Designed To Fit

Networking; past present and future; and the importance of personality

Many applications benet from user location data, but lo- cation data raises privacy concerns. Anonymization can protect privacy, but identities can sometimes be inferred from supposedly anonymous data. This paper studies a new attack on the anonymity of location data. We show that if the approximate locations of an individual's home and workplace can both be deduced from a location trace, then the median size of the individual's anonymity set in the U.S. working population is 1, 21 and 34,980, for locations known at the granularity of a census block, census track and county respectively. The location data of people who live and work in dierent regions can be re-identied even more easily. Our results show that the threat of re-identication for location data is much greater when the individual's home and work locations can both be deduced from the data. To preserve anonymity, we oer guidance for obfuscating location traces before they are disclosed.

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This paper presents a context-aware mobile recommender system, codenamed Magitti. Magitti is unique in that it infers user activity from context and patterns of user behavior and, without its user having to issue a query, automatically generates recommendations for content matching. Extensive field studies of leisure time practices in an urban setting (Tokyo) motivated the idea, shaped the details of its design and provided data describing typical behavior patterns. The paper describes the fieldwork, user interface, system components and functionality, and an evaluation of the Magitti prototype.

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All visions of the future contain images of pervasive screens throughout the lives of users: Days Made of Glass, Minority Report, Blade Runner, even Apple’s Knowledge Navigator of 1987. Research centers are creating technologies for autoscopic 3D, flexible displays, augmented reality, responsive media, immersive googles and domes, inexpensive pervasive displays, and all running at 8K resolution or higher. How do we separate the hype from the reality of these visions? Which of these innovations will users reject, like 3D TV, which will take off, and how can we decide which innovations to design for? Technology advocates often compare media technology innovations to the change from black-and-white to color TV, but when is that characterization fair and when is it overstatement? In this presentation, I’ll present a case study in the field of responsive media, called Responsive Mirror, and apply lessons learned from that to anticipate the fates of today’s hot topics in visions of tomorrow’s ubiquitous media. Target Audience: Innovators of new products and services, particularly using new media technologies such as 3D, Augmented Reality, Responsive Media, and more. Benefit for Participants: 1. A taxonomy of media technology visions: terminology and categories. 2. Lessons learned from deployment of media-based technologies. 3. A framework for identifying likely adoption of novel media experiences.

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Jesse Holmes April 1st 2016 Professor Linda Beach IFSM 304: Ethics in Information Technology Paper B1 Privacy Related Matrix Introduction Everyone has a right to privacy and the advancement in the IT industry has brought a lot of advantages to organizations and even individuals but one of the major demerits is the issue of privacy. For example, a cell phone is a device that people use to connect with friends and family but security agencies can easily use the device to track your location. In this assignment, I will create a matrix to map key organizational ethical issues and how these issues are affected by law, regulations and policies (Elshtain, &Decosse, 2006). Ethical Dilemma Matrix: Ethical dilemma: Installing cameras at workplace to monitor employees’ actions Stakeholders Finality Autonomy Tolerance 1: The employees The employees have a responsibility to take action that override the demands of the organization. If they feel that a particular action is invading their privacy as having surveillance cameras then that should be final. Autonomy is the duty to maximize the individual’s right to make his or her own decisions. Employees have the right to enjoy their freedom at workplace as long as it is within the limits of the law. Employees should also respect the privacy of their employers in order to bring harmony at work. Employees do not like to be monitored and therefore, employers should respect that. The employees also have the responsibility to understand and to accept the perspective of the employers if reasons to do so are warranted. Employers are interested in ensuring that employees utilize their work time to the maximum rather than spending it doing other things at workplace. 2: The employer Also the employers have the accountability to take action that override the demands of the workforce. Their decision to have surveillance cameras at workstations should be final because their aim is to monitor work actions and not personal behavior. Employers have the duty to allow employees to make their own decisions regarding the issue of camera surveillance at their workstations. On the other hand, this principle works vice versa where employees also have the accountability to let employers make their own decisions without interference because they are the job providers and they feel that if they don’t monitor workers they will do other things apart from work which will lead to losses. The employers have the responsibility to understand the employees’ point of view. Research shows that workers who feel they are always being monitored do not perform well and this ends up lowering an organization’s productivity. Employers have to understand workers perspective if their reasons are warranted. On the other hand, workers who are being monitored are always on point and finish their works early and therefore, may increase an organization’s productivity and that is why this is an ethical dilemma. 3: The Board of Directors The .

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Presented at PodCamp Cleveland at the Cuyahoga Valley Career Center in Brecksville, Ohio on April 29, 2011 by Carol Smith of Midwest Research, LLC. The gap between a good design and a great one can be bridged by understanding your users. In this presentation find out the basics of usability and user experience. Learn cheap and easy techniques to find out more about your users and improve your audience's experience. Effective visuals will be introduced that can help you remember and share what you learn.

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Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. This tutorial presents an overview of algorithmic bias / discrimination issues observed over the last few years and the lessons learned, key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We will motivate the need for adopting a "fairness by design" approach (as opposed to viewing algorithmic bias / fairness considerations as an afterthought), when developing machine learning based models and systems for different consumer and enterprise applications. Then, we will focus on the application of fairness-aware machine learning techniques in practice by presenting non-proprietary case studies from different technology companies. Finally, based on our experiences working on fairness in machine learning at companies such as Facebook, Google, LinkedIn, and Microsoft, we will present open problems and research directions for the data mining / machine learning community. Please cite as: Sarah Bird, Ben Hutchinson, Krishnaram Kenthapadi, Emre Kiciman, and Margaret Mitchell. Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned. WSDM 2019.

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