发布求助
文献互助 智能选刊 最新文献

CompSciRN: Artificial Intelligence (Topic)最新文献

筛选
英文 中文
Artificial Intelligence and Transparency: A Blueprint for Improving the Regulation of AI Applications in the EU 人工智能和透明度:改善欧盟人工智能应用监管的蓝图
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2020-08-01 DOI: 10.54648/eulr2020024
A. Wulf, O. Seizov
{"title":"Artificial Intelligence and Transparency: A Blueprint for Improving the Regulation of AI Applications in the EU","authors":"A. Wulf, O. Seizov","doi":"10.54648/eulr2020024","DOIUrl":"https://doi.org/10.54648/eulr2020024","url":null,"abstract":"The adoption of Artificial Intelligence is steadily increasing, but the underlying algorithms have become so complex that they are no longer transparent. The EU has introduced some modest AI transparency requirements as part of its General Data Protection Regulation. However, two years after their introduction, the effectiveness of these rules remains questionable. Our aim is to contribute towards the further development of a governance framework for AI. We begin by explaining how the algorithms that enable the speed and data processing power of AI also obscure its transparency. We review how major guidelines on AI ethics operationalize algorithmic transparency, following which we assess whether these principles are adequately covered by the GDPR. We then present the results of semi-structured interviews of a heterogeneous sample of stakeholders of consumer information online (N=75). Our data provide evidence that the current implementation of the EU’s informed consumer paradigm fails to establish a satisfactory level of consumer protection and information online. If simple technological applications such as cookies remain non-transparent to consumers, the current approaches are entirely incapable of addressing the problem of complex AI applications. We conclude by formulating a policy proposal as to how the transparency of AI applications could be improved from the perspective of end users.\u0000artificial intelligence, explainable AI, automated individual decision-making, profiling, consumer protection, data protection, GDPR, information disclosures, transparency, AI ethics","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124767351","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: 5
Frameworks For Improving AI Explainability Using Accountability Through Regulation and Design 通过监管和设计使用问责制来提高人工智能可解释性的框架
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2020-05-27 DOI: 10.2139/ssrn.3617349
Arsh Shah
{"title":"Frameworks For Improving AI Explainability Using Accountability Through Regulation and Design","authors":"Arsh Shah","doi":"10.2139/ssrn.3617349","DOIUrl":"https://doi.org/10.2139/ssrn.3617349","url":null,"abstract":"This paper discusses frameworks for improving AI explainability regulations and frameworks, drawing on ethical AI design, self-regulation, blockchain solutions for auditing, and FAT (fairness, accountability and transparency) Forensics packages forked from Github. The work takes a look at approaches to AI in the GDPR, Chinese AI Standards, United States law, and domestic Australian Law (at both the State and Federal Levels).","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133002347","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
Stock Market Trading Agent Using On-Policy Reinforcement Learning Algorithms 基于策略强化学习算法的股票市场交易代理
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2020-04-21 DOI: 10.2139/ssrn.3582014
Shreyas Lele, Kavit Gangar, Harsh Daftary, Dewashish Dharkar
{"title":"Stock Market Trading Agent Using On-Policy Reinforcement Learning Algorithms","authors":"Shreyas Lele, Kavit Gangar, Harsh Daftary, Dewashish Dharkar","doi":"10.2139/ssrn.3582014","DOIUrl":"https://doi.org/10.2139/ssrn.3582014","url":null,"abstract":"Stock market has been a complex system which has been difficult to predict for humans, thereby, making the trading decisions difficult to take. It will be useful for traders if there is a model agent which can learn the stock market trends and suggest trading decisions which in turn maximizes the profits. Inorder to develop this agent we have formulated the problem as a Markov Decision Process (MDP) and created a stock trading environment which serves as a platform for this agent to trade the stocks. In this paper, we introduce a Reinforcement Learning based approach to develop a trading agent which performs trading actions on the environment and learns according to the rewards in terms of profit or loss it receives. We have applied different On-policy Reinforcement Learning Algorithms such as Vanilla Policy Gradient (VPG), Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) on the environment to obtain the profits while trading stocks for 3 companies viz. Apple, Microsoft and Nike. The performance of these algorithms in order to maximize the profits have been evaluated and the results and conclusions have been elaborated.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116485086","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: 3
Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence 人与机器:复杂的估计和审计师对人工智能的依赖
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2020-02-24 DOI: 10.2139/ssrn.3422591
Benjamin P. Commerford, Sean A. Dennis, Jennifer R. Joe, Jenny W. Ulla
{"title":"Man Versus Machine: Complex Estimates and Auditor Reliance on Artificial Intelligence","authors":"Benjamin P. Commerford, Sean A. Dennis, Jennifer R. Joe, Jenny W. Ulla","doi":"10.2139/ssrn.3422591","DOIUrl":"https://doi.org/10.2139/ssrn.3422591","url":null,"abstract":"Audit firms are investing billions of dollars to develop artificial intelligence (AI) systems that will help auditors execute challenging tasks (e.g., evaluating complex estimates). Although firms assume AI will enhance audit quality, a growing body of research documents that individuals often exhibit “algorithm aversion” – the tendency to discount computer-based advice more heavily than human advice, although the advice is identical otherwise. Therefore, we conduct an experiment to examine how algorithm aversion manifests in auditor judgments. Consistent with theory, we find that auditors receiving contradictory evidence from their firm’s specialist system (instead of a human specialist) propose smaller adjustments to management’s complex estimates, particularly when management develops their estimates using relatively objective (versus subjective) inputs. Our findings suggest auditor susceptibility to algorithm aversion could prove costly for the profession and financial statements users.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"575 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132518888","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: 40
Classification of Ripening of Banana Fruit Using Convolutional Neural Networks 基于卷积神经网络的香蕉果实成熟分类
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2020-02-21 DOI: 10.2139/ssrn.3558355
M. K. Sri, K. Saikrishna, V. Kumar
{"title":"Classification of Ripening of Banana Fruit Using Convolutional Neural Networks","authors":"M. K. Sri, K. Saikrishna, V. Kumar","doi":"10.2139/ssrn.3558355","DOIUrl":"https://doi.org/10.2139/ssrn.3558355","url":null,"abstract":"Many technological advancements have been developed for precise learning in every field. It is very important to analyse the data in order to extract some useful information. Machine learning and Deep Learning is an integral part of artificial intelligence, which is used to design algorithms based on the data trends and historical relationships between data. Machine learning is used in many fields but the most upgrading and preferred area in which this technology can be seen with value is agriculture. Machine Learning and Deep Neural Networks have made a significant footprint in agriculture area. To standardize the quality of bananas it is essential to determine ripening stages of bananas. This paper proposed a special Convolutional Neural Network architecture to classify the ripening of banana fruits correctly. It learns a set of image features based on a data-driven mechanism and offers a deep indicator of banana’s ripening stage.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134391237","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: 6
Application of Artificial Intelligence in Forecasting: A Systematic Review 人工智能在预测中的应用:系统综述
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2019-11-08 DOI: 10.2139/ssrn.3483313
Albert Annor Antwi, Ayman Abdulsalam Mohamed Al-Dherasi
{"title":"Application of Artificial Intelligence in Forecasting: A Systematic Review","authors":"Albert Annor Antwi, Ayman Abdulsalam Mohamed Al-Dherasi","doi":"10.2139/ssrn.3483313","DOIUrl":"https://doi.org/10.2139/ssrn.3483313","url":null,"abstract":"Purpose:- The aim of this reach is to identify how Artificial Intelligence (AI) could be used in enhancing forecasting to achieve more accurate outcomes. The research also explores the influence that forecasting has on global economy and the reasons why it needs to be accurate. Also, the research explains various pitfalls identified in forecasting. Method:- This research implements two research approaches which are review of literature and formulation of hypotheses. Seven hypotheses are created. Findings:- AI, when integrated with other technologies such as Machine Learning (ML) and when provided with the right computer power, yields much more accurate results than many other forecasting methods. The technology is costly, however, and it is prone to cyber-attacks. Conclusion:- The future of business is highly reliant on forecasting, which directly impacts the global economy. But, not every business will have the power to own the forecasting technology due to the cost, and business will need to increase security to protect the forecasting systems.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134284750","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: 3
