InfoSciRN: Machine Learning (Sub-Topic)最新文献

Analysing crypto news sentiment to predict bitcoin prices 分析加密新闻情绪，预测比特币价格

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2021-08-29 DOI: 10.2139/ssrn.3913652

Abhishek Kumar

引用次数: 1

Escaping the 'Impossibility of Fairness': From Formal to Substantive Algorithmic Fairness 逃避“不可能的公平”:从形式到实质的算法公平

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2021-07-09 DOI: 10.2139/ssrn.3883649

Ben Green

{"title":"Escaping the 'Impossibility of Fairness': From Formal to Substantive Algorithmic Fairness","authors":"Ben Green","doi":"10.2139/ssrn.3883649","DOIUrl":"https://doi.org/10.2139/ssrn.3883649","url":null,"abstract":"In the face of compounding crises of social and economic inequality, many have turned to algorithmic decision-making to achieve greater fairness in society. As these efforts intensify, reasoning within the burgeoning field of “algorithmic fairness” increasingly shapes how fairness manifests in practice. This paper interrogates whether algorithmic fairness provides the appropriate conceptual and practical tools for enhancing social equality. I argue that the dominant, “formal” approach to algorithmic fairness is ill-equipped as a framework for pursuing equality, as its narrow frame of analysis generates restrictive approaches to reform. In light of these shortcomings, I propose an alternative: a “substantive” approach to algorithmic fairness that centers opposition to social hierarchies and provides a more expansive analysis of how to address inequality. This substantive approach enables more fruitful theorizing about the role of algorithms in combatting oppression. The distinction between formal and substantive algorithmic fairness is exemplified by each approach’s responses to the “impossibility of fairness” (an incompatibility between mathematical definitions of algorithmic fairness). While the formal approach requires us to accept the “impossibility of fairness” as a harsh limit on efforts to enhance equality, the substantive approach allows us to escape the “impossibility of fairness” by suggesting reforms that are not subject to this false dilemma and that are better equipped to ameliorate conditions of social oppression.","PeriodicalId":189628,"journal":{"name":"InfoSciRN: Machine Learning (Sub-Topic)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114437009","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: 20

Real-Time Prediction of Online False Information Purveyors and their Characteristics 网络虚假信息提供者的实时预测及其特征

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2020-10-30 DOI: 10.2139/ssrn.3725919

Anil R. Doshi, S. Raghavan, W. Schmidt

{"title":"Real-Time Prediction of Online False Information Purveyors and their Characteristics","authors":"Anil R. Doshi, S. Raghavan, W. Schmidt","doi":"10.2139/ssrn.3725919","DOIUrl":"https://doi.org/10.2139/ssrn.3725919","url":null,"abstract":"Disinformation, misinformation, and other 'fake news' - collectively false information - is quick and inexpensive to create and distribute in our increasingly digital and connected world. Identifying false information early and cost effectively can offset some of those operational advantages. In this paper, we develop light-weight machine learning models that utilize (1) a novel data set tracking browsing behavior and (2) domain registration data that is available for all websites when they are established. Using only the domain registration data, we develop and demonstrate a machine learning classifier that identifies domains, at the time the domain is registered, that will go on to produce false information. We then combine this data with our browsing data and develop a machine learning classifier that identifies false information domains whose content is most associated with higher levels of consumption. Finally, we use our data to identify false information domains that will cease operations after an event of interest, in our case the 2016 U.S. presidential election. We theorize that the last category involves actors seeking primarily to manipulate perceptions and outcomes of that event.","PeriodicalId":189628,"journal":{"name":"InfoSciRN: Machine Learning (Sub-Topic)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127746937","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

Deep Learning, Predictability, and Optimal Portfolio Returns 深度学习、可预测性和最优投资组合回报

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2020-09-07 DOI: 10.2139/ssrn.3688577

M. Babiak, Jozef Baruník

引用次数: 5

Impute, Select, Decision Tree and Naïve Bayes (ISE-DNC): An Ensemble Learning Approach to Classify the Lung Cancer Impute, Select, Decision Tree and Naïve Bayes (ISE-DNC):一种肺癌分类的集成学习方法

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2020-08-05 DOI: 10.2139/ssrn.3667438

