arXiv - CS - Computers and Society最新文献_第7页

Addressing Information Asymmetry in Legal Disputes through Data-Driven Law Firm Rankings 通过数据驱动的律师事务所排名解决法律纠纷中的信息不对称问题

arXiv - CS - Computers and Society Pub Date : 2024-08-29 DOI: arxiv-2408.16863

Alexandre Mojon, Robert Mahari, Sandro Claudio Lera

{"title":"Addressing Information Asymmetry in Legal Disputes through Data-Driven Law Firm Rankings","authors":"Alexandre Mojon, Robert Mahari, Sandro Claudio Lera","doi":"arxiv-2408.16863","DOIUrl":"https://doi.org/arxiv-2408.16863","url":null,"abstract":"Legal disputes are on the rise, contributing to growing litigation costs.\u0000Parties in these disputes must select a law firm to represent them, however,\u0000public rankings of law firms are based on reputation and, we find, have little\u0000correlation with actual litigation outcomes, giving parties with more\u0000experience and inside knowledge an advantage. To enable litigants to make\u0000informed decisions, we present a novel dataset of 310,876 U.S. civil lawsuits\u0000and we apply an algorithm that generalizes the Bradley-Terry model to assess\u0000law firm effectiveness. We find that our outcome-based ranking system better\u0000accounts for future performance than traditional reputation-based rankings,\u0000which often fail to reflect future legal performance. Moreover, this\u0000predictability decays to zero as the number of interactions between law firms\u0000increases, providing new evidence to the long-standing debate about whether\u0000litigation win rates approach 50% as information asymmetry diminishes. By\u0000prioritizing empirical results, our approach aims to provide a more equitable\u0000assessment of law firm quality, challenging existing prestige-focused metrics,\u0000and levels the playing field between litigants.","PeriodicalId":501112,"journal":{"name":"arXiv - CS - Computers and Society","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

JINet: easy and secure private data analysis for everyone JINet：为每个人提供简单安全的私人数据分析

arXiv - CS - Computers and Society Pub Date : 2024-08-29 DOI: arxiv-2408.16402

Giada Lalli, James Collier, Yves Moreau, Daniele Raimondi

引用次数: 0

Defining Interoperability: a universal standard 定义互操作性：通用标准

arXiv - CS - Computers and Society Pub Date : 2024-08-29 DOI: arxiv-2408.16411

Giada Lalli

引用次数: 0

Ethical AI Governance: Methods for Evaluating Trustworthy AI 合乎道德的人工智能治理：评估可信人工智能的方法

arXiv - CS - Computers and Society Pub Date : 2024-08-28 DOI: arxiv-2409.07473

Louise McCormack, Malika Bendechache

引用次数: 0

Navigating Design Science Research in mHealth Applications: A Guide to Best Practices 移动医疗应用中的设计科学研究导航：最佳实践指南

arXiv - CS - Computers and Society Pub Date : 2024-08-28 DOI: arxiv-2409.07470

Avnish Singh Jat, Tor-Morten Grønli, George Ghinea

引用次数: 0

Navigating the Future of Education: Educators' Insights on AI Integration and Challenges in Greece, Hungary, Latvia, Ireland and Armenia 引领教育的未来：希腊、匈牙利、拉脱维亚、爱尔兰和亚美尼亚教育工作者对人工智能融合与挑战的见解

