Interactions (New York, N.Y.)最新文献_第7页

Beyond the Binary --- Queering AI for an Inclusive Future 超越二元——为包容性的未来探索人工智能

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3590141

Evelina Liliequist, Andrea Aler Tubella, K. Danielsson, Coppélie Cocq

引用次数: 1

OpenSpeaks before AI: Frameworks for Creating the AI/ML Building Blocks for Low-Resource Languages 人工智能之前的OpenSpeaks：为低资源语言创建AI/ML构建块的框架

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3591211

Subhashish Panigrahi

引用次数: 0

Undoing Data Worlds 撤消数据世界

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3592847

Alex H. Taylor, D. Rosner, Mikael Wiberg, E. Churchill

引用次数: 0

What's Missing in the ACM Code of Ethics and Professional Conduct ACM道德与职业行为准则缺失了什么

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3588003

Aaditeshwar Seth

引用次数: 0

Designing Data Physicalization Artifacts 设计数据物理化工件

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3589783

Marijel Melo

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But I'm Not Paranoid! 但我不是偏执狂!

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3588997

Gopinaath Kannabiran

{"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}

引用次数: 0

Throwing Spaghetti against the Wall: Why Technology Leaders Need to Invest More in HCI and UX 把意大利面条扔到墙上：为什么技术领导者需要在HCI和用户体验上投入更多

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3589187

E. Churchill

引用次数: 0

Srravya Chandhiramowuli Srravya Chandhiramowuli

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3592492

Srravya Chandhiramowuli

引用次数: 0

Sunset on the American Dream 2 美国梦的日落2

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3591451

Eugenia Cheng

引用次数: 0

Using Graphical Perception in Visualization Recommendation 在可视化推荐中使用图形感知

Interactions (New York, N.Y.) Pub Date : 2023-05-01 DOI: 10.1145/3588744

Zehua Zeng, L. Battle

{"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}

引用次数: 0