Evelina Liliequist, Andrea Aler Tubella, K. Danielsson, Coppélie Cocq
{"title":"Beyond the Binary --- Queering AI for an Inclusive Future","authors":"Evelina Liliequist, Andrea Aler Tubella, K. Danielsson, Coppélie Cocq","doi":"10.1145/3590141","DOIUrl":"https://doi.org/10.1145/3590141","url":null,"abstract":"","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":"30 1","pages":"31 - 33"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41358811","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 Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.
{"title":"OpenSpeaks before AI: Frameworks for Creating the AI/ML Building Blocks for Low-Resource Languages","authors":"Subhashish Panigrahi","doi":"10.1145/3591211","DOIUrl":"https://doi.org/10.1145/3591211","url":null,"abstract":"The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":" ","pages":"6 - 7"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45176848","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}
Alex H. Taylor, D. Rosner, Mikael Wiberg, E. Churchill
{"title":"Undoing Data Worlds","authors":"Alex H. Taylor, D. Rosner, Mikael Wiberg, E. Churchill","doi":"10.1145/3592847","DOIUrl":"https://doi.org/10.1145/3592847","url":null,"abstract":"","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":" ","pages":"5 - 5"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44353832","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}
avoid these harms. But understanding the goals of the system need not be a prerequisite for that. The ACM Code of Ethics and Professional Conduct (CEPC) largely focuses on the second question—of uncovering harm, avoiding harm, and speaking out against harm—but does not say much about defining the goals of systems built by computing professionals. CEPC at best prescribes broad goals such as building systems for the “benefit of society,” or slightly more specific goals such as “promoting fundamental human rights” or Two questions that are often encountered when evaluating the ethics of a technology project are Who is your product or service meant to benefit? and Is somebody being harmed by your product or service? These questions require different frameworks to answer them. The first question requires clarity on the objectives of the technology system and consequently helps understand whose needs these objectives are meant to serve. Answering the second question, however, does not require clarity on the goals of the system. If harms being caused by the system can be identified, then T What’s Missing in the ACM Code of Ethics and Professional Conduct
{"title":"What's Missing in the ACM Code of Ethics and Professional Conduct","authors":"Aaditeshwar Seth","doi":"10.1145/3588003","DOIUrl":"https://doi.org/10.1145/3588003","url":null,"abstract":"avoid these harms. But understanding the goals of the system need not be a prerequisite for that. The ACM Code of Ethics and Professional Conduct (CEPC) largely focuses on the second question—of uncovering harm, avoiding harm, and speaking out against harm—but does not say much about defining the goals of systems built by computing professionals. CEPC at best prescribes broad goals such as building systems for the “benefit of society,” or slightly more specific goals such as “promoting fundamental human rights” or Two questions that are often encountered when evaluating the ethics of a technology project are Who is your product or service meant to benefit? and Is somebody being harmed by your product or service? These questions require different frameworks to answer them. The first question requires clarity on the objectives of the technology system and consequently helps understand whose needs these objectives are meant to serve. Answering the second question, however, does not require clarity on the goals of the system. If harms being caused by the system can be identified, then T What’s Missing in the ACM Code of Ethics and Professional Conduct","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":"30 1","pages":"44 - 47"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46229670","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}
{"title":"Designing Data Physicalization Artifacts","authors":"Marijel Melo","doi":"10.1145/3589783","DOIUrl":"https://doi.org/10.1145/3589783","url":null,"abstract":"","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":" ","pages":"14 - 15"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45475164","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 above Tamil adage roughly translates to “Every shadow is a ghost to the eyes that hold fear!” To dismiss someone’s fear as paranoia is a contention for power about what can be admitted as “reasonable” while persuading others. Therefore, an ethical consideration of paranoia behooves us to ask who benefits from dismissing others’ fears and how such reasoning is enmeshed within design discourses. Expanding beyond a pathologizing conceptualization, I explore paranoia as a sociotechnical episteme—a way of knowing and making sense—that can offer a multitude of competing explanations and speculative expressions that arise out of suspicion. John Farrell offers a genealogy of suspicion in modern Western thought and characterizes paranoia as “a psychological tendency in which the intellectual powers of the sufferer are neither entirely undermined nor completely cut off from reality, but rather deployed with a particular distortion” [2]. Paranoid thinking deserves careful consideration because it cannot be readily dismissed as persecutory delusions of an individual. Farrell argues that “modern people identify with the paranoid character [because they] feel the need to account for their individual and collective failures, to set their own lives meaningfully in the context of their moral relations with others” [2]. Paranoia then can be characterized as an Other-oriented episteme that is inherently