arXiv - CS - Computers and Society Pub Date : 2024-08-28 DOI: arxiv-2408.15686

Evangelia Daskalaki, Katerina Psaroudaki, Paraskevi Fragopoulou

{"title":"Navigating the Future of Education: Educators' Insights on AI Integration and Challenges in Greece, Hungary, Latvia, Ireland and Armenia","authors":"Evangelia Daskalaki, Katerina Psaroudaki, Paraskevi Fragopoulou","doi":"arxiv-2408.15686","DOIUrl":"https://doi.org/arxiv-2408.15686","url":null,"abstract":"Understanding teachers' perspectives on AI in Education (AIEd) is crucial for\u0000its effective integration into the educational framework. This paper aims to\u0000explore how teachers currently use AI and how it can enhance the educational\u0000process. We conducted a cross-national study spanning Greece, Hungary, Latvia,\u0000Ireland, and Armenia, surveying 1754 educators through an online questionnaire,\u0000addressing three research questions. Our first research question examines\u0000educators' understanding of AIEd, their skepticism, and its integration within\u0000schools. Most educators report a solid understanding of AI and acknowledge its\u0000potential risks. AIEd is primarily used for educator support and engaging\u0000students. However, concerns exist about AI's impact on fostering critical\u0000thinking and exposing students to biased data. The second research question\u0000investigates student engagement with AI tools from educators' perspectives.\u0000Teachers indicate that students use AI mainly to manage their academic\u0000workload, while outside school, AI tools are primarily used for entertainment.\u0000The third research question addresses future implications of AI in education.\u0000Educators are optimistic about AI's potential to enhance educational processes,\u0000particularly through personalized learning experiences. Nonetheless, they\u0000express significant concerns about AI's impact on cultivating critical thinking\u0000and ethical issues related to potential misuse. There is a strong emphasis on\u0000the need for professional development through training seminars, workshops, and\u0000online courses to integrate AI effectively into teaching practices. Overall,\u0000the findings highlight a cautious optimism among educators regarding AI in\u0000education, alongside a clear demand for targeted professional development to\u0000address concerns and enhance skills in using AI tools.","PeriodicalId":501112,"journal":{"name":"arXiv - CS - Computers and Society","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Responsible AI for Test Equity and Quality: The Duolingo English Test as a Case Study 负责任的人工智能促进测试公平和质量：以 Duolingo 英语测试为例

arXiv - CS - Computers and Society Pub Date : 2024-08-28 DOI: arxiv-2409.07476

Jill Burstein, Geoffrey T. LaFlair, Kevin Yancey, Alina A. von Davier, Ravit Dotan

引用次数: 0

AI, Climate, and Transparency: Operationalizing and Improving the AI Act 人工智能、气候和透明度：人工智能法的实施与改进

arXiv - CS - Computers and Society Pub Date : 2024-08-28 DOI: arxiv-2409.07471

Nicolas Alder, Kai Ebert, Ralf Herbrich, Philipp Hacker

引用次数: 0

Verification methods for international AI agreements 国际人工智能协议的核查方法

arXiv - CS - Computers and Society Pub Date : 2024-08-28 DOI: arxiv-2408.16074

Akash R. Wasil, Tom Reed, Jack William Miller, Peter Barnett

引用次数: 0

Comparing diversity, negativity, and stereotypes in Chinese-language AI technologies: a case study on Baidu, Ernie and Qwen 比较中文人工智能技术的多样性、消极性和刻板印象：关于百度、Ernie 和 Qwen 的案例研究

arXiv - CS - Computers and Society Pub Date : 2024-08-28 DOI: arxiv-2408.15696

Geng Liu, Carlo Alberto Bono, Francesco Pierri

{"title":"Comparing diversity, negativity, and stereotypes in Chinese-language AI technologies: a case study on Baidu, Ernie and Qwen","authors":"Geng Liu, Carlo Alberto Bono, Francesco Pierri","doi":"arxiv-2408.15696","DOIUrl":"https://doi.org/arxiv-2408.15696","url":null,"abstract":"Large Language Models (LLMs) and search engines have the potential to\u0000perpetuate biases and stereotypes by amplifying existing prejudices in their\u0000training data and algorithmic processes, thereby influencing public perception\u0000and decision-making. While most work has focused on Western-centric AI\u0000technologies, we study Chinese-based tools by investigating social biases\u0000embedded in the major Chinese search engine, Baidu, and two leading LLMs, Ernie\u0000and Qwen. Leveraging a dataset of 240 social groups across 13 categories\u0000describing Chinese society, we collect over 30k views encoded in the\u0000aforementioned tools by prompting them for candidate words describing such\u0000groups. We find that language models exhibit a larger variety of embedded views\u0000compared to the search engine, although Baidu and Qwen generate negative\u0000content more often than Ernie. We also find a moderate prevalence of\u0000stereotypes embedded in the language models, many of which potentially promote\u0000offensive and derogatory views. Our work highlights the importance of promoting\u0000fairness and inclusivity in AI technologies with a global perspective.","PeriodicalId":501112,"journal":{"name":"arXiv - CS - Computers and Society","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142183913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0