Humans can play a decisive role in detecting and mitigating cyber attacks if they possess sufficient cybersecurity skills and knowledge. Realizing this potential requires effective cybersecurity training. Cyber range exercises (CRXs) represent a novel form of cybersecurity training in which trainees can experience realistic cyber attacks in authentic environments. Although evaluation is undeniably essential for any learning environment, it has been widely neglected in CRX research. Addressing this issue, we propose a taxonomy-based framework to facilitate a comprehensive and structured evaluation of CRXs. To demonstrate the applicability and potential of the framework, we instantiate it to evaluate Iceberg CRX, a training we recently developed to improve cybersecurity education at our university. For this matter, we conducted a user study with 50 students to identify both strengths and weaknesses of the CRX.
{"title":"Train as you Fight: Evaluating Authentic Cybersecurity Training in Cyber Ranges","authors":"M. Glas, Manfred Vielberth, Guenther Pernul","doi":"10.1145/3544548.3581046","DOIUrl":"https://doi.org/10.1145/3544548.3581046","url":null,"abstract":"Humans can play a decisive role in detecting and mitigating cyber attacks if they possess sufficient cybersecurity skills and knowledge. Realizing this potential requires effective cybersecurity training. Cyber range exercises (CRXs) represent a novel form of cybersecurity training in which trainees can experience realistic cyber attacks in authentic environments. Although evaluation is undeniably essential for any learning environment, it has been widely neglected in CRX research. Addressing this issue, we propose a taxonomy-based framework to facilitate a comprehensive and structured evaluation of CRXs. To demonstrate the applicability and potential of the framework, we instantiate it to evaluate Iceberg CRX, a training we recently developed to improve cybersecurity education at our university. For this matter, we conducted a user study with 50 students to identify both strengths and weaknesses of the CRX.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127204117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present Health Buddy, a voice agent integrated into commercially available Voice User Interfaces (VUIs) to support informal self-regulated learning (SRL) of health-related topics through multiple learning strategies and examine the efficacy of Health Buddy on learning outcomes for younger and older adults. We conducted a mixed-factorial-design experiment with 26 younger and 25 older adults, assigned to three SRL strategies (within-subjects): monologue, dialogue-based scaffolding building, and conceptual diagramming. We found that while younger adults benefit more from scaffolding building and conceptual diagramming, both younger and older adults showed equivalent learning outcomes. Furthermore, interaction fluency (operationalized by the number of conversational breakdowns) was associated with learning outcomes regardless of age. While older adults did not experience less fluent conversations, interaction fluency affected their technology acceptance toward VUIs more than younger ones. Our study discusses age-related learning differences and has implications for designing VUI-based learning programs for older adults.
{"title":"OK Google, Let's Learn: Using Voice User Interfaces for Informal Self-Regulated Learning of Health Topics among Younger and Older Adults","authors":"Smit Desai, Jessie Chin","doi":"10.1145/3544548.3581507","DOIUrl":"https://doi.org/10.1145/3544548.3581507","url":null,"abstract":"In this paper, we present Health Buddy, a voice agent integrated into commercially available Voice User Interfaces (VUIs) to support informal self-regulated learning (SRL) of health-related topics through multiple learning strategies and examine the efficacy of Health Buddy on learning outcomes for younger and older adults. We conducted a mixed-factorial-design experiment with 26 younger and 25 older adults, assigned to three SRL strategies (within-subjects): monologue, dialogue-based scaffolding building, and conceptual diagramming. We found that while younger adults benefit more from scaffolding building and conceptual diagramming, both younger and older adults showed equivalent learning outcomes. Furthermore, interaction fluency (operationalized by the number of conversational breakdowns) was associated with learning outcomes regardless of age. While older adults did not experience less fluent conversations, interaction fluency affected their technology acceptance toward VUIs more than younger ones. Our study discusses age-related learning differences and has implications for designing VUI-based learning programs for older adults.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127377383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Bhat, Neha Kumar, Karthik Shamanna, Nipun Kwatra, Mohit Jain
The growing platformization of health has spurred new avenues for healthcare access and reinvigorated telemedicine as a viable pathway to care. Telemedicine adoption during the COVID-19 pandemic has surfaced barriers to patient-centered care that call for attention. Our work extends current Human-Computer Interaction (HCI) research on telemedicine and the challenges to remote care, and investigates the scope for enhancing remote care seeking and provision through telemedicine workflows involving intermediation. Our study, focused on the urban Indian context, involved providing doctors with videos of remote clinical examinations to aid in telemedicine. We present a qualitative evaluation of this modified telemedicine experience, highlighting how workflows involving intermediation could bridge existing gaps in telemedicine, and how their acceptance among doctors could shift interaction dynamics between doctors and patients. We conclude by discussing the implications of such telemedicine workflows on patient-centered care and the future of care work.
