The International FLAIRS Conference Proceedings最新文献_第6页

Further Thoughts on Defining f(x) for Ethical Machines: Ethics, Rational Choice, and Risk Analysis 对伦理机器定义f(x)的进一步思考:伦理、理性选择和风险分析

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133203

Clayton Peterson

{"title":"Further Thoughts on Defining f(x) for Ethical Machines: Ethics, Rational Choice, and Risk Analysis","authors":"Clayton Peterson","doi":"10.32473/flairs.36.133203","DOIUrl":"https://doi.org/10.32473/flairs.36.133203","url":null,"abstract":"There is a tendency to anthropomorphize artificial intelligence (AI) and reify it as a person. From the perspective of machine ethics and ethical AI, this has resulted in the belief that truly autonomous ethical agents (i.e., machines and algorithms) can be defined, and that machines could, by themselves, behave ethically and perform actions that are justified from a normative standpoint. Under this assumption, and given that utilities and risks are generally seen as quantifiable, many scholars have seen consequentialism (utilitarianism) and rational choice theory as likely candidates to be implemented in automated ethical decision procedures, for instance to assess and manage risks as well as maximize expected utility. Building on a recent example from the machine ethics literature, we use computer simulations to argue that technical issues with ethical ramifications leave room for reasonable disagreement even when algorithms are based on ethical and rational foundations such as consequentialism and rational choice theory. By doing so, our aim is to illustrate the limitations of automated behavior and ethical AI and, incidentally, to raise awareness on the limits of so-called ethical agents.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123267025","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

Unsupervised Keyword Extraction for Hashtag Recommendation in Social Media 社交媒体标签推荐的无监督关键字提取

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133280

Behafarid Mohammad Jafari, Xiao Luo, Ali Jafari

{"title":"Unsupervised Keyword Extraction for Hashtag Recommendation in Social Media","authors":"Behafarid Mohammad Jafari, Xiao Luo, Ali Jafari","doi":"10.32473/flairs.36.133280","DOIUrl":"https://doi.org/10.32473/flairs.36.133280","url":null,"abstract":"Hashtag recommendation aims to suggest hashtags to users to annotate and describe the key information in the text, or categorize their posts. In recent years, several hashtag recommendation methods are proposed and developed to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This paper investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation. To do so, well-known unsupervised keyword extraction methods are applied to three real-world datasets including a new dataset containing texts of user-generated posts on a social learning platform. Experimental evaluations demonstrate that statistical methods performs newer methods including graph-based and embedding-based approaches in generating hashtags for long text, whereas the embedding-based approaches works better on generating hashtags for short texts. As a consequence, it can be concluded that unsupervised keyword extraction models can be adapted for hashtag recommendation when the social platform is new or there is no existing data to develop dedicated supervised learning models.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131756641","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: 8

Predicting the Effectiveness of Blockchain Bug Bounty Programs 预测区块链漏洞赏金计划的有效性

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133377

Ed Marcavage, Jake Mason, Chen Zhong

引用次数: 0

Analysis of Artificial Intelligence regulations for trustworthiness 人工智能法规对可信度的影响分析

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133301

Hyesung Park, Hun-Yeong Kwon

引用次数: 0

Visual Episodic Memory-based Exploration 基于视觉情景记忆的探索

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133322

J. Vice, Natalie Ruiz-Sanchez, P. Douglas, G. Sukthankar

{"title":"Visual Episodic Memory-based Exploration","authors":"J. Vice, Natalie Ruiz-Sanchez, P. Douglas, G. Sukthankar","doi":"10.32473/flairs.36.133322","DOIUrl":"https://doi.org/10.32473/flairs.36.133322","url":null,"abstract":"In humans, intrinsic motivation is an important mechanism for open-ended cognitive development; in robots, it has been shown to be valuable for exploration. An important aspect of human cognitive development is episodic memory which enables both the recollection of events from the past and the projection of subjective future. This paper explores the use of visual episodic memory as a source of intrinsic motivation for robotic exploration problems. Using a convolutional recurrent neural network autoencoder, the agent learns an efficient representation for spatiotemporal features such that accurate sequence prediction can only happen once spatiotemporal features have been learned. Structural similarity between ground truth and autoencoder generated images is used as an intrinsic motivation signal to guide exploration. Our proposed episodic memory model also implicitly accounts for the agent's actions, motivating the robot to seek new interactive experiences rather than just areas that are visually dissimilar. When guiding robotic exploration, our proposed method outperforms the Curiosity-driven Variational Autoencoder (CVAE) at finding dynamic anomalies.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"304 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134123411","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

