Situating Data Sets: Making Public Data Actionable for Housing Justice
- URL: http://arxiv.org/abs/2402.12505v1
- Date: Mon, 19 Feb 2024 20:13:42 GMT
- Title: Situating Data Sets: Making Public Data Actionable for Housing Justice
- Authors: Anh-Ton Tran, Grace Guo, Jordan Taylor, Katsuki Chan, Elora Raymond,
Carl DiSalvo
- Abstract summary: We investigate and describe the work of making eviction data open to tenant organizers.
This work combines observation, direct participation in data work, and creating media artifacts, specifically digital maps.
- Score: 5.281983320884712
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Activists, governmentsm and academics regularly advocate for more open data.
But how is data made open, and for whom is it made useful and usable? In this
paper, we investigate and describe the work of making eviction data open to
tenant organizers. We do this through an ethnographic description of ongoing
work with a local housing activist organization. This work combines
observation, direct participation in data work, and creating media artifacts,
specifically digital maps. Our interpretation is grounded in D'Ignazio and
Klein's Data Feminism, emphasizing standpoint theory. Through our analysis and
discussion, we highlight how shifting positionalities from data intermediaries
to data accomplices affects the design of data sets and maps. We provide HCI
scholars with three design implications when situating data for grassroots
organizers: becoming a domain beginner, striving for data actionability, and
evaluating our design artifacts by the social relations they sustain rather
than just their technical efficacy.
Related papers
- "Near Data" and "Far Data" for Urban Sustainability: How Do Community Advocates Envision Data Intermediaries? [1.8900583145555927]
Data intermediaries are crucial stakeholders in facilitating data access and use.
Community advocates live in these sites of social injustices and opportunities for change.
This paper examines the unique perspectives that community advocates offer on data intermediaries.
arXiv Detail & Related papers (2025-01-13T19:47:44Z) - On Inferring User Socioeconomic Status with Mobility Records [61.0966646857356]
We propose a socioeconomic-aware deep model called DeepSEI.
The DeepSEI model incorporates two networks called deep network and recurrent network.
We conduct extensive experiments on real mobility records data, POI data and house prices data.
arXiv Detail & Related papers (2022-11-15T15:07:45Z) - Whose AI Dream? In search of the aspiration in data annotation [12.454034525520497]
This paper investigates the work practices concerning data annotation as performed in the industry, in India.
Previous investigations have largely focused on annotator subjectivity, bias and efficiency.
Our results show that the work of annotators is dictated by the interests, priorities and values of others above their station.
arXiv Detail & Related papers (2022-03-21T06:28:54Z) - Studying Up Machine Learning Data: Why Talk About Bias When We Mean
Power? [0.0]
We argue that reducing societal problems to "bias" misses the context-based nature of data.
We highlight the corporate forces and market imperatives involved in the labor of data workers that subsequently shape ML datasets.
arXiv Detail & Related papers (2021-09-16T17:38:26Z) - Competency Problems: On Finding and Removing Artifacts in Language Data [50.09608320112584]
We argue that for complex language understanding tasks, all simple feature correlations are spurious.
We theoretically analyze the difficulty of creating data for competency problems when human bias is taken into account.
arXiv Detail & Related papers (2021-04-17T21:34:10Z) - Explainable Patterns: Going from Findings to Insights to Support Data
Analytics Democratization [60.18814584837969]
We present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings.
ExPatt automatically generates plausible explanations for observed or selected findings using an external (textual) source of information.
arXiv Detail & Related papers (2021-01-19T16:13:44Z) - Hidden Footprints: Learning Contextual Walkability from 3D Human Trails [70.01257397390361]
Current datasets only tell you where people are, not where they could be.
We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints.
We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss.
arXiv Detail & Related papers (2020-08-19T23:19:08Z) - Between Subjectivity and Imposition: Power Dynamics in Data Annotation
for Computer Vision [1.933681537640272]
This paper investigates practices of image data annotation as performed in industrial contexts.
We define data annotation as a sense-making practice, where annotators assign meaning to data through the use of labels.
arXiv Detail & Related papers (2020-07-29T15:02:56Z) - Bringing the People Back In: Contesting Benchmark Machine Learning
Datasets [11.00769651520502]
We outline a research program - a genealogy of machine learning data - for investigating how and why these datasets have been created.
We describe the ways in which benchmark datasets in machine learning operate as infrastructure and pose four research questions for these datasets.
arXiv Detail & Related papers (2020-07-14T23:22:13Z) - REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets [64.76453161039973]
REVISE (REvealing VIsual biaSEs) is a tool that assists in the investigation of a visual dataset.
It surfacing potential biases along three dimensions: (1) object-based, (2) person-based, and (3) geography-based.
arXiv Detail & Related papers (2020-04-16T23:54:37Z) - A Philosophy of Data [91.3755431537592]
We work from the fundamental properties necessary for statistical computation to a definition of statistical data.
We argue that the need for useful data to be commensurable rules out an understanding of properties as fundamentally unique or equal.
With our increasing reliance on data and data technologies, these two characteristics of data affect our collective conception of reality.
arXiv Detail & Related papers (2020-04-15T14:47:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.