• 제목/요약/키워드: Reliability of artificial intelligence

검색결과 204건 처리시간 0.023초

인공지능 기반 화자 식별 기술의 불공정성 분석 (Analysis of unfairness of artificial intelligence-based speaker identification technology)

  • 신나연;이진민;노현;이일구
    • 융합보안논문지
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    • 제23권1호
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    • pp.27-33
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    • 2023
  • Covid-19으로 인한 디지털화는 인공지능 기반의 음성인식 기술을 급속하게 발전시켰다. 그러나 이 기술은 데이터셋이 일부 집단에 편향될 경우 인종 및 성차별과 같은 불공정한 사회적 문제를 초래하고 인공지능 서비스의 신뢰성과 보안성을 열화시키는 요인이 된다. 본 연구에서는 대표적인 인공지능의 CNN(Convolutional Neural Network) 모델인 VGGNet(Visual Geometry Group Network), ResNet(Residual neural Network), MobileNet을 활용한 편향된 데이터 환경에서 정확도에 기반한 불공정성을 비교 및 분석한다. 실험 결과에 따르면 Top1-accuracy에서 ResNet34가 여성과 남성이 91%, 89.9%로 가장 높은 정확도를 보였고, 성별 간 정확도 차는 ResNet18이 1.8%로 가장 작았다. 모델별 성별 간의 정확도 차이는 서비스 이용 시 남녀 간의 서비스 품질에 대한 차이와 불공정한 결과를 야기한다.

Implementation of Algorithm to Write Articles by Stock Robot

  • Sim, Da Hun;Shin, Seung Jung
    • International journal of advanced smart convergence
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    • 제5권4호
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    • pp.40-47
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    • 2016
  • Journalism robot by using a computer algorithm, while maintaining the precision and reliability of the existing media refers to an article which is automatically created. In this paper, we introduce 'stock robot' of robot journalism which writes securities articles and describe artificial intelligence algorithms in stages. Key steps of stock robot implemented artificial intelligence algorithm through four steps of data collection and storage, key event extraction, article content production, and article production. This research has developed a stock robot that collects and analyzes data on social issues and stock indexes for the last 2 years. In the future, as the algorithm is further developed, it becomes possible to write securities articles quickly and accurately through social issues. It will also provide customized information tailored to the user's preferences.

A Study on Factors Influencing AI Learning Continuity : Focused on Business Major Students

  • 박소현
    • 한국정보시스템학회지:정보시스템연구
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    • 제32권4호
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    • pp.189-210
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    • 2023
  • Purpose This study aims to investigate factors that positively influence the continuous Artificial Intelligence(AI) Learning Continuity of business major students. Design/methodology/approach To evaluate the impact of AI education, a survey was conducted among 119 business-related majors who completed a software/AI course. Frequency analysis was employed to examine the general characteristics of the sample. Furthermore, factor analysis using Varimax rotation was conducted to validate the derived variables from the survey items, and Cronbach's α coefficient was used to measure the reliability of the variables. Findings Positive correlations were observed between business major students' AI Learning Continuity and their AI Interest, AI Awareness, and Data Analysis Capability related to their majors. Additionally, the study identified that AI Project Awareness and AI Literacy Capability play pivotal roles as mediators in fostering AI Learning Continuity. Students who acquired problem-solving skills and related technologies through AI Projects Awareness showed increased motivation for AI Learning Continuity. Lastly, AI Self-Efficacy significantly influences students' AI Learning Continuity.

주요국의 지능로봇 정책 추진 현황과 시사점 (Status and Implications of Policies on Intelligent Robotics in Major Countries)

  • 고순주
    • 전자통신동향분석
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    • 제39권3호
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    • pp.25-35
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    • 2024
  • As artificial intelligence advances, major countries are actively promoting the use of intelligent robots to improve industrial productivity and enhance the quality of life. As robots become more capable of interacting with humans, they are being increasingly integrated into the human realm. Accordingly, major countries are actively implementing policies to lead intelligent robot technology and secure market leadership. We examine the status of policies related to intelligent robots in five countries: United States, China, Japan, Germany, and South Korea. These countries apply 1) government-led intelligent robot policies, 2) investments to secure core robot technologies and promote the convergence of artificial intelligence and robots, 3) programs for research and development on intelligent robots, 4) strengthened human-centered human-robot interaction and collaboration, and 5) ethics, stability, and reliability in the development and use of robot technologies. For Korea to compete with major countries and promote the intelligent robot industry, high-risk, high-performance innovation projects should be prioritized.