{"title":"Towards Intermediated Workflows for Hybrid Telemedicine","authors":"K. Bhat, Neha Kumar, Karthik Shamanna, Nipun Kwatra, Mohit Jain","doi":"10.1145/3544548.3580653","DOIUrl":"https://doi.org/10.1145/3544548.3580653","url":null,"abstract":"The growing platformization of health has spurred new avenues for healthcare access and reinvigorated telemedicine as a viable pathway to care. Telemedicine adoption during the COVID-19 pandemic has surfaced barriers to patient-centered care that call for attention. Our work extends current Human-Computer Interaction (HCI) research on telemedicine and the challenges to remote care, and investigates the scope for enhancing remote care seeking and provision through telemedicine workflows involving intermediation. Our study, focused on the urban Indian context, involved providing doctors with videos of remote clinical examinations to aid in telemedicine. We present a qualitative evaluation of this modified telemedicine experience, highlighting how workflows involving intermediation could bridge existing gaps in telemedicine, and how their acceptance among doctors could shift interaction dynamics between doctors and patients. We conclude by discussing the implications of such telemedicine workflows on patient-centered care and the future of care work.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127459909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Nazari, Ali Shahidi, Kate M. Kaufman, Julia E Bondi, Lorans Alabood, Vikram K. Jaswal, Diwakar Krishnamurthy, Mea Wang
About one-third of autistic people are nonspeaking, and most are never provided access to an effective alternative to speech. Thoughtfully designed AR applications could provide members of this population with structured learning opportunities, including training on skills that underlie alternative forms of communication. A fundamental step toward creating such opportunities, however, is to investigate nonspeaking autistic people’s ability to tolerate a head-mounted AR device and to interact with virtual objects. We present the first study to examine the usability of an interactive AR-based application by this population. We recruited 17 nonspeaking autistic subjects to play a HoloLens 2 game we developed that involved holographic animations and buttons. Almost all subjects tolerated the device long enough to begin the game, and most completed increasingly challenging tasks that involved pressing holographic buttons. Based on the results, we discuss best practice design and process recommendations. Our findings contradict prevailing assumptions about nonspeaking autistic people and thus open up exciting possibilities for AR-based solutions for this understudied and underserved population.
