Hybrid Refining Approach of PrOnto Ontology

This paper presents a refinement of PrOnto ontology using a validation test based on legal experts’ annotation of privacy policies combined with an Open Knowledge Extraction (OKE) algorithm. To ensure robustness of the results while preserving an interdisciplinary approach, the integration of legal and technical knowledge has been carried out as follows. The set of privacy policies was first analysed by the legal experts to discover legal concepts and map the text into PrOnto. The mapping was then provided to computer scientists to perform the OKE analysis. Results were validated by the legal experts, who provided feedbacks and refinements (i.e. new classes and modules) of the ontology according to MeLOn methodology. Three iterations were performed on a set of (development) policies, and a final test using a new set of privacy policies. The results are 75,43% of detection of concepts in the policy texts and an increase of roughly 33% in the accuracy gain on the test set, using the new refined version of PrOnto enriched with SKOS-XL lexicon terms and definitions

A Web-Based Tool for Analysing Normative Documents in English

Our goal is to use formal methods to analyse normative documents written in English, such as privacy policies and service-level agreements. This requires the combination of a number of different elements, including information extraction from natural language, formal languages for model representation, and an interface for property specification and verification. We have worked on a collection of components for this task: a natural language extraction tool, a suitable formalism for representing such documents, an interface for building models in this formalism, and methods for answering queries asked of a given model. In this work, each of these concerns is brought together in a web-based tool, providing a single interface for analysing normative texts in English. Through the use of a running example, we describe each component and demonstrate the workflow established by our tool

인공지능 관련 뉴스 기사의 프레임, 감정 분석

학위논문(석사) -- 서울대학교대학원 : 사회과학대학 언론정보학과, 2022. 8. 이철주 .This study examines how artificial intelligence (AI) is presented in the news media by examining the frames and emotions expressed in news coverage about AI. For analysis, I used computational text analysis techniques -structural topic model (STM) to extract frames and NRC Emotion Lexicon and Linguistic Inquiry and Word Count (LIWC) to detect emotions. Then I examined their correlations with the political ideology of media outlets (conservative vs. liberal) and media type (newspapers vs TV news). By identifying the frames and the emotions embedded in the news media, it would be possible to predict how they influence the formation of public opinions and attitudes towards AI.본 연구는 컴퓨터 텍스트 분석 기술을 통해 인공지능 (AI)에 대한 뉴스 보도에 드러난 프레임과 감정을 분석하여 인공지능이 뉴스 미디어에서 어떻게 표현되는지를 살펴보는 것을 목적으로 한다. 프레임 추출을 위해 Structural Topic Model (STM) 기법을, 감정 추출을 위해 NRC Emotion Lexicon과 Linguistic Inquiry and Word Count (LIWC) 프로그램을 활용했다. 언론사의 정치 성향(보수 – 진보)과 미디어 유형(신문 – 방송)을 변수로 설정해, 추출된 결과와 각 변수와의 상관관계를 분석했다. 뉴스 미디어에 내재된 프레임과 감정을 파악함으로써, 그것이 AI에 대한 여론 및 태도 형성에 어떤 영향을 미치는지 예측할 수 있을 것이다.Chapter 1. Introduction 1 Chapter 2. Literature Review and Research Aim 2 Chapter 3. Conceptual Framework 8 Chapter 4. Methods 21 Chapter 5. Results 27 Chapter 6. Discussion 45 Appendix. 49 Bibliography. 51 Abstract in Korean. 57석

Self-regulatory information sharing in participatory social sensing

Participation in social sensing applications is challenged by privacy threats. Large-scale access to citizens’ data allow surveillance and discriminatory actions that may result in segregation phenomena in society. On the contrary are the benefits of accurate computing analytics required for more informed decision-making, more effective policies and regulation of techno-socio-economic systems supported by ‘Internet-of Things’ technologies. In contrast to earlier work that either focuses on privacy protection or Big Data analytics, this paper proposes a self-regulatory information sharing system that bridges this gap. This is achieved by modeling information sharing as a supply-demand system run by computational markets. On the supply side lie the citizens that make incentivized but self-determined decisions about the level of information they share. On the demand side stand data aggregators that provide rewards to citizens to receive the required data for accurate analytics. The system is empirically evaluated with two real-world datasets from two application domains: (i) Smart Grids and (ii) mobile phone sensing. Experimental results quantify trade-offs between privacy-preservation, accuracy of analytics and costs from the provided rewards under different experimental settings. Findings show a higher privacy-preservation that depends on the number of participating citizens and the type of data summarized. Moreover, analytics with summarization data tolerate high local errors without a significant influence on the global accuracy. In other words, local errors cancel out. Rewards can be optimized to be fair so that citizens with more significant sharing of information receive higher rewards. All these findings motivate a new paradigm of truly decentralized and ethical data analytics.ISSN:2193-112