-Event
When: 11 August AEST; 10 August EDT
Salah Hamdoun (MC) is a doctoral student in the Innovation in Global Development (IGD) Program at the School for the Future of Innovation in Society at Arizona State University. His research interests lie in the area of financial technology and human development. Specifically, Salah’s work focuses on the power dynamics and relationships within societies and the impact of financialization on the social structure. He has over ten years of professional experience in money markets, financial derivatives and in alternative investments. He started his career at ABN Amro Bank and has worked for institutions such as Standard Chartered Bank and Mashreqbank. Salah is currently the Administrator of the IEEE Transactions on Technology and Society.
James Winterbottom has been involved in location solutions for use in emergency services since 2000. James started work in cellular location system for 9-1-1 in the United States before helping to pioneer location and routing architectures for Next Generation emergency solutions based around IP calling devices. Since then, he has gone on to lead the specification, development and rollout of the PEMEA network in Europe. PEMEA is a solution that allows all forms of communications Apps to roam and provide connectivity to Emergency services, with a focus on improved and equivalent access for people with disabilities. James is currently the Chief Architect of Product Manger for the Emergency Solutions product portfolio at Deveryware.
Eusebio Scornavacca is Professor at the School for the Future of Innovation in Society, College of Global Futures, and Thunderbird School of Global Management at Arizona State University (ASU). Prior to joining ASU, Prof. Scornavacca was the Parsons Professor of Digital Innovation and Director of the Center for Digital Communication, Commerce and Culture at the University of Baltimore. Eusebio is a truly global scholar with a strong research collaboration network across six continents. He has held visiting positions in Japan, China, Egypt, Italy, France, Finland, Spain, New Zealand, Morocco, and Brazil. His research interests include disruptive digital innovation, high-impact innovation, digital entrepreneurship, ICT for development, and digital ecosystems. During the past 20 years, he has conducted multidisciplinary research using qualitative and quantitative methods in a wide range of industries, including research sponsored by the private sector. Professor Scornavacca’s research has appeared in journals such as the Journal of Information Technology, Communications of the ACM, Decision Support Systems, Communications of the AIS, Information & Management, Computers in Human Behavior, Journal of Computer Information Systems and Television & New Media. He has held several editorial positions and served as a track chair at leading conferences.
Katina Michael is a professor at Arizona State University, holding a joint appointment in the School for the Future of Innovation in Society and School of Computing and Augmented Intelligence. She is also the director of the Society Policy Engineering Collective (SPEC) and the Founding Editor-in-Chief of the IEEE Transactions on Technology and Society. Before Katina came into academia she spent a significant period in industry. In her last full-time industry role Katina was a senior network and business planner for the Network and Systems Solutions team, a pre-sales engineering arm of Nortel Networks. Katina was one of the first academics in the world to introduce a course dedicated to Location-Based Services together with Andrew Corporation in 2004 while at the University of Wollongong. She also was the program director of the IP Location Services research center at the University of Wollongong between 2004-2006; a center funded by industry and dedicated to the end-to-end architecture of location-based services on the ISO stack with an emphasis on emergency management and commercial location information service offerings. For more visit: www.katinamichael.com
Martin Perez Comisso is a graduate student in the PhD program Human and Social Dimensions of Science and Technology, in the School for the Future of Innovation in Society in the College of Global Futures, Arizona State University. He has a Master in Chemistry from the Universidad de Chile, a diploma in Quality Management and Leadership and a Bachelor in Chemistry from the same institution. He has taught many courses in diverse areas of STEM in English and Spanish.
Farah Najar Arevalo is a graduate student in the PhD program Innovation in Global Development College of Global Futures, in the School for the Future of Innovation in Society in the College of Global Futures, Arizona State University. She is a research associate at the Center for Smart Cities.