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Human Computer Interaction: Overviews


Differential Privacy for Eye Tracking with Temporal Correlations

arXiv.org Machine Learning

Head mounted displays bring eye tracking into daily use and this raises privacy concerns for users. Privacy-preservation techniques such as differential privacy mechanisms are recently applied to the eye tracking data obtained from such displays; however, standard differential privacy mechanisms are vulnerable to temporal correlations in the eye movement features. In this work, a transform coding based differential privacy mechanism is proposed for the first time in the eye tracking literature to further adapt it to statistics of eye movement feature data by comparing various low-complexity methods. Fourier Perturbation Algorithm, which is a differential privacy mechanism, is extended and a scaling mistake in its proof is corrected. Significant reductions in correlations in addition to query sensitivities are illustrated, which provide the best utility-privacy trade-off in the literature for the eye tracking dataset used. The differentially private eye movement data are evaluated also for classification accuracies for gender and document-type predictions to show that higher privacy is obtained without a reduction in the classification accuracies by using proposed methods.


Artificial intelligence in medicine and healthcare: a review and classification of current and near-future applications and their ethical and social Impact

arXiv.org Artificial Intelligence

This paper provides an overview of the current and near-future applications of Artificial Intelligence (AI) in Medicine and Health Care and presents a classification according to their ethical and societal aspects, potential benefits and pitfalls, and issues that can be considered controversial and are not deeply discussed in the literature. This work is based on an analysis of the state of the art of research and technology, including existing software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. Motivated by our review, we present and describe the notion of 'extended personalized medicine', we then review existing applications of AI in medicine and healthcare and explore the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks that even question fundamental medical concepts. Many of these topics coincide with urgent priorities recently defined by the World Health Organization for the coming decade. In addition, we study the transformations of the roles of doctors and patients in an age of ubiquitous information, identify the risk of a division of Medicine into 'fake-based', 'patient-generated', and 'scientifically tailored', and draw the attention of some aspects that need further thorough analysis and public debate.


A Review of Another 5 Major Tech Trends In 2020

#artificialintelligence

In our recent blog, we covered some exciting tech trends hitting 2020 such as autonomous driving, hyperautomation and more. There are many areas however with even more developments, ones you may have heard of and ones that you may have not. Technology is accelerating at such a rapid pace that every industry will be affected as well as the everyday consumer. We examine a further 5 top tech trends hitting our doors in 2020. At this stage, we all know or have at least heard of the cloud.


Enabling Value Sensitive AI Systems through Participatory Design Fictions

arXiv.org Artificial Intelligence

Two general routes have been followed to develop artificial agents that are sensitive to human values---a top-down approach to encode values into the agents, and a bottom-up approach to learn from human actions, whether from real-world interactions or stories. Although both approaches have made exciting scientific progress, they may face challenges when applied to the current development practices of AI systems, which require the under-standing of the specific domains and specific stakeholders involved. In this work, we bring together perspectives from the human-computer interaction (HCI) community, where designing technologies sensitive to user values has been a longstanding focus. We highlight several well-established areas focusing on developing empirical methods for inquiring user values. Based on these methods, we propose participatory design fictions to study user values involved in AI systems and present preliminary results from a case study. With this paper, we invite the consideration of user-centered value inquiry and value learning.


AI & New Retail: Recent Developments and Future Trends

#artificialintelligence

The traditional retail industry is facing challenges as the rapid development and continuous improvement of AI tools and techniques ushers in the era of New Retail. Many once-successful brick and mortar shops are at risk of disappearing altogether if they fail to adapt to the changing marketplace. As the concept of "New Retail" spreads globally, many Fortune Global 500 retail companies are implementing AI technologies such as computer vision and NLP in unmanned stores and warehouses or virtual stores, and are accelerating supply chain upgrades to better position themselves for the future of shopping. Global Retail Industry Market Size The steady growth of the global economy and per capita disposable income have triggered a surge in the retail industry over the past few years. The global retail industry market was valued at nearly US $23.5 trillion in 2017, a 5.3 percent year-on increase.


AI & New Retail: Recent Developments and Future Trends

#artificialintelligence

The traditional retail industry is facing challenges as the rapid development and continuous improvement of AI tools and techniques ushers in the era of New Retail. Many once-successful brick and mortar shops are at risk of disappearing altogether if they fail to adapt to the changing marketplace. As the concept of "New Retail" spreads globally, many Fortune Global 500 retail companies are implementing AI technologies such as computer vision and NLP in unmanned stores and warehouses or virtual stores, and are accelerating supply chain upgrades to better position themselves for the future of shopping. Global Retail Industry Market Size The steady growth of the global economy and per capita disposable income have triggered a surge in the retail industry over the past few years. The global retail industry market was valued at nearly US $23.5 trillion in 2017, a 5.3 percent year-on increase.


Currents Trends in IT

#artificialintelligence

Given that we are currently in the age of digital transformation, technological trends stand out among all others. The digital mesh will consist of automated devices, robots, humans, services and content, all driven by disruptive technological trends. We are here to discuss the top trends of IT in 2019 that are going to shape the future of business operations for the rest of the year. Artificial intelligence (AI) essentially involves harnessing the power of algorithms and machine learning to teach machines to identify, comprehend and mimic human behavior. Though it has been around for quite some time, this year we have witnessed AI combined with machine learning (ML) entering into the business platform to enable smart business operations.


OpenEDS: Open Eye Dataset

arXiv.org Machine Learning

We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabelled eye-images, (iii) 91,200 frames from randomly selected video sequence of 1.5 seconds in duration and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study. A baseline experiment has been evaluated on OpenEDS for the task of semantic segmentation of pupil, iris, sclera and background, with the mean intersectionover-union (mIoU) of 98.3 %. We anticipate that OpenEDS will create opportunities to researchers in the eye tracking community and the broader machine learning and computer vision community to advance the state of eye-tracking for VR applications. The dataset is available for download upon request at https://research.fb.com/programs/openeds-challenge


Differential Privacy for Eye-Tracking Data

arXiv.org Artificial Intelligence

As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that aggregating individuals' data into composite representations such as heatmaps protects the individual. However, we analytically examine the privacy of (noise-free) heatmaps and show that they do not guarantee privacy. We further propose two noise mechanisms that guarantee privacy and analyze their privacy-utility tradeoff. Analysis reveals that our Gaussian noise mechanism is an elegant solution to preserve privacy for heatmaps. Our results have implications for interdisciplinary research to create differentially private mechanisms for eye tracking.


Augmented Utilitarianism for AGI Safety

arXiv.org Artificial Intelligence

In the light of ongoing progresses of research on artificial intelligent systems exhibiting a steadily increasing problem-solving ability, the identification of practicable solutions to the value alignment problem in AGI Safety is becoming a matter of urgency. In this context, one preeminent challenge that has been addressed by multiple researchers is the adequate formulation of utility functions or equivalents reliably capturing human ethical conceptions. However, the specification of suitable utility functions harbors the risk of "perverse instantiation" for which no final consensus on responsible proactive countermeasures has been achieved so far. Amidst this background, we propose a novel socio-technological ethical framework denoted Augmented Utilitarianism which directly alleviates the perverse instantiation problem. We elaborate on how augmented by AI and more generally science and technology, it might allow a society to craft and update ethical utility functions while jointly undergoing a dynamical ethical enhancement. Further, we elucidate the need to consider embodied simulations in the design of utility functions for AGIs aligned with human values. Finally, we discuss future prospects regarding the usage of the presented scientifically grounded ethical framework and mention possible challenges.