1,721,012 research outputs found
AI Systems Under Criminal Law: a Legal Analysis and a Regulatory Perspective
Get PDFCriminal liability for acts committed by AI systems has recently become a hot legal topic. This paper includes three different contributions. The first contribution is an analysis of the extent to which an AI system can satisfy the requirements for criminal liability: accomplishing an actus reus, having the corresponding mens rea, possessing the cognitive capacities needed for responsibility. The second contribution is a discussion of criminal activity accomplished by an AI entity, with reference to a recent case involving an online bot, the Random Darknet Shopper. This discussion will provide the context for the analysis of commonalities and differences between criminal activities by humans and by artificial systems. The third contribution concerns the evaluation of different ways of addressing criminal activities by AI systems in a regulatory perspective
The Use of Copyrighted Works by AI Systems: Art Works in the Data Mill
Get PDFWe shall first introduce the use of artificial intelligence (AI) in producing new intellectual creations, distinguishing approaches based on knowledge representation and on machine learning. Then we shall provide an overview of some significant applications of AI to the production of intellectual creations, distinguishing the extent to which they depend on pre-existing works, and the different ways in which such pre-existing works are used in the creative process. In addition, we shall discuss some methods to automatically assess the similarity of works and styles, in the context of AI technologies for text generation. Finally, we shall discuss the legal aspects of AI-reuse of copyrighted works, focusing on the rights of the authors of such works relative to the process and the outputs of AI
Artificial intelligence in the big data era: risks and opportunities
No full textAfter introducing the concept and methods of artificial intelligence, we shall consider the prospects of machine learning technologies, as applied to big data. We shall discuss the opportunities and risks of these technological developments. In particular we shall consider how AI on the one hand further expands the persistence and pervasiveness of citizens’ assessment and control, but on the other hand, can also support individuals. Finally, we shall address the regulation of artificial intelligence (AI), namely, how to ensure that AI benefits citizens and communities, respecting individual rights and social values
Beyond the Price Tag: Personalized Pricing and the Pre-contractual Rights of Consumers and Data Subjects under EU Law
No full textIn the current digital era, the growth of digital commerce and the data-driven economy has created new opportunities for firms to predict consumer behavior, including their willingness to pay a certain price. This practice of algorithmic pricing has become a widespread business model, raising concerns among economists and lawyers about its impact on the market and society. The Cambridge Handbook of Algorithmic Price Personalization and the Law is a comprehensive overview of the key debates surrounding algorithmic pricing, written by a multidisciplinary group of scholars with expertise in legal, economic, data science, and marketing research. The Handbook critically examines existing knowledge, identifies weaknesses, and proposes feasible alternatives for legal analysis, market regulation, and protection of vulnerable individuals. This comprehensive overview of algorithmic pricing is a one-stop reference for the political and legal community
La manopola etica : i veicoli autonomi eticamente personalizzabili e il diritto
No full textPublished: December 2017Accidents involving autonomous vehicles (AVs) raise difficult ethical dilemmas and legal issues. It has been argued that self-driving cars should be programmed to kill, that is, they should be equipped with pre-programmed approaches to the choice of what lives to sacrifice when losses are inevitable. Here we shall explore a different approach, namely, giving the user/passenger the task (and burden) of deciding what ethical approach should be taken by AVs in unavoidable accident scenarios. We thus assume that AVs are equipped with what we call an «Ethical Knob», a device enabling passengers to ethically customise their AVs, namely, to choose between different settings corresponding to different moral approaches or principles. Accordingly, AVs would be entrusted with implementing users' ethical choices, while manufacturers/programmers would be tasked with enabling the user's choice and ensuring implementation by the AV
Detecting and explaining unfairness in consumer contracts through memory networks
Get PDFRecent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the clas- sification accuracy, but are also able to offer meaningful, natural language explana- tions of otherwise opaque classifier outcomes
Consent to Targeted Advertising
Get PDFTargeted advertising in digital markets involves multiple actors collecting, exchanging, and processing personal data for the purpose of capturing users’ attention in online environments. This ecosystem has given rise to considerable adverse effects on individuals and society, resulting from mass surveillance, the manipulation of choices and opinions, and the spread of addictive or fake messages. Against this background, this article critically discusses the regulation of consent in online targeted advertising. To this end, we review EU laws and proposals and consider the extent to which a requirement of informed consent may provide effective consumer protection. On the basis of such an analysis, we make suggestions for possible avenues that may be pursued
Unfair clause detection in terms of service across multiple languages
Get PDFMost of the existing natural language processing systems for legal texts are developed for the English language. Nevertheless, there are several application domains where multiple versions of the same documents are provided in different languages, especially inside the European Union. One notable example is given by Terms of Service (ToS). In this paper, we compare different approaches to the task of detecting potential unfair clauses in ToS across multiple languages. In particular, after developing an annotated corpus and a machine learning classifier for English, we consider and compare several strategies to extend the system to other languages: building a novel corpus and training a novel machine learning system for each language, from scratch; projecting annotations across documents in different languages, to avoid the creation of novel corpora; translating training documents while keeping the original annotations; translating queries at prediction time and relying on the English system only. An extended experimental evaluation conducted on a large, original dataset indicates that the time-consuming task of re-building a novel annotated corpus for each language can often be avoided with no significant degradation in terms of performance
Make privacy policies longer and appoint LLM readers
Get PDFIn a world of human-only readers, a trade-off persists between comprehensiveness and comprehensibility: only privacy policies too long to be humanly readable can precisely describe the intended data processing. We argue that this trade-off no longer exists where LLMs are able to extract tailored information from clearly-drafted fully-comprehensive privacy policies. AQ1 To substantiate this claim, we provide a methodology for drafting comprehensive non-ambiguous privacy policies and for querying them using LLMs prompts. Our methodology is tested with an experiment aimed at determining to what extent GPT-4 and Llama2 are able to answer questions regarding the content of privacy policies designed in the format we propose. We further support this claim by analyzing real privacy policies in the chosen market sectors through two experiments (one with legal experts, and another by using LLMs). Based on the success of our experiments, we submit that data protection law should change: it must require controllers to provide clearly drafted, fully comprehensive privacy policies from which data subjects and other actors can extract the needed information, with the help of LLMs
Detecting Vague Clauses in Privacy Policies: The Analysis of Data Categories Using BERT Models and LLMs
No full textDespite some improvements in compliance metrics after the implementation of the European General Data Protection Regulation (GDPR), privacy policies have become longer and more ambiguous. They often fail to fully meet GDPR requirements, thus leaving users without a reliable way to understand how their data is processed. We present a novel corpus composed by 30 privacy policies of online platforms and a new set of annotation guidelines, to assess the level of comprehensiveness of information. We focus on the processed categories of data, classifying each clause either as fully informative or as insufficiently informative. In our experimental evaluation, we perform 6 different classification and detection tasks, comparing BERT models and generative Large Language Models
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