My sousveillence data criticizes the superficial process of identifying your disability status to an employer. When your mouse is on the left side of the canvas, the form is white with the usual text. When the mouse is moved to the right side, the form's text changes to a more cynical and revealing tone. If you click your mouse, real data populates the form from actual respondents to similar forms.
I believe the collection of this data is very invasive, especially if its from mandatory forms like the Voluntary Self-Identification of Disability form. Furthermore, the process feels superficial. Even if you disclose