Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning

Hagendorff, Thilo

doi:10.1007/s11023-021-09573-8

Cited by 24 publications

(13 citation statements)

References 123 publications

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“…Filtering empirical data in a way that only the desired moral judgments can become actual training stimuli by doing litmus tests with overarching ethical theories is not the only reasonable approach, though. Instead of filtering the training and label data, one can also "filter" people [41]. That is, social sorting techniques for selecting ethics experts and detecting effects of ethicsrelated biases on themselves could be deployed instead of an "unfiltered" crowdsourcing.…”

Section: Missing Ground Truthmentioning

confidence: 99%

Ethical and methodological challenges in building morally informed AI systems

Hagendorff

Danks

2022

AI Ethics

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Recent progress in large language models has led to applications that can (at least) simulate possession of full moral agency due to their capacity to report context-sensitive moral assessments in open-domain conversations. However, automating moral decision-making faces several methodological as well as ethical challenges. They arise in the fields of bias mitigation, missing ground truth for moral “correctness”, effects of bounded ethicality in machines, changes in moral norms over time, risks of using morally informed AI systems as actual advice, as well as societal implications an increasing importance of algorithmic moral decision-making would have. This paper comments on all these challenges and provides critical considerations for future research on full artificial moral agency. Importantly, some of the adduced challenges can be met by more careful technology design, but others necessarily require engagement with core problems of meta-ethics.

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Section: Missing Ground Truthmentioning

confidence: 99%

Ethical and methodological challenges in building morally informed AI systems

Hagendorff

Danks

2022

AI Ethics

Self Cite

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“…With the popularization of artificial intelligence technology in the entertainment industry, the traditional way of entertainment has been greatly impacted. For example, intelligent piano has been favored by many piano lovers because of its integration into artificial intelligence technology, which has also changed the traditional piano teaching mode [8]. Intelligent piano is not only the integration of new electroacoustic technology, artificial intelligence technology, automatic control technology, and network communication technology, but also the product of the development of traditional piano.…”

Section: Related Workmentioning

confidence: 99%

Piano Teaching Improvement Based on Machine Learning and Artificial Intelligence

Zhang

2022

Security and Communication Networks

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With the development of modern piano and the increasing demand of people for music hobbies, the traditional piano teaching mode had exposed more and more problems. Artificial intelligence accelerated the development of music education to a certain extent and effectively improved the teaching effect of teachers to piano lovers. If artificial intelligence and machine learning were organically integrated, the application of intelligent piano can be more wide. Therefore, this paper proposed a piano teaching model based on machine learning and artificial intelligence, which aimed to provide support for improving the quality of piano teaching. Firstly, based on the analysis of machine learning-related theories, this paper expounded the characteristics of neural network in data processing and gave the process of the integration of machine learning and artificial intelligence and its application advantages in music teaching. Secondly, according to the interactive needs between intelligent piano and learners, this paper put forward intelligent piano teaching assistance methods and constructed an intelligent piano teaching service management system by improving the teaching information resource base. Finally, it analyzed the influence of artificial intelligence on piano teaching from different angles. In order to test the effectiveness of the piano teaching mode proposed in this paper, the piano learners under different teaching modes were investigated and counted. From the piano teaching effect and the evaluation results of the piano practitioners on the teaching mode, it was known that compared with the traditional piano teaching mode, the piano teaching mode proposed in this paper had achieved remarkable results in both theoretical teaching and practical operation. It can significantly improve the quality of piano teaching and promote the enthusiasm of learners.

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“…e essence of oral English pronunciation recognition based on machine learning [12][13][14] is to classify and recognize each word in the input audio. e essence of oral English pronunciation recognition based on deep learning [15][16][17] is the same.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Based Assisted Teaching System for Oral English

Liu

2022

Security and Communication Networks

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The progress of global economic integration has forced English learners to have an urgent need to improve their oral English. College students’ oral English ability is currently the worst of the four abilities of listening, speaking, reading, and writing. The main reasons are internal and external. The internal reason is that the pronunciation characteristics of Chinese students are different from those of English. The external cause is that the practice environment and tools of oral English are not ideal, which affects the improvement of learners’ oral English. This study proposes using a deep learning algorithm (DLA) English in the evaluation of oral English quality to improve learners’ oral English level. The quality of oral English can be comprehensively evaluated in terms of pitch, speed of sound, and rhythm. The standard of pronunciation is the foundation of oral English and is the most critical factor. In many DLAs, the input unit of DNN at a certain moment and its upper and lower moment input units have no relationship and are independent of each other, and the timing dependencies of adjacent units are not fully considered. The results are generally not very good on speech recognition tasks. This study proposes a time-delay neural network (TDNN) and a long short-term memory (LSTM) network to calculate the posterior probability of the model state to model context-dependent features in order to solve this problem. The fusion model TDNN-LSTM is applied in the English spoken pronunciation recognition task. To compare the accuracy of oral English pronunciation, several classic DLAs are introduced. The experimental results show that the method described in this study has a number of advantages. Although the performance improvement of this method in terms of recognition accuracy is not large, a certain degree of improvement is also very important for the oral English teaching assistant system.

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Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning

Cited by 24 publications

References 123 publications

Ethical and methodological challenges in building morally informed AI systems

Ethical and methodological challenges in building morally informed AI systems

Piano Teaching Improvement Based on Machine Learning and Artificial Intelligence

A Deep Learning-Based Assisted Teaching System for Oral English

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