{"title":"Interactive AR Applications for Nonspeaking Autistic People? - A Usability Study","authors":"A. Nazari, Ali Shahidi, Kate M. Kaufman, Julia E Bondi, Lorans Alabood, Vikram K. Jaswal, Diwakar Krishnamurthy, Mea Wang","doi":"10.1145/3544548.3580721","DOIUrl":"https://doi.org/10.1145/3544548.3580721","url":null,"abstract":"About one-third of autistic people are nonspeaking, and most are never provided access to an effective alternative to speech. Thoughtfully designed AR applications could provide members of this population with structured learning opportunities, including training on skills that underlie alternative forms of communication. A fundamental step toward creating such opportunities, however, is to investigate nonspeaking autistic people’s ability to tolerate a head-mounted AR device and to interact with virtual objects. We present the first study to examine the usability of an interactive AR-based application by this population. We recruited 17 nonspeaking autistic subjects to play a HoloLens 2 game we developed that involved holographic animations and buttons. Almost all subjects tolerated the device long enough to begin the game, and most completed increasingly challenging tasks that involved pressing holographic buttons. Based on the results, we discuss best practice design and process recommendations. Our findings contradict prevailing assumptions about nonspeaking autistic people and thus open up exciting possibilities for AR-based solutions for this understudied and underserved population.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123271674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Li, Thomas Chen, Alec Zadikian, Albert Tung, Lydia B. Chilton
Longform spoken dialog delivers rich streams of informative content through podcasts, interviews, debates, and meetings. While production of this medium has grown tremendously, spoken dialog remains challenging to consume as listening is slower than reading and difficult to skim or navigate relative to text. Recent systems leveraging automatic speech recognition (ASR) and automatic summarization allow users to better browse speech data and forage for information of interest. However, these systems intake disfluent speech which causes automatic summarization to yield readability, adequacy, and accuracy problems. To improve navigability and browsability of speech, we present three training agnostic post-processing techniques that address dialog concerns of readability, coherence, and adequacy. We integrate these improvements with user interfaces which communicate estimated summary metrics to aid user browsing heuristics. Quantitative evaluation metrics show a 19% improvement in summary quality. We discuss how summarization technologies can help people browse longform audio in trustworthy and readable ways.
{"title":"Improving Automatic Summarization for Browsing Longform Spoken Dialog","authors":"Daniel Li, Thomas Chen, Alec Zadikian, Albert Tung, Lydia B. Chilton","doi":"10.1145/3544548.3581339","DOIUrl":"https://doi.org/10.1145/3544548.3581339","url":null,"abstract":"Longform spoken dialog delivers rich streams of informative content through podcasts, interviews, debates, and meetings. While production of this medium has grown tremendously, spoken dialog remains challenging to consume as listening is slower than reading and difficult to skim or navigate relative to text. Recent systems leveraging automatic speech recognition (ASR) and automatic summarization allow users to better browse speech data and forage for information of interest. However, these systems intake disfluent speech which causes automatic summarization to yield readability, adequacy, and accuracy problems. To improve navigability and browsability of speech, we present three training agnostic post-processing techniques that address dialog concerns of readability, coherence, and adequacy. We integrate these improvements with user interfaces which communicate estimated summary metrics to aid user browsing heuristics. Quantitative evaluation metrics show a 19% improvement in summary quality. We discuss how summarization technologies can help people browse longform audio in trustworthy and readable ways.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123451587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The launch of open governmental data portals (OGDPs) has popularized the open data movement of last decade. Although the amount of data in OGDPs is increasing, their functionalities are limited to finding datasets with titles/descriptions and downloading the actual files. This hinders the end users, especially those without technical skills, to find the open data tables and make use of them. We present Governor, an open-sourced[17] web application developed to make OGDPs more accessible to end users by facilitating searching actual records in the tables, previewing them directly without downloading, and suggesting joinable and unionable tables to users based on their latest working tables. Governor also manages the provenance of integrated tables allowing users and their collaborators to easily trace back to the original tables in OGDP. We evaluate Governor with a two-part user study and the results demonstrate its value and effectiveness in finding and integrating tables in OGDP.
