A conversational framework for machine learning

Research output: Contribution to conferencePaper

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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  • Cite this

    Walker, K. (2019). A conversational framework for machine learning. Paper presented at Human Centred Machine Learning Perspectives , Glasgow, United Kingdom.