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Author |
Veale, M.; Binns, R.; Edwards, L. |
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Title |
Algorithms That Remember: Model Inversion Attacks and Data Protection Law |
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Journal Article |
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Year |
2018 |
Publication |
Philosophical Transactions of the Royal Society a: Mathematical, Physical and Engineering Sciences |
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376 |
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2133 |
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20180083 |
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Keywords |
artificial intelligence, AI |
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Abstract |
Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EUs recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around model inversion and membership inference attacks, which indicates that the process of turning training data into machine-learned systems is not one way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation. |
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The Royal Society |
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1364-503x |
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refbase @ admin @ veale_algorithms_2018 |
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17413 |
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Author |
Winfield, A.F.T.; Jirotka, M. |
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Title |
Ethical Governance Is Essential to Building Trust in Robotics and Artificial Intelligence Systems |
Type |
Journal Article |
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Year |
2018 |
Publication |
Philosophical Transactions of the Royal Society a: Mathematical, Physical and Engineering Sciences |
Abbreviated Journal |
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Volume |
376 |
Issue |
2133 |
Pages |
20180085 |
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Keywords |
artificial intelligence, AI |
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This paper explores the question of ethical governance for robotics and artificial intelligence (AI) systems. We outline a roadmap–which links a number of elements, including ethics, standards, regulation, responsible research and innovation, and public engagement–as a framework to guide ethical governance in robotics and AI. We argue that ethical governance is essential to building public trust in robotics and AI, and conclude by proposing five pillars of good ethical governance. |
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The Royal Society |
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refbase @ admin @ winfield_ethical_2018 |
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17414 |
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Author |
Pagallo, U. |
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Title |
Apples, Oranges, Robots: Four Misunderstandings in Todays Debate on the Legal Status of Ai Systems |
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Journal Article |
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Year |
2018 |
Publication |
Philosophical Transactions of the Royal Society a: Mathematical, Physical and Engineering Sciences |
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Volume |
376 |
Issue |
2133 |
Pages |
20180168 |
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Keywords |
artificial intelligence, AI |
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Scholars have increasingly discussed the legal status(es) of robots and artificial intelligence (AI) systems over the past three decades; however, the 2017 resolution of the EU parliament on the electronic personhood of AI robots has reignited and even made current debate ideological. Against this background, the aim of the paper is twofold. First, the intent is to show how often todays discussion on the legal status(es) of AI systems leads to different kinds of misunderstanding that regard both the legal personhood of AI robots and their status as accountable agents establishing rights and obligations in contracts and business law. Second, the paper claims that whether or not the legal status of AI systems as accountable agents in civil–as opposed to criminal–law may make sense is an empirical issue, which should not be politicized. Rather, a pragmatic approach seems preferable, as shown by methods of competitive federalism and legal experimentation. In the light of the classical distinction between primary rules and secondary rules of the law, examples of competitive federalism and legal experimentation aim to show how the secondary rules of the law can help us understanding what kind of primary rules we may wish for our AI robots. |
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The Royal Society |
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1364-503x |
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no |
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refbase @ admin @ pagallo_apples_2018 |
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17415 |
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Author |
Harambam, J.; Helberger, N.; Hoboken, J.V. |
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Title |
Democratizing Algorithmic News Recommenders: How to Materialize Voice in a Technologically Saturated Media Ecosystem |
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Journal Article |
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Year |
2018 |
Publication |
Philosophical Transactions of the Royal Society a: Mathematical, Physical and Engineering Sciences |
Abbreviated Journal |
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Volume |
376 |
Issue |
2133 |
Pages |
20180088 |
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Keywords |
artificial intelligence, AI |
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The deployment of various forms of AI, most notably of machine learning algorithms, radically transforms many domains of social life. In this paper we focus on the news industry, where different algorithms are used to customize news offerings to increasingly specific audience preferences. While this personalization of news enables media organizations to be more receptive to their audience, it can be questioned whether current deployments of algorithmic news recommenders (ANR) live up to their emancipatory promise. Like in various other domains, people have little knowledge of what personal data is used and how such algorithmic curation comes about, let alone that they have any concrete ways to influence these data-driven processes. Instead of going down the intricate avenue of trying to make ANR more transparent, we explore in this article ways to give people more influence over the information news recommendation algorithms provide by thinking about and enabling possibilities to express voice. After differentiating four ideal typical modalities of expressing voice (alternation, awareness, adjustment and obfuscation) which are illustrated with currently existing empirical examples, we present and argue for algorithmic recommender personae as a way for people to take more control over the algorithms that curate peoples news provision. |
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The Royal Society |
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1364-503x |
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no |
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refbase @ admin @ harambam_democratizing_2018 |
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17416 |
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Author |
Mohan, K.N. |
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Title |
Stem Cell Models to Investigate the Role of DNA Methylation Machinery in Development of Neuropsychiatric Disorders |
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Journal Article |
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Year |
2016 |
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Stem Cells International |
Abbreviated Journal |
Stem Cells Int |
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2016 |
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Pages |
4379425 |
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Abstract |
Epigenetic mechanisms underlie differentiation of pluripotent stem cells into different lineages that contain identical genomes but express different sets of cell type-specific genes. Because of high discordance rates in monozygotic twins, epigenetic mechanisms are also implicated in development of neuropsychiatric disorders such as schizophrenia and autism. In support of this notion, increased levels of DNA methyltransferases (DNMTs), DNMT polymorphisms, and dysregulation of DNA methylation network were reported among schizophrenia patients. These results point to the importance of development of DNA methylation machinery-based models for studying the mechanism of abnormal neurogenesis due to certain DNMT alleles or dysregulated DNMTs. Achieving this goal is strongly confronted by embryonic lethality associated with altered levels of epigenetic machinery such as DNMT1 and expensive approaches in developing in vivo models. In light of literature evidence that embryonic stem cells (ESCs) are tolerant of DNMT mutations and advancement in the technology of gene targeting, it is now possible to introduce desired mutations in DNMT loci to generate suitable ESC lines that can help understand the underlying mechanisms by which abnormal levels of DNMTs or their specific mutations/alleles result in abnormal neurogenesis. In the future, these models can facilitate development of suitable drugs for treatment of neuropsychiatric disorders. |
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Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Jawaharnagar, Hyderabad 500 078, India |
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English |
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1687-966X |
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PMID:26798355; PMCID:PMC4699075 |
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refbase @ user @ |
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16707 |
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