Special Issue "AI for (and by) the People"

A special issue of Multimodal Technologies and Interaction (ISSN 2414-4088).

Deadline for manuscript submissions: closed (15 October 2021) | Viewed by 3702

Special Issue Editors

Dr. Alison M Smith-Renner
E-Mail Website
Guest Editor
Decisive Analytics Corporation, Arlington, VA, USA
Interests: interactive machine learning; explainable artificial intelligence; human–machine teaming; HCI
Dr. Gonzalo Ramos
E-Mail Website
Guest Editor
Microsoft Research + AI, Redmond, WA 98052, USA
Interests: interactive machine teaching; interactive machine learning; human-centered machine learning; interaction and visual design; information visualization; human-computer interaction
Mr. Gagan Bansal
E-Mail Website
Guest Editor
University of Washington, Seattle, WA, USA
Interests: artificial intelligence; machine learning; human–computer interaction

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the opportunities and challenges of designing and developing machine learning and artificial intelligence for people.

Increasingly, machine learning (ML) and artificial intelligence (AI) technologies power people-facing interactive and autonomous systems, for example, in healthcare, finance, entertainment, security, and other domains. Hopefully, these AI technologies will imbue such systems with more intelligent behavior and help to better serve the needs and goals of the stakeholders. Ultimately, the creation and use of such ML/AI-based systems is a people’s problem, motivating the need to study and design the interactions between ML and its stakeholders from a human-centered point of view. Human-centered machine learning (HCML) is an emerging interdisciplinary research area that shares this view and that considers human goals and behavior during the development of ML and AI-based systems.

We encourage authors to submit original works that are novel or extend existing work, survey and synthesize related fields, articulate a position, share recent case studies, or present thought-provoking design fictions about topics including but not limited to:

  • Development and evaluation of fair ML models;
  • Explanations and transparency of algorithmic decisions and models;
  • Usability challenges and evaluation methods for HCML systems;
  • Interactive machine learning techniques, algorithms, and interfaces;
  • Methods for stakeholder participation in ML design;
  • Interactive machine teaching methods and systems;
  • Algorithms and interfaces for supporting human–AI collaboration and augmenting human intelligence (e.g., in decision making);
  • Design guidelines for human-in-the-loop and machine-in-the-loop systems;
  • Case studies of real-world applications of HCML in industry;
  • Legal, ethical, and policy issues to consider when deploying HCML systems.

By collecting diverse perspectives, we hope to contribute to a shared understanding of the emerging HCML field, of how people think about, create, and use ML and AI systems.

Dr. Alison M Smith-Renner
Dr. Gonzalo Ramos
Mr. Gagan Bansal
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Multimodal Technologies and Interaction is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

Article
Dimension-Based Interactions with Virtual Assistants: A Co-Design Project with Design Fictions
Multimodal Technol. Interact. 2021, 5(12), 78; https://doi.org/10.3390/mti5120078 - 03 Dec 2021
Viewed by 997
Abstract
This study presents a co-design project that invites participants with little or no background in artificial intelligence (AI) and machine learning (ML) to design their ideal virtual assistants (VAs) for everyday (/daily) use. VAs are differently designed and function when integrated into people’s [...] Read more.
This study presents a co-design project that invites participants with little or no background in artificial intelligence (AI) and machine learning (ML) to design their ideal virtual assistants (VAs) for everyday (/daily) use. VAs are differently designed and function when integrated into people’s daily lives (e.g., voice-controlled VAs are designed to blend in based on their natural qualities). To further understand users’ ideas of their ideal VA designs, participants were invited to generate designs of personal VAs. However, end users may have unrealistic expectations of future technologies. Therefore, design fiction was adopted as a method of guiding the participants’ image of the future and carefully managing their realistic, as well as unrealistic, expectations of future technologies. The result suggests the need for a human–AI relationship based on controls with various dimensions (e.g., vocalness degree and autonomy level) instead of specific features. The design insights are discussed in detail. Additionally, the co-design process offers insights into how users can participate in AI/ML designs. Full article
(This article belongs to the Special Issue AI for (and by) the People)
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Article
A Survey of Domain Knowledge Elicitation in Applied Machine Learning
Multimodal Technol. Interact. 2021, 5(12), 73; https://doi.org/10.3390/mti5120073 - 24 Nov 2021
Cited by 1 | Viewed by 1333
Abstract
Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers [...] Read more.
Eliciting knowledge from domain experts can play an important role throughout the machine learning process, from correctly specifying the task to evaluating model results. However, knowledge elicitation is also fraught with challenges. In this work, we consider why and how machine learning researchers elicit knowledge from experts in the model development process. We develop a taxonomy to characterize elicitation approaches according to the elicitation goal, elicitation target, elicitation process, and use of elicited knowledge. We analyze the elicitation trends observed in 28 papers with this taxonomy and identify opportunities for adding rigor to these elicitation approaches. We suggest future directions for research in elicitation for machine learning by highlighting avenues for further exploration and drawing on what we can learn from elicitation research in other fields. Full article
(This article belongs to the Special Issue AI for (and by) the People)
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