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AI-driven biometry and the infrastructures of everyday life

#artificialintelligence

Over the past years, we have become witness to the exponentially growing proliferation of biometric technologies: facial recognition technology and fingerprint scanners in our phones, sleep-pattern detection technology on our wrists or speech-recognition software that facilitates auto-dictation such as captioning. What all these technologies do is measure and record some aspect of the human body or its function: facial recognition technology measures facial features, fingerprint scanners measure the distance between the ridges that make up a unique fingerprint, sleep-pattern detection measures movement in our sleep as a proxy for wakefulness, and so on. AI is fundamentally a scaling technology. It is walking in the footsteps of many other technologies that have deployed classification and categorisation in the name of making bureaucratic processes more efficient, from ancient library systems to punch cards, to modern computer-vision technologies that'know' the difference between a house, a road, a vehicle and a human. The basic idea of these scaling technologies is to minimise situations in which individual judgement is required (see also Lorraine Daston's seminal work on rules).


Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence

arXiv.org Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.


Toward Fairness in AI for People with Disabilities: A Research Roadmap

arXiv.org Artificial Intelligence

AI technologies have the potential to dramatically impact the lives of people with disabilities (PWD). Indeed, improving the lives of PWD is a motivator for many state-of-the-art AI systems, such as automated speech recognition tools that can caption videos for people who are deaf and hard of hearing, or language prediction algorithms that can augment communication for people with speech or cognitive disabilities. However, widely deployed AI systems may not work properly for PWD, or worse, may actively discriminate against them. These considerations regarding fairness in AI for PWD have thus far received little attention. In this position paper, we identify potential areas of concern regarding how several AI technology categories may impact particular disability constituencies if care is not taken in their design, development, and testing. We intend for this risk assessment of how various classes of AI might interact with various classes of disability to provide a roadmap for future research that is needed to gather data, test these hypotheses, and build more inclusive algorithms.