{"title":"Governor: Turning Open Government Data Portals into Interactive Databases","authors":"Chang Liu, Arif Usta, J. Zhao, S. Salihoglu","doi":"10.1145/3544548.3580868","DOIUrl":"https://doi.org/10.1145/3544548.3580868","url":null,"abstract":"The launch of open governmental data portals (OGDPs) has popularized the open data movement of last decade. Although the amount of data in OGDPs is increasing, their functionalities are limited to finding datasets with titles/descriptions and downloading the actual files. This hinders the end users, especially those without technical skills, to find the open data tables and make use of them. We present Governor, an open-sourced[17] web application developed to make OGDPs more accessible to end users by facilitating searching actual records in the tables, previewing them directly without downloading, and suggesting joinable and unionable tables to users based on their latest working tables. Governor also manages the provenance of integrated tables allowing users and their collaborators to easily trace back to the original tables in OGDP. We evaluate Governor with a two-part user study and the results demonstrate its value and effectiveness in finding and integrating tables in OGDP.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123484511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, Qiang Yang
Pre-trained large language models (“LLMs”) like GPT-3 can engage in fluent, multi-turn instruction-taking out-of-the-box, making them attractive materials for designing natural language interactions. Using natural language to steer LLM outputs (“prompting”) has emerged as an important design technique potentially accessible to non-AI-experts. Crafting effective prompts can be challenging, however, and prompt-based interactions are brittle. Here, we explore whether non-AI-experts can successfully engage in “end-user prompt engineering” using a design probe—a prototype LLM-based chatbot design tool supporting development and systematic evaluation of prompting strategies. Ultimately, our probe participants explored prompt designs opportunistically, not systematically, and struggled in ways echoing end-user programming systems and interactive machine learning systems. Expectations stemming from human-to-human instructional experiences, and a tendency to overgeneralize, were barriers to effective prompt design. These findings have implications for non-AI-expert-facing LLM-based tool design and for improving LLM-and-prompt literacy among programmers and the public, and present opportunities for further research.
{"title":"Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts","authors":"J. Zamfirescu-Pereira, Richmond Y. Wong, Bjoern Hartmann, Qiang Yang","doi":"10.1145/3544548.3581388","DOIUrl":"https://doi.org/10.1145/3544548.3581388","url":null,"abstract":"Pre-trained large language models (“LLMs”) like GPT-3 can engage in fluent, multi-turn instruction-taking out-of-the-box, making them attractive materials for designing natural language interactions. Using natural language to steer LLM outputs (“prompting”) has emerged as an important design technique potentially accessible to non-AI-experts. Crafting effective prompts can be challenging, however, and prompt-based interactions are brittle. Here, we explore whether non-AI-experts can successfully engage in “end-user prompt engineering” using a design probe—a prototype LLM-based chatbot design tool supporting development and systematic evaluation of prompting strategies. Ultimately, our probe participants explored prompt designs opportunistically, not systematically, and struggled in ways echoing end-user programming systems and interactive machine learning systems. Expectations stemming from human-to-human instructional experiences, and a tendency to overgeneralize, were barriers to effective prompt design. These findings have implications for non-AI-expert-facing LLM-based tool design and for improving LLM-and-prompt literacy among programmers and the public, and present opportunities for further research.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126782060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Wang, Andrew Head, Ashley Ge Zhang, Steve Oney, Christopher Brooks
Multi-stage programming tutorials are key learning resources for programmers, using progressive incremental steps to teach them how to build larger software systems. A good multi-stage tutorial describes the code clearly, explains the rationale and code changes for each step, and allows readers to experiment as they work through the tutorial. In practice, it is time-consuming for authors to create tutorials with these attributes. In this paper, we introduce Colaroid, an interactive authoring tool for creating high quality multi-stage tutorials. Colaroid tutorials are augmented computational notebooks, where snippets and outputs represent a snapshot of a project, with source code differences highlighted, complete source code context for each snippet, and the ability to load and tinker with any stage of the project in a linked IDE. In two laboratory studies, we found Colaroid makes it easy to create multi-stage tutorials, while offering advantages to readers compared to video and web-based tutorials.