relational. Made manifest and mediated through a sociotechnical matrix of interactions, paranoid thinking becomes a form of “group thinking” that involves an orienting belief about “possessing a special insight into the epistemologies of enmity” [1]. Ieva Jusionyte and Daniel M. Goldstein illustrate “the multiple and shifting intersections of in/visibility and in/ security in today’s security-minded world” [3]. They assert that “paranoid concealment and creative camouflage are the modi operandi of contemporary security regimes, and the ability to manipulate visibility and to penetrate the opaque are key techno-discursive components of ongoing state projects of security” [3]. Wendy Hui Kyong Chun provocatively states, “To be paranoid is to think like a machine” [4]. Thus, to leave no stone unturned is a machine-logic response to perceived threat. We use digital vaccine passports that determine how human bodies can move across human-made borders and There was no place for him to go. No place he could hide. No place where his enemy didn’t exist. No escape from unconscious wakefulness. There was no rest. And so he just lay there with the nauseous pain of exhaustion.... Yet it was this constant and all-pervading pain that seemed to allow him to survive for without it the overwhelming anguish and terror of his mind would have destroyed him. — Hubert Selby Jr. (The Room, 1971)
{"title":"But I'm Not Paranoid!","authors":"Gopinaath Kannabiran","doi":"10.1145/3588997","DOIUrl":"https://doi.org/10.1145/3588997","url":null,"abstract":"The above Tamil adage roughly translates to “Every shadow is a ghost to the eyes that hold fear!” To dismiss someone’s fear as paranoia is a contention for power about what can be admitted as “reasonable” while persuading others. Therefore, an ethical consideration of paranoia behooves us to ask who benefits from dismissing others’ fears and how such reasoning is enmeshed within design discourses. Expanding beyond a pathologizing conceptualization, I explore paranoia as a sociotechnical episteme—a way of knowing and making sense—that can offer a multitude of competing explanations and speculative expressions that arise out of suspicion. John Farrell offers a genealogy of suspicion in modern Western thought and characterizes paranoia as “a psychological tendency in which the intellectual powers of the sufferer are neither entirely undermined nor completely cut off from reality, but rather deployed with a particular distortion” [2]. Paranoid thinking deserves careful consideration because it cannot be readily dismissed as persecutory delusions of an individual. Farrell argues that “modern people identify with the paranoid character [because they] feel the need to account for their individual and collective failures, to set their own lives meaningfully in the context of their moral relations with others” [2]. Paranoia then can be characterized as an Other-oriented episteme that is inherently relational. Made manifest and mediated through a sociotechnical matrix of interactions, paranoid thinking becomes a form of “group thinking” that involves an orienting belief about “possessing a special insight into the epistemologies of enmity” [1]. Ieva Jusionyte and Daniel M. Goldstein illustrate “the multiple and shifting intersections of in/visibility and in/ security in today’s security-minded world” [3]. They assert that “paranoid concealment and creative camouflage are the modi operandi of contemporary security regimes, and the ability to manipulate visibility and to penetrate the opaque are key techno-discursive components of ongoing state projects of security” [3]. Wendy Hui Kyong Chun provocatively states, “To be paranoid is to think like a machine” [4]. Thus, to leave no stone unturned is a machine-logic response to perceived threat. We use digital vaccine passports that determine how human bodies can move across human-made borders and There was no place for him to go. No place he could hide. No place where his enemy didn’t exist. No escape from unconscious wakefulness. There was no rest. And so he just lay there with the nauseous pain of exhaustion.... Yet it was this constant and all-pervading pain that seemed to allow him to survive for without it the overwhelming anguish and terror of his mind would have destroyed him. — Hubert Selby Jr. (The Room, 1971)","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":" ","pages":"18 - 20"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47692750","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}
{"title":"Throwing Spaghetti against the Wall: Why Technology Leaders Need to Invest More in HCI and UX","authors":"E. Churchill","doi":"10.1145/3589187","DOIUrl":"https://doi.org/10.1145/3589187","url":null,"abstract":"","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":"30 1","pages":"21 - 22"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43130479","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}
{"title":"Srravya Chandhiramowuli","authors":"Srravya Chandhiramowuli","doi":"10.1145/3592492","DOIUrl":"https://doi.org/10.1145/3592492","url":null,"abstract":"","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":"30 1","pages":"12 - 13"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41640158","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}
{"title":"Sunset on the American Dream 2","authors":"Eugenia Cheng","doi":"10.1145/3591451","DOIUrl":"https://doi.org/10.1145/3591451","url":null,"abstract":"","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":"30 1","pages":"72 - 72"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64073807","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}
