The synthetization of human voices

Bendel, Oliver

doi:10.1007/s00146-017-0748-x

Cited by 21 publications

(16 citation statements)

References 8 publications

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“…Notwithstanding the above, companies can cause massive job losses due to AI implementation (Frey and Osborne 2013), conduct unmonitored forms of AI experiments on society without informed consent (Kramer, Guillory, and Hancock 2014), suffer from data breaches (Schneier 2018), use unfair, biased algorithms (Eubanks 2018), provide unsafe AI products (Sitawarin et al 2018), use trade secrets to disguise harmful or flawed AI functionalities ), rush to integrate and put immature AI applications on the market and many more. Furthermore, criminal or black-hat hackers can use AI to tailor cyberattacks, steal information, attack IT infrastructures, rig elections, spread misinformation for example through deepfakes, use voice synthesis technologies for fraud or social engineering (Bendel 2017), or disclose personal traits that are actually secret or private via machine learning applications (Kosinski and Wang 2018;Kosinski, Stillwell, and Graepel 2013;Kosinski et al 2015). All in all, only a very small number of papers is published about the misuse of AI systems, even though they impressively show what massive damage can be done with those systems (Brundage et al 2018;King et al 2019; O'Neil 2016).…”

Section: Ai In Practice 31 Business Versus Ethicsmentioning

confidence: 99%

The Ethics of AI Ethics: An Evaluation of Guidelines

2020

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Current advances in research, development and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics. In consequence, a number of ethics guidelines have been released in recent years. These guidelines comprise normative principles and recommendations aimed to harness the "disruptive" potentials of new AI technologies. Designed as a comprehensive evaluation, this paper analyzes and compares these guidelines highlighting overlaps but also omissions. As a result, I give a detailed overview of the field of AI ethics. Finally, I also examine to what extent the respective ethical principles and values are implemented in the practice of research, development and application of AI systems -and how the effectiveness in the demands of AI ethics can be improved.

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Section: Ai In Practice 31 Business Versus Ethicsmentioning

confidence: 99%

The Ethics of AI Ethics: An Evaluation of Guidelines

2020

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“…In the overall view, machine learning technologies make it possible to automatically create any media, be it images (Karras et al 2017), videos (Thies et al 2016), audio recordings (Bendel 2017) or texts (Radford et al 2019a). The quality of the media created is constantly improving, so that previously accepted principles, such as "seeing is Amount of monetary costs to acquire parƟcular sets of hard-or soŌware/code equipped with or without comments/code equipped with or without hyperparameters/code is compiled or raw/details about the types of hardware being used believing" or "hearing is believing", have to be abandoned.…”

Section: Synthetic Mediamentioning

confidence: 99%

Forbidden knowledge in machine learning reflections on the limits of research and publication

Hagendorff

2020

AI & Soc

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Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance, with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up till now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline deliberations on how to deal with questions concerning the dissemination of such information. It proposes a tentative ethical framework for the machine learning community on how to deal with forbidden knowledge and dual-use applications.

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“…The AI is utilized to sharpen techniques to commit traditional cybercrimes such as financial fraud, cyberterrorism, cyberextortion, etc. For example, when hackers try to voice phishing, the hackers can deceive victims by using the realistically imitated voices of the victims' family or friends [23].…”

Section: A Ai Security Threats and Crimementioning

confidence: 99%

Artificial Intelligence Security Threat, Crime, and Forensics: Taxonomy and Open Issues

Jeong

2020

IEEE Access

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Advances in Artificial Intelligence (AI) have influenced almost every field including computer science, robotics, social engineering, psychology, criminology and so on. Although AI has solved various challenges, potential security threats of AI algorithms and training data have been stressed by AI researchers. As AI system inherits security threats of traditional computer system, the concern about novel cyberattack enhanced by AI is also growing. In addition, AI is deeply connected to physical space (e.g. autonomous vehicle, intelligent virtual assistant), so AI-related crime can harm people physically, beyond the cyberspace. In this context, we represent a literature review of security threats and AI-related crime. Based on the literature review, this article defines the term AI crime and classifies AI crime into 2 categories: AI as tool crime and AI as target crime, inspired by a taxonomy of cybercrime: Computer as tool crime and Computer as tool crime. Through the proposed taxonomy, foreseeable AI crimes are systematically studied and related forensic techniques are also addressed. We also analyze the characteristics of the AI crimes and present challenges that are difficult to be solved with the traditional forensic techniques. Finally, open issues are presented, with emphasis on the need to establish novel strategies for AI forensics.

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The synthetization of human voices

Cited by 21 publications

References 8 publications

The Ethics of AI Ethics: An Evaluation of Guidelines

The Ethics of AI Ethics: An Evaluation of Guidelines

Forbidden knowledge in machine learning reflections on the limits of research and publication

Artificial Intelligence Security Threat, Crime, and Forensics: Taxonomy and Open Issues

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