{"title":"Colaroid: A Literate Programming Approach for Authoring Explorable Multi-Stage Tutorials","authors":"A. Wang, Andrew Head, Ashley Ge Zhang, Steve Oney, Christopher Brooks","doi":"10.1145/3544548.3581525","DOIUrl":"https://doi.org/10.1145/3544548.3581525","url":null,"abstract":"Multi-stage programming tutorials are key learning resources for programmers, using progressive incremental steps to teach them how to build larger software systems. A good multi-stage tutorial describes the code clearly, explains the rationale and code changes for each step, and allows readers to experiment as they work through the tutorial. In practice, it is time-consuming for authors to create tutorials with these attributes. In this paper, we introduce Colaroid, an interactive authoring tool for creating high quality multi-stage tutorials. Colaroid tutorials are augmented computational notebooks, where snippets and outputs represent a snapshot of a project, with source code differences highlighted, complete source code context for each snippet, and the ability to load and tinker with any stage of the project in a linked IDE. In two laboratory studies, we found Colaroid makes it easy to create multi-stage tutorials, while offering advantages to readers compared to video and web-based tutorials.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122375784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fengjie Wang, Xuye Liu, Oujing Liu, Ali Neshati, Tengfei Ma, Min Zhu, J. Zhao
Data scientists often have to use other presentation tools (e.g., Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much tedious and repetitive work is needed to transfer the routines of notebooks (e.g., code, plots) to the presentable contents on slides (e.g., bullet points, figures). We propose a human-AI collaborative approach and operationalize it within Slide4N, an interactive AI assistant for data scientists to create slides from computational notebooks. Slide4N leverages advanced natural language processing techniques to distill key information from user-selected notebook cells and then renders them in appropriate slide layouts. The tool also provides intuitive interactions that allow further refinement and customization of the generated slides. We evaluated Slide4N with a two-part user study, where participants appreciated this human-AI collaborative approach compared to fully-manual or fully-automatic methods. The results also indicate the usefulness and effectiveness of Slide4N in slide creation tasks from notebooks.
{"title":"Slide4N: Creating Presentation Slides from Computational Notebooks with Human-AI Collaboration","authors":"Fengjie Wang, Xuye Liu, Oujing Liu, Ali Neshati, Tengfei Ma, Min Zhu, J. Zhao","doi":"10.1145/3544548.3580753","DOIUrl":"https://doi.org/10.1145/3544548.3580753","url":null,"abstract":"Data scientists often have to use other presentation tools (e.g., Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much tedious and repetitive work is needed to transfer the routines of notebooks (e.g., code, plots) to the presentable contents on slides (e.g., bullet points, figures). We propose a human-AI collaborative approach and operationalize it within Slide4N, an interactive AI assistant for data scientists to create slides from computational notebooks. Slide4N leverages advanced natural language processing techniques to distill key information from user-selected notebook cells and then renders them in appropriate slide layouts. The tool also provides intuitive interactions that allow further refinement and customization of the generated slides. We evaluated Slide4N with a two-part user study, where participants appreciated this human-AI collaborative approach compared to fully-manual or fully-automatic methods. The results also indicate the usefulness and effectiveness of Slide4N in slide creation tasks from notebooks.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122778009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are “coded” into language and the assumption of “male” as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.
{"title":"Transcending the “Male Code”: Implicit Masculine Biases in NLP Contexts","authors":"Katie Seaborn, S. Chandra, Thibault Fabre","doi":"10.1145/3544548.3581017","DOIUrl":"https://doi.org/10.1145/3544548.3581017","url":null,"abstract":"Critical scholarship has elevated the problem of gender bias in data sets used to train virtual assistants (VAs). Most work has focused on explicit biases in language, especially against women, girls, femme-identifying people, and genderqueer folk; implicit associations through word embeddings; and limited models of gender and masculinities, especially toxic masculinities, conflation of sex and gender, and a sex/gender binary framing of the masculine as diametric to the feminine. Yet, we must also interrogate how masculinities are “coded” into language and the assumption of “male” as the linguistic default: implicit masculine biases. To this end, we examined two natural language processing (NLP) data sets. We found that when gendered language was present, so were gender biases and especially masculine biases. Moreover, these biases related in nuanced ways to the NLP context. We offer a new dictionary called AVA that covers ambiguous associations between gendered language and the language of VAs.","PeriodicalId":314098,"journal":{"name":"Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122837494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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