Interactive machine teaching: a human-centered approach to building machine-learned models

Ramos, Gonzalo; Meek, Christopher; Simard, Patrice; Suh, Jina; Ghorashi, Soroush

doi:10.1080/07370024.2020.1734931

Cited by 79 publications

(60 citation statements)

References 49 publications

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“…Dove et al [209] surveyed to understand how design innovation is practiced in the ML domain in terms of user experience. [63], [70], [165], [174], [211] [124], [125], [128], [175], [217] [69], [112], [113], [118], [130], [131], [133], [135], [137], [140], [144], [152], [153], [155] [49], [57], [67], [98], [110], [114], [180], [192], [193], [203], [214], [215] [48], [59], [62], [75], [79], [111], [120], [156], [159], [185], [195], [197], [207], [218] [65], [83],…”

Section: Survey Resultsmentioning

confidence: 99%

“…They have conducted an evaluation study with ML students to validate their tool. To make machine learning models accessible to non-AI-experts, Ramos et al [ 195 ] showed how to leverage intrinsic human capabilities of teaching to teach machines to do machine learning.…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

“…Some of the prior works that target the developer as the human focus on novice ML engineers to help them develop ML systems faster [62,197]. Notably, the majority of works that target the developer side focused on ML engineers [59,65,71,75,79,120,156,160,185,195,196,198,199,201,207].…”

Section: The 'Human' In Hcmlmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

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Section: Survey Resultsmentioning

confidence: 99%

Section: Classifying Hcml Researchmentioning

confidence: 99%

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A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

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“…Some participants rationalized machine suggestions: even when they saw a decision they did not agree with, they forgave the system because the system did not have the full picture: "(P11) I feel like maybe I trained it poorly and it would not highlight good things, but I'm still super interested in that highlighting feature." Other participants wanted to correct an incorrect suggestion by teaching the system what it did wrong or what to look for in clips, highlighting a "teaching" moment that future systems could design for [29].…”

Section: Machine Suggestions Were Accepted As Partmentioning

confidence: 99%

ForSense: Accelerating Online Research Through Sensemaking Integration and Machine Research Support

Rachatasumrit

Ramos

Suh

et al. 2021

26th International Conference on Intelligent User Interfaces

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Online research is a frequent and important activity people perform on the Internet, yet current support for this task is basic, fragmented and not well integrated into web browser experiences. Guided by sensemaking theory, we present ForSense, a browser extension for accelerating people's online research experience. The two primary sources of novelty of ForSense are the integration of multiple stages of online research and providing machine assistance to the user by leveraging recent advances in neural-driven machine reading. We use ForSense as a design probe to explore (1) the benefits of integrating multiple stages of online research, (2) the opportunities to accelerate online research using current advances in machine reading, and (3) the opportunities to support online research tasks under the presence of imprecise machine suggestions. In our study, we observe people performing online research tasks, and see that they benefit from ForSense's integration and machine support for online research. From our study, we derive and share key recommendations for designing and supporting imprecise machine assistance for research tasks. CCS CONCEPTS• Human-centered computing → Interactive systems and tools; Empirical studies in interaction design; • Information systems → Web searching and information discovery.

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“…O desenvolvimento de aplicativos de ML não é trivial e o seu processo de desenvolvimento difere de um software tradicional, pois envolve adquirir um conjunto rotulado de exemplos, selecionar um algoritmo de aprendizado apropriado e seus parâmetros, treinar um modelo, avaliar as previsões desse modelo em relação ao conjunto de testes e, finalmente, sua implantação em produção (Ramos et al, 2020). Normalmente, os modelos de ML são desenvolvidos usando linguagens de programação baseadas em texto que requerem codificação.…”

Section: Introductionunclassified

Ferramentas Visuais para o Ensino de Machine Learning na Educação Básica

Wangenheim

Hauck

Pacheco³

et al. 2021

RENOTE

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Ensinar Machine Learning (ML) em escolas ajuda a preparar estudantes para lidar com questões sociais, econômicas e éticas do século 21. Isso requer ferramentas apropriadas a cada idade que permitem que os estudantes criem uma concepção abrangente de ML e sua aplicação. Desse modo, apresentamos um mapeamento de ferramentas visuais emergentes que ajudam a ensinar conceitos de ML na Educação Básica e caracterizamos as ferramentas em relação ao suporte para o desenvolvimento e deployment de modelos de ML em contextos educacionais. Os resultados podem ajudar designers instrucionais e instrutores a selecionar ferramentas apropriadas a fim de contribuir para a democratização de competências em ML.

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Interactive machine teaching: a human-centered approach to building machine-learned models

Cited by 79 publications

References 49 publications

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

ForSense: Accelerating Online Research Through Sensemaking Integration and Machine Research Support

Ferramentas Visuais para o Ensino de Machine Learning na Educação Básica

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