A conversational framework for machine learning

    Research output: Contribution to conferencePaperpeer-review

    Abstract

    This position paper describes a framework for machine learning (ML) based on conversation, which positions human and machine participants in a learning system with feedback mechanisms separated into a level of actions and a level of descriptions. This social model is based on theories of human learning and systems theory, and is intended for programmers, domain experts, users and non-experts to make sense of human-machine learning systems. It enables
    journalistic questions to be raised about the descriptions, actions and feedback in a ML system, and furthermore addresses broader ethical dimensions of such a system such as trust and control, as ML systems become rapidly distributed and networked globally. Situated in art and design, I explore critical and creative uses of ML as an alternative to commercial ones, with the conversational framework outlined here intended for analysis and design of all these, across a broad range of applications.
    Original languageEnglish
    Publication statusPublished - 4 May 2019
    EventHuman Centred Machine Learning Perspectives : workshop at CHI 2019 conference - Glasgow, United Kingdom
    Duration: 4 May 20194 May 2019
    https://gonzoramos.github.io/hcmlperspectives/

    Conference

    ConferenceHuman Centred Machine Learning Perspectives
    Abbreviated titleCHI HCML workshop
    CountryUnited Kingdom
    CityGlasgow
    Period4/05/194/05/19
    Internet address

    Keywords

    • machine learning
    • art
    • systems
    • cybernetics
    • conversation

    ASJC Scopus subject areas

    • Human-Computer Interaction

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