different encoding choices, which could inform the development of visualization recommendation tools. However, when we surveyed current tools [2], we noticed a surprising pattern: They seem to reference few if any findings from graphical perception when recommending visual encodings. This result led us to another important question: Why aren’t current visualization recommendation tools incorporating experiment results and guidelines from graphical perception research? A natural starting point is to review the graphical perception literature and figure out which parts are most relevant to visualization recommendation tools. This led us to review 132 interesting works in graphical perception [3], from visualization textbooks to decadesold experiments of how people perceive bar charts to studies of what happens when you add iconography or other embellishments to visualizations, among others. The sheer breadth and depth of work was at times overwhelming, and we started to see the problems that developers were running into. For example, it’s a struggle to separate the papers (and textbooks) that are relevant to visualization recommendation from those that are A s data continues to grow at unprecedented rates, we encounter unique challenges in helping analysts make sense of it. A prime example involves visualizing the data, where an analyst may have to reduce thousands of data columns and billions of data records to a single visualization. This often involves selecting which columns to visualize; sampling, filtering, or aggregating the data down to a manageable number of records; and mapping the results to intuitive visual encodings such as positional axes, bar heights, or color hues. Every step of the way, the analyst must grapple with what to focus on and how to translate the focus into a compelling image. We see a small slice of this problem in Figure 1: We can generate many different visualizations for a movie dataset, but the default design choices can be problematic. For example, the line chart in Figure 1 is just a blob of blue pixels. How can visualization tools help analysts navigate this complex and even frustrating web of interconnected design decisions? We have seen an explosion of visualization recommendation tools responding to this challenge. These tools aim to reduce decision fatigue by automating part or even all of the visualization design process. We summarize how these tools behave based on what they aim to automate [2]: which parts of the data to focus on (recommending data columns, rows, queries, etc.), which visual encodings to apply (recommending scales, colors, shapes, etc.), or both. Graphical perception research Why aren’t current tools incorporating experiment results and guidelines from graphical perception research? Using Graphical Perception in Visualization Recommendation
{"title":"Using Graphical Perception in Visualization Recommendation","authors":"Zehua Zeng, L. Battle","doi":"10.1145/3588744","DOIUrl":"https://doi.org/10.1145/3588744","url":null,"abstract":"different encoding choices, which could inform the development of visualization recommendation tools. However, when we surveyed current tools [2], we noticed a surprising pattern: They seem to reference few if any findings from graphical perception when recommending visual encodings. This result led us to another important question: Why aren’t current visualization recommendation tools incorporating experiment results and guidelines from graphical perception research? A natural starting point is to review the graphical perception literature and figure out which parts are most relevant to visualization recommendation tools. This led us to review 132 interesting works in graphical perception [3], from visualization textbooks to decadesold experiments of how people perceive bar charts to studies of what happens when you add iconography or other embellishments to visualizations, among others. The sheer breadth and depth of work was at times overwhelming, and we started to see the problems that developers were running into. For example, it’s a struggle to separate the papers (and textbooks) that are relevant to visualization recommendation from those that are A s data continues to grow at unprecedented rates, we encounter unique challenges in helping analysts make sense of it. A prime example involves visualizing the data, where an analyst may have to reduce thousands of data columns and billions of data records to a single visualization. This often involves selecting which columns to visualize; sampling, filtering, or aggregating the data down to a manageable number of records; and mapping the results to intuitive visual encodings such as positional axes, bar heights, or color hues. Every step of the way, the analyst must grapple with what to focus on and how to translate the focus into a compelling image. We see a small slice of this problem in Figure 1: We can generate many different visualizations for a movie dataset, but the default design choices can be problematic. For example, the line chart in Figure 1 is just a blob of blue pixels. How can visualization tools help analysts navigate this complex and even frustrating web of interconnected design decisions? We have seen an explosion of visualization recommendation tools responding to this challenge. These tools aim to reduce decision fatigue by automating part or even all of the visualization design process. We summarize how these tools behave based on what they aim to automate [2]: which parts of the data to focus on (recommending data columns, rows, queries, etc.), which visual encodings to apply (recommending scales, colors, shapes, etc.), or both. Graphical perception research Why aren’t current tools incorporating experiment results and guidelines from graphical perception research? Using Graphical Perception in Visualization Recommendation","PeriodicalId":73404,"journal":{"name":"Interactions (New York, N.Y.)","volume":"30 1","pages":"23 - 25"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42880712","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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