Enhancing Accuracy and Explainability of Recidivism Prediction Models 提高累犯预测模型的准确性和可解释性

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133382

Tammy Babad, Soon Chun

引用次数: 0

Interpreting Predictive Models through Causality: A Query-Driven Methodology 通过因果关系解释预测模型:一种查询驱动的方法

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133387

Mahdi Hadj Ali, Yann Le Biannic, Pierre-Henri Wuillemin

{"title":"Interpreting Predictive Models through Causality: A Query-Driven Methodology","authors":"Mahdi Hadj Ali, Yann Le Biannic, Pierre-Henri Wuillemin","doi":"10.32473/flairs.36.133387","DOIUrl":"https://doi.org/10.32473/flairs.36.133387","url":null,"abstract":"Machine learning algorithms have been widely adopted in recent years due to their efficiency and versatility across many fields. However, the complexity of predictive models has led to a lack of interpretability in automatic decision-making. Recent works have improved general interpretability by estimating the contributions of input features to the prediction of a pre-trained model. Despite these advancements, practitioners still seek to gain causal insights into the underlying data-generating mechanisms. To this end, some works have attempted to integrate causal knowledge into interpretability, as non-causal techniques can lead to paradoxical explanations. These efforts have provided answers to various queries, but relying on a single pre-trained model may result in quantification problems. In this paper, we argue that each causal query requires its own reasoning; thus, a single predictive model is not suited for all questions. Instead, we propose a new framework that prioritizes the query of interest and then derives a query-driven methodology accordingly to the structure of the causal model. It results in a tailored predictive model adapted to the query and an adapted interpretability technique. Specifically, it provides a numerical estimate of causal effects, which allows for accurate answers to explanatory questions when the causal structure is known.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122061440","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

Medical Relevancy of Cancer-Related Tweets and Its Relation to Misinformation 癌症相关推文的医学相关性及其与错误信息的关系

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133364

Melanie McCord, Fahmida Hamid

引用次数: 0

Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach 鸡群算法:一种两步多群优化方法

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133368

M. Salido, A. Giret, Christian Pérez, Carlos March

{"title":"Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach","authors":"M. Salido, A. Giret, Christian Pérez, Carlos March","doi":"10.32473/flairs.36.133368","DOIUrl":"https://doi.org/10.32473/flairs.36.133368","url":null,"abstract":"Particle Swarm Optimization is a metaheuristic optimization algorithm inspired by the collective behavior of animal swarms where a set of candidate solutions, called particles, are randomly initialized in the search space, and their movements are iteratively updated based on their individual best solutions and the global best solution found by the swarm. This paper proposes a Multi-Swarm rooster colony algorithm (RCA) that considers a set of roosters, each owning a group of hens to compose a team. Each team (rooster and its hens) competes for the resource (food) with the other teams. From the combinatorial optimization point of view, each team analyzes part of the search space by an independent PSO algorithm with the same objective function. The RCA algorithm concurrently executes all PSO algorithms with different inertial weights for exploring different regions and the best solution (Gbest) of each team will compose the initial population for a new further centralized PSO algorithm that will exploit the previous solutions to search for the optimal one. Thus, the proposed RCA is composed of two steps, based on exploration and exploitation strategies to find an optimized solution in the search space. The results show that the proposed algorithm is competitive in solving well-known optimization functions. The objective is to apply this technique to solving real-life scheduling problems.","PeriodicalId":302103,"journal":{"name":"The International FLAIRS Conference Proceedings","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127718904","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

TinderAI: Support System for Matching AI Algorithms and Embedded Devices TinderAI: AI算法与嵌入式设备匹配的支持系统

The International FLAIRS Conference Proceedings Pub Date : 2023-05-08 DOI: 10.32473/flairs.36.133100

Matteo Francobaldi, A. D. Filippo, Andrea Borghesi, Nikola Pizurica, Igor Jovančević, Tim Llewellynn, Miguel de Prado

引用次数: 0