첨단 인공지능 안전 및 신뢰성 기술 표준 동향 (Standardization Trends on Safety and Trustworthiness Technology for Advanced AI)

  • 전종홍
    • 전자통신동향분석
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    • 제39권5호
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    • pp.108-122
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    • 2024
  • Artificial Intelligence (AI) has rapidly evolved over the past decade and has advanced in areas such as language comprehension, image and video recognition, programming, and scientific reasoning. Recent AI technologies based on large language models and foundation models are approaching or surpassing artificial general intelligence. These systems demonstrate superior performance in complex problem-solving, natural language processing, and multidomain tasks, and can potentially transform fields such as science, industry, healthcare, and education. However, these advancements have raised concerns regarding the safety and trustworthiness of advanced AI, including risks related to uncontrollability, ethical conflicts, long-term socioeconomic impacts, and safety assurance. Efforts are being expended to develop internationally agreed-upon standards to ensure the safety and reliability of AI. This study analyzes international trends in safety and trustworthiness standardization for advanced AI, identifies key areas for standardization, proposes future directions and strategies, and draws policy implications. The goal is to support the safe and trustworthy development of advanced AI and enhance international competitiveness through effective standardization.

Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs

  • Kaan Orhan;Ceren Aktuna Belgin;David Manulis;Maria Golitsyna;Seval Bayrak;Secil Aksoy;Alex Sanders;Merve Onder;Matvey Ezhov;Mamat Shamshiev;Maxim Gusarev;Vladislav Shlenskii
    • Imaging Science in Dentistry
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    • 제53권3호
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    • pp.199-207
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    • 2023
  • Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs(PRs), as well as to assess the appropriateness of its treatment recommendations. Materials and Methods: PRs from 100 patients(representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.

A Study on the Development of Service Quality Scale in Traditional Market for Big Data Analysis

  • HWANG, Moon-Young
    • 한국인공지능학회지
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    • 제7권1호
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    • pp.23-59
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    • 2019
  • The purpose of this study is to develop a measure of service quality in the traditional market by examining previous research on the service quality of the traditional market studied so far. After defining basic concepts through definition of traditional market and existing studies, 5 categories of configuration items for SERVQUAL measurement in traditional market were made up based on existing researches related to definition of service quality and service quality of traditional market. A survey was conducted on the items that fit the intention of this study and various statistical analyzes were conducted. Statistical analysis was performed using SPSS 22.0 and AMOS 22.0. The reliability of the items was measured by the reliability test, and the predictability and accuracy of the items were examined. The validity of the measured variables was verified through confirmatory factor analysis. Reliability, empathy, responsiveness, certainty, and tangibility were the most important factors in this study. Responsiveness factors include communication, time reduction, real time, promptness. Assurance factors include the assurance of delivery, prompt answers, product knowledge items. Tangibility factors include, convenient device systems, location information, presence as a fact, and as a result, the latest modern items are adopted. The quality of service in the traditional market developed in this study was found to be good in reliability and validity test. Confirmatory factor analysis result using structural equation model also met the conformity index standard. If service satisfaction is measured based on this research, basic data can be presented to policy makers who implement policies on traditional markets to make the right decisions. In addition, it will be able to provide traditional market operators with operational strategy and marketing data. In the future, based on the traditional market service quality scale developed in this study, it is necessary to grasp the factors to be continuously managed to improve the service quality of the traditional market, user satisfaction, and intention to use.

Exploratory Analysis of AI-based Policy Decision-making Implementation

  • SunYoung SHIN
    • International Journal of Internet, Broadcasting and Communication
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    • 제16권1호
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    • pp.203-214
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    • 2024
  • This study seeks to provide implications for domestic-related policies through exploratory analysis research to support AI-based policy decision-making. The following should be considered when establishing an AI-based decision-making model in Korea. First, we need to understand the impact that the use of AI will have on policy and the service sector. The positive and negative impacts of AI use need to be better understood, guided by a public value perspective, and take into account the existence of different levels of governance and interests across public policy and service sectors. Second, reliability is essential for implementing innovative AI systems. In most organizations today, comprehensive AI model frameworks to enable and operationalize trust, accountability, and transparency are often insufficient or absent, with limited access to effective guidance, key practices, or government regulations. Third, the AI system is accountable. The OECD AI Principles set out five value-based principles for responsible management of trustworthy AI: inclusive growth, sustainable development and wellbeing, human-centered values and fairness values and fairness, transparency and explainability, robustness, security and safety, and accountability. Based on this, we need to build an AI-based decision-making system in Korea, and efforts should be made to build a system that can support policies by reflecting this. The limiting factor of this study is that it is an exploratory study of existing research data, and we would like to suggest future research plans by collecting opinions from experts in related fields. The expected effect of this study is analytical research on artificial intelligence-based decision-making systems, which will contribute to policy establishment and research in related fields.

