• Journal of The Korean Association For Science Education
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• v.20 no.2
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• pp.214-220
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• 2000

The difference (bias) between self-efficacy and chemistry problem-solving ability was investigated for 96 (male: 48, female: 48) high school students. A self-efficacy instrument was administered, which asked the confidence in solving algorithmic and conceptual problems successfully. Their chemistry problem-solving ability was then assessed with 10 algorithmic and 10 conceptual problems as same in the self-efficacy instrument. Although students had higher scores in the algorithmic problems, no significant difference was found in the self-efficacy to solve the two different forms of problems. Therefore, the bias scores in the conceptual problems were higher than those in the algorithmic problems. Two-way ANOVA results for the bias in the algorithmic problems revealed a significant interaction between gender and the previous achievement level. Analysis of simple effects indicated that the bias scores of high-achieving boys were significantly higher than those of high-achieving girls. While most high-achieving boys were in the overconfident category, high-achieving girls were more likely to be in the underconfident category.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.12
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• pp.20-27
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• 1997

In this paper, a multiplying digital-to-analog converter (MDAC) circuit for low-power high-resolution CMOS algorithmic A/D converters (ADC's) is proposed. The proposed MDAC is designed to operte properly at a supply at a supply voltge between 3 V and 5 V and employs an analog0domain power reduction technique based on a bias switching circuit so that the total power consumption can be optimized. As metal-to-metal capacitors are implemented as frequency compensation capacitors, opamps' performance can be varied by imperfect process control. The MDAC minimizes the effects by the circuit performance variations with on-chip tuning circuits. The proposed low-power MDAC is implementd as a sub-block of a 10-bit 200kHz algorithmic ADC using a 0.6 um single-poly double-metal n-well CMOS technology. With the power-reduction technique enabled, the power consumption of the experimental ADC is reduced from 11mW to 7mW at a 3.3V supply voltage and the power reduction ratio of 36% is achieved.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.3
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• pp.69-76
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• 2008

This work proposes a 12b 1kS/s 65uA 0.35um CMOS algorithmic ADC for sensor interface applications such as accelerometers and gyro sensors requiring high resolution, ultra-low power, and small size simultaneously. The proposed ADC is based on an algorithmic architecture with recycling techniques to optimize sampling rate, resolution, chip area, and power consumption. Two versions of ADCs are fabricated with a conventional open-loop sampling scheme and a closed-loop sampling scheme to investigate the effects of offset and 1/f noise during dynamic operation. Switched bias power-reduction techniques and bias circuit sharing reduce the power consumption of amplifiers in the SHA and MDAC. The current and voltage references are implemented on chip with optional of-chip voltage references for low-power SoC applications. The prototype ADC in a 0.35um 2P4M CMOS technology demonstrates a measured DNL and INL within 0.78LSB and 2.24LSB, and shows a maximum SNDR and SFDR of 60dB and 70dB in versionl, and 63dB and 75dB in version2 at 1kS/s. The versionl and version2 ADCs with an active die area of $0.78mm^2$ and $0.81mm^2$ consume 0.163mW and 0.176mW at 1kS/s and 2.5V, respectively.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.12 s.354
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• pp.65-73
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• 2006

This work presents a 14b 200KS/s $0.87mm^2$ 1.2mW 0.18um CMOS algorithmic A/D converter (ADC) for intelligent sensors control systems, battery-powered system applications simultaneously requiring high resolution, low power, and small area. The proposed algorithmic ADC not using a conventional sample-and-hold amplifier employs efficient switched-bias power-reduction techniques in analog circuits, a clock selective sampling-capacitor switching in the multiplying D/A converter, and ultra low-power on-chip current and voltage references to optimize sampling rate, resolution, power consumption, and chip area. The prototype ADC implemented in a 0.18um 1P6M CMOS process shows a measured DNL and INL of maximum 0.98LSB and 15.72LSB, respectively. The ADC demonstrates a maximum SNDR and SFDR of 54dB and 69dB, respectively, and a power consumption of 1.2mW at 200KS/s and 1.8V. The occupied active die area is $0.87mm^2$.

• Journal of Science and Technology Studies
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• v.18 no.3
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• pp.153-216
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• 2018

The contemporary practices of Big-Data based automated decision making algorithms are widely deployed not just because we expect algorithmic decision making might distribute social resources in a more efficient way but also because we hope algorithms might make fairer decisions than the ones humans make with their prejudice, bias, and arbitrary judgment. However, there are increasingly more claims that algorithmic decision making does not do justice to those who are affected by the outcome. These unfair examples bring about new important questions such as how decision making was translated into processes and which factors should be considered to constitute to fair decision making. This paper attempts to delve into a bunch of research which addressed three areas of algorithmic application: criminal justice, law enforcement, and national security. By doing so, it will address some questions about whether artificial intelligence algorithm discriminates certain groups of humans and what are the criteria of a fair decision making process. Prior to the review, factors in each stage of data mining that could, either deliberately or unintentionally, lead to discriminatory results will be discussed. This paper will conclude with implications of this theoretical and practical analysis for the contemporary Korean society.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.59-68
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• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.713-722
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• 2024

This study highlights the impact of Artificial Intelligence (AI) technology on journalism, discussing its utility and addressing major ethical concerns. Broadcasting companies and media institutions, such as the Bloomberg, Guardian, WSJ, WP, NYT, globally are utilizing AI for innovation in news production, data analysis, and content generation. Accordingly, the ecosystem of AI journalism will be analyzed in terms of scale, economic feasibility, diversity, and value enhancement of major media AI service types. Through the previous literature review, this study identifies key ethical and social issues in AI journalism as well. It aims to bridge societal and technological concerns by exploring mutual development directions for AI technology and the media industry. Additionally, it advocates for the necessity of integrated guidelines and advanced AI literacy through social consensus in addressing these issues.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.11 s.353
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• pp.48-57
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• 2006

This work describes a 12b 200KHz 0.52mA $0.47mm^2$ algorithmic ADC for sensor applications such as motor controls, 3-phase power controls, and CMOS image sensors simultaneously requiring ultra-low power and small size. The proposed ADC is based on the conventional algorithmic architecture with recycling techniques to optimize sampling rate, resolution, chip area, and power consumption. The input SHA with eight input channels for high integration employs a folded-cascode architecture to achieve a required DC gain and a sufficient phase margin. A signal insensitive 3-D fully symmetrical layout with critical signal lines shielded reduces the capacitor and device mismatch of the MDAC. The improved switched bias power-reduction techniques reduce the power consumption of analog amplifiers. Current and voltage references are integrated on the chip with optional off-chip voltage references for low glitch noise. The employed down-sampling clock signal selects the sampling rate of 200KS/s or 10KS/s with a reduced power depending on applications. The prototype ADC in a 0.18um n-well 1P6M CMOS technology demonstrates the measured DNL and INL within 0.76LSB and 2.47LSB. The ADC shows a maximum SNDR and SFDR of 55dB and 70dB at all sampling frequencies up to 200KS/s, respectively. The active die area is $0.47mm^2$ and the chip consumes 0.94mW at 200KS/s and 0.63mW at 10KS/s at a 1.8V supply.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.