자료유형  

 학위논문

Control Number  

0016931964

International Standard Book Number  

9798379652432

Dewey Decimal Classification Number  

400

Main Entry-Personal Name  

Jain, Prachee.

Publication, Distribution, etc. (Imprint  

[S.l.] : Stanford University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(108 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

General Note  

Advisor: Christin, Angele;Valentine, Melissa;Hinds, Pamela.

Dissertation Note  

Thesis (Ph.D.)--Stanford University, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Artificial Intelligence (AI) technologies are increasingly becoming ubiquitous and invisible - nudging, making recommendations, influencing and making decisions, providing information, forming long-term relationships with users, or merely providing company. Despite this prevalence, few studies have examined how these technologies are designed and developed. This study examines how developers of AI technologies, faced with high levels of ambiguity, approach their work.Development of artificial intelligent (AI) technologies presents a unique phenomenon in which two of the three, input-process-output variables are ambiguous. There is opacity in cause-effect, that is, it is difficult if not impossible to know how inputs to AI technologies are related to outputs, thereby making multiple interpretations of causality plausible. The evaluation of outputs of AI technologies is often based on datasets called ground truth that are inherently ambiguous and dependent upon subjective decisions such as what categories to include in the classification system, and on the interpretation of people assigning these categories to different entities in the dataset.Through this ethnographic study of the development process of conversational AI technologies, I find that developers engage in three ambiguity attitudes - avoiding ambiguity by using manually coded, rule-based response generation techniques to exert control over the output of the technology. They also exhibit ambiguity seeking by employing opaque, deep learning, large language models to auto-generate responses to build resilience in the technology to produce an output in unexpected situations in which the technology would otherwise 'fall off the cliff' or fail. At the same time, developers attempt to resolve ambiguity by engaging in a process of building an empirical understanding from first principles of the phenomenon being automated, by ad hoc experimentation with proxy metrics and intuitions. I call this process 'reverse-building of phenomena.' Developers who embraced ambiguity and built resilient technologies fared better in the competition than those who did not.I contribute to an understanding of how modern-day work is changing for developers with the advent of opaque and ambiguous artificial intelligent technologies.

Subject Added Entry-Topical Term  

Language.

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Behavior.

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Software.

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Deep learning.

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Ambiguity.

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Verbal communication.

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Probability distribution.

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Technology.

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Natural language.

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Decision making.

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Medical research.

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Design.

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Probability.

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Subject specialists.

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Algorithms.

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Attitudes.

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Communication.

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Computer science.

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Medicine.

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Statistics.

Added Entry-Corporate Name  

Stanford University.

Host Item Entry  

Dissertations Abstracts International. 84-12B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:640520