지식베이스 구축을 위한 한국어 위키피디아의 학습 기반 지식추출 방법론 및 플랫폼 연구 (Knowledge Extraction Methodology and Framework from Wikipedia Articles for Construction of Knowledge-Base)

  • 김재헌;이명진
    • 지능정보연구
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    • 제25권1호
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    • pp.43-61
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    • 2019
  • 최근 4차 산업혁명과 함께 인공지능 기술에 대한 연구가 활발히 진행되고 있으며, 이전의 그 어느 때보다도 기술의 발전이 빠르게 진행되고 있는 추세이다. 이러한 인공지능 환경에서 양질의 지식베이스는 인공지능 기술의 향상 및 사용자 경험을 높이기 위한 기반 기술로써 중요한 역할을 하고 있다. 특히 최근에는 인공지능 스피커를 통한 질의응답과 같은 서비스의 기반 지식으로 활용되고 있다. 하지만 지식베이스를 구축하는 것은 사람의 많은 노력을 요하며, 이로 인해 지식을 구축하는데 많은 시간과 비용이 소모된다. 이러한 문제를 해결하기 위해 본 연구에서는 기계학습을 이용하여 지식베이스의 구조에 따라 학습을 수행하고, 이를 통해 자연어 문서로부터 지식을 추출하여 지식화하는 방법에 대해 제안하고자 한다. 이러한 방법의 적절성을 보이기 위해 DBpedia 온톨로지의 구조를 기반으로 학습을 수행하여 지식을 구축할 것이다. 즉, DBpedia의 온톨로지 구조에 따라 위키피디아 문서에 기술되어 있는 인포박스를 이용하여 학습을 수행하고 이를 바탕으로 자연어 텍스트로부터 지식을 추출하여 온톨로지화하기 위한 방법론을 제안하고자 한다. 학습을 바탕으로 지식을 추출하기 위한 과정은 문서 분류, 적합 문장 분류, 그리고 지식 추출 및 지식베이스 변환의 과정으로 이루어진다. 이와 같은 방법론에 따라 실제 지식 추출을 위한 플랫폼을 구축하였으며, 실험을 통해 본 연구에서 제안하고자 하는 방법론이 지식을 확장하는데 있어 유용하게 활용될 수 있음을 증명하였다. 이러한 방법을 통해 구축된 지식은 향후 지식베이스를 기반으로 한 인공지능을 위해 활용될 수 있을 것으로 판단된다.

중학생의 AI 핵심역량 측정을 위한 체크리스트 문항 개발 (Development of checklist questions to measure AI core competencies of middle school students)

  • 이은철;한정수
    • 사물인터넷융복합논문지
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    • 제10권3호
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    • pp.49-55
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    • 2024
  • 본 연구는 중학생의 AI 역량 측정을 위한 체크리스트 문항을 개발하는 목적으로 수행되었다. 연구의 목적을 달성하기 위해서 문헌 분석과 문항개발 델파이 조사를 사용하였다. 문헌 분석을 위해 검색을 통해 국내 연구 2편, 국외 연구 5편, 교육부의 교육과정 보고서를 수집하였다. 수집된 자료를 분석해서 핵심역량 측정 요소를 구성하였다. 핵심역량 측정 요소는 인공지능의 이해(5개 요소), 인공지능 사고(5개 요소), 인공지능 활용(4개 요수), 인공지능 윤리(6개 요소), 인공지능 사회-정서(6개 요소)로 구성하였다. 구성된 측정 요소의 지식과 기능 그리고 태도를 고려하여, 31개 문항을 개발하였다. 개발된 문항은 1차 델파이 조사를 통해서 검증하였고, 수정의견에 따라 10개의 문항을 수정하였다. 2차 델파이 조사를 통해서 31개 문항의 타당성을 검증하였다. 본 연구에서 개발한 체크리스트 문항은 자기보고식 설문이 아닌 수행 및 행동 관찰을 기반으로 교사의 평가에 의해서 측정된다. 이에 측정 결과가 신뢰할 수 있는 수준이 높아진다는 시사점을 가지고 있다.