S. Bhanumathi, N. ChandrashekaraS

引用次数: 0

AI and Algorithmic Bias: Source, Detection, Mitigation and Implications 人工智能和算法偏差:来源、检测、缓解和影响

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2020-07-26 DOI: 10.2139/ssrn.3681517

Runshan Fu, Yan Huang, Param Vir Singh

{"title":"AI and Algorithmic Bias: Source, Detection, Mitigation and Implications","authors":"Runshan Fu, Yan Huang, Param Vir Singh","doi":"10.2139/ssrn.3681517","DOIUrl":"https://doi.org/10.2139/ssrn.3681517","url":null,"abstract":"Artificial intelligence (AI) and machine learning (ML) algorithms are widely used throughout our economy in making decisions that have far-reaching impacts on employment, education, access to credit, and other areas. Initially considered neutral and fair, ML algorithms have recently been found increasingly biased, creating and perpetuating structural inequalities in society. With the rising concerns about algorithmic bias, a growing body of literature attempts to understand and resolve the issue of algorithmic bias. In this tutorial, we discuss five important aspects of algorithmic bias. We start with its definition and the notions of fairness policy makers, practitioners, and academic researchers have used and proposed. Next, we note the challenges in identifying and detecting algorithmic bias given the observed decision outcome, and we describe methods for bias detection. We then explain the potential sources of algorithmic bias and review several bias-correction methods. Finally, we discuss how agents’ strategic behavior may lead to biased societal outcomes, even when the algorithm itself is unbiased. We conclude by discussing open questions and future research directions.","PeriodicalId":189628,"journal":{"name":"InfoSciRN: Machine Learning (Sub-Topic)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124314075","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: 12

Addressing Big Data and AI Challenges: A Taxonomy and Why the GDPR Cannot Provide a One-size-fits-all Solution 应对大数据和人工智能的挑战:一个分类和为什么GDPR不能提供一个通用的解决方案

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2020-07-17 DOI: 10.2139/ssrn.3654119

M. Tzanou

引用次数: 0

Machine Learning and IoT based Real Time Parking System: Challenges and Implementation 基于机器学习和物联网的实时停车系统:挑战和实施

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2020-03-29 DOI: 10.2139/ssrn.3563377

R. Gupta, Geeta Rani

{"title":"Machine Learning and IoT based Real Time Parking System: Challenges and Implementation","authors":"R. Gupta, Geeta Rani","doi":"10.2139/ssrn.3563377","DOIUrl":"https://doi.org/10.2139/ssrn.3563377","url":null,"abstract":"There is a tremendous increase in number of vehicles in last two decades. So, it becomes important to make effective use of technology to enable hassle free parking at public and/or private places. In traditional parking systems, drivers face difficulty in finding available parking slots. These systems ignore the fact of parking the vehicles on roads, time management in peak hours, wrong parking of a vehicle in a parking slot. Moreover, the traditional systems require more human intervention in a parking zone. To deal with above said issues, there is an urgent requirement of developing Smart Parking Systems. In this manuscript, the authors propose a Smart Parking System based on IoT and Machine learning techniques to answer the real time management of parking and uncertainties. The proposed solution utilizes smart sensors, cloud computing and cyber physical system. Development of graphical user interface for administrator and end-user is a major challenge as it requires to ensure smooth monitoring, control and security of parking system. Moreover, it needs to establish effortless coordination with an end-user. The proposed system is successful in smartly addressing the challenges such as indicating status of parking slot well in advance to end-user, use of reserved and unreserved parking slots, wrong parking, unauthorized parking, real time analysis of free and occupied slots, detecting multiple objects in a parking slot such as bike in car slot, fault detection in one or more components and traffic management during peak hours. The system minimizes the human intervention and saves time, money and energy.","PeriodicalId":189628,"journal":{"name":"InfoSciRN: Machine Learning (Sub-Topic)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122576861","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

Underwater Fish Images Classification by Deep Neural Network 基于深度神经网络的水下鱼类图像分类

InfoSciRN: Machine Learning (Sub-Topic) Pub Date : 2020-01-19 DOI: 10.2139/ssrn.3622031

A. Supriya, Chiluka Venkat, Aliketti Deepak, GV Hari Prasad

引用次数: 0