Blame It on the Machine: A Socio-Legal Analysis of Liability in an AI World

As technology continues to evolve, interactions between humans and artificial intelligence (“AI”) will skyrocket. It is important to understand the impact AI can have on society, as well as the potential harm and subsequent liability that could result, and to develop best practices designed to address them. The U.S. needs a comprehensive framework to govern the design, creation, use and risks associated with AI. At the time of this writing, no such framework has been implemented. This article takes a socio-legal, interdisciplinary approach to explore ideas on socio-ethical concerns and theories of liability related to AI, and applies a sociological perspective to assess existing legal frameworks that currently govern human-AI interaction. By adopting an interdisciplinary approach, this article seeks to encourage holistic and robust dialogue about how AI could be developed and operated, hoping that humans and AI can coexist harmoniously. It also proposes a framework to regulate such development in the U.S. There are a few limitations in this article. First, due to the accelerated pace of technological change, the future state of AI will be different from the current state. Hence, the framework proposed in this article might eventually become obsolete. Second, this article is derived from secondary sources and, although the information collected includes rich empirical data, no primary data was generated other than the authors’ views. Third, only specific aspects of AI were selected for analysis – there are other factors in policy, sociology and law that are not addressed. Lastly, this article is primarily focused on Western cultures, North America and Europe in particular; hence, it might not be applicable globally

Lp-stability (1 less than or equal to p less than or equal to infinity) of multivariable nonlinear time-varying feedback systems that are open-loop unstable

A class of multivariable, nonlinear time-varying feedback systems with an unstable convolution subsystem as feedforward and a time-varying nonlinear gain as feedback was considered. The impulse response of the convolution subsystem is the sum of a finite number of increasing exponentials multiplied by nonnegative powers of the time t, a term that is absolutely integrable and an infinite series of delayed impulses. The main result is a theorem. It essentially states that if the unstable convolution subsystem can be stabilized by a constant feedback gain F and if incremental gain of the difference between the nonlinear gain function and F is sufficiently small, then the nonlinear system is L(p)-stable for any p between one and infinity. Furthermore, the solutions of the nonlinear system depend continuously on the inputs in any L(p)-norm. The fixed point theorem is crucial in deriving the above theorem

Social Meaning in Prosodic Variability

An analysis of six characters from a Chinese television drama reveals socially meaningful patterns of variation in rhythm and final lengthening. Two measures of rhythm, the syllabic PVI and Varco∆S, reveal the three female characters to be more “stress-timed” than the three male characters; smoothing splines analysis, meanwhile, shows that the women do more lengthening of utterance-final syllables than the men. Interspeaker differences in rhythm among the men suggest that the social meaning of rhythmic variability may be linked to a cultural binary between “martial” and “refined” masculinities. This study opens up new avenues in the sociolinguistic study of rhythm and prosody, which has not seen widely reported gender differences in rhythm; as well it is the first study of final lengthening as a sociolinguistic variable in its own right

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Polynomial solutions to H∞ problems

The paper presents a polynomial solution to the standard H∞-optimal control problem. Based on two polynomial J-spectral factorization problems, a parameterization of all suboptimal compensators is obtained. A bound on the McMillan degree of suboptimal compensators is derived and an algorithm is formulated that may be used to solve polynomial J-spectral factorization problems

Genomic data-sharing: What will be our legacy?

Prior to 1974, the Tuskegee Syphilis experiments, expansive use of the HeLa cells, and other blatant instances of research abuse pervaded the medical research field. Discussion today about these challenges have caused the general public to develop a reluctance and distrust for medical research. This has significant implications for the advancement of genomic science, and the public\u27s perception of genomic research