Biclustering via Mixtures of Regression Models 基于混合回归模型的双聚类
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2019-06-08 DOI: 10.1007/978-3-030-22741-8_38
R. Velu, Zhaoque Zhou, C. W. Tee
{"title":"Biclustering via Mixtures of Regression Models","authors":"R. Velu, Zhaoque Zhou, C. W. Tee","doi":"10.1007/978-3-030-22741-8_38","DOIUrl":"https://doi.org/10.1007/978-3-030-22741-8_38","url":null,"abstract":"","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115428704","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
Search for Answers in Domain-Specific Supported by Intelligent Agents 智能代理支持的特定领域答案搜索
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2018-10-08 DOI: 10.2139/ssrn.3263190
Fernando Zacaŕias, Rosalba Cuapa, Guillermo De Ita, Miguel Bracamontes
{"title":"Search for Answers in Domain-Specific Supported by Intelligent Agents","authors":"Fernando Zacaŕias, Rosalba Cuapa, Guillermo De Ita, Miguel Bracamontes","doi":"10.2139/ssrn.3263190","DOIUrl":"https://doi.org/10.2139/ssrn.3263190","url":null,"abstract":"Search for answers in specific domains is a new milestone in question answering. Traditionally, question answering has focused on general domain questions. Thus, the most relevant answers (or passages) are selected according to the type of question and the Named Entities included in the possible answers. In this paper, we present a novel approach on question answering over specific (or technical) domains. This proposal allows us to answer questions such as “What article is appropriate for … “, “What are the articles related to … “, these kind of questions cannot be answered by a general question answering system. Our approach is based on a set of laws of a specific domain, which contain a large set of laws regarding the work organized into a hierarchy. We consider generic concepts such as “article” semantic categories. Our results on the corpus of Federal Labor Law show that this approach is effective and highly reliable.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129532259","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
Deeply Learning Derivatives 深度学习衍生品
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2018-09-06 DOI: 10.2139/ssrn.3244821
Ryan Ferguson, Andrew Green
{"title":"Deeply Learning Derivatives","authors":"Ryan Ferguson, Andrew Green","doi":"10.2139/ssrn.3244821","DOIUrl":"https://doi.org/10.2139/ssrn.3244821","url":null,"abstract":"This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models. We develop a methodology to randomly generate appropriate training data and explore the impact of several parameters including layer width and depth, training data quality and quantity on model speed and accuracy.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130715524","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: 35
Examining the Impacts of Airbnb's Review Policy Change on Listing Reviews 考察Airbnb审核政策变化对房源审核的影响
CompSciRN: Artificial Intelligence (Topic) Pub Date : 2018-07-02 DOI: 10.2139/ssrn.3206895
Reza Mousavi, K. Zhao
{"title":"Examining the Impacts of Airbnb's Review Policy Change on Listing Reviews","authors":"Reza Mousavi, K. Zhao","doi":"10.2139/ssrn.3206895","DOIUrl":"https://doi.org/10.2139/ssrn.3206895","url":null,"abstract":"In July 2014, Airbnb, one of the biggest firms in the sharing economy, decided to change the way the guests and the hosts review each other on the platform. Before this change, the guests and hosts were able to post reviews about their experiences asynchronously - the host/guest would be able to see the other party’s review whenever it was posted. The new review policy though, rolled out a simultaneous review system in which reviews are viewable only after both the host and the guest posting their own reviews. Our aim in this study was to examine the impacts of this new review policy on guest reviews. Using Regression Discontinuity Design, Panel Data Analysis and a variety of techniques in the text analytics domain, we discovered that the new review policy made the reviews more diverse in terms of topics discussed in the reviews, lengthier, more objective, less positive, and more diverse in terms of their sentiment. Interestingly, we also discovered that the review policy had a long-lasting impact on the constructs related to personal opinions (i.e., sentiment and the diversity of sentiment in the reviews). However, the impact of review policy on informational content (i.e., diversity of topics, depth, and objectivity of the reviews) was short-lived.","PeriodicalId":241211,"journal":{"name":"CompSciRN: Artificial Intelligence (Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129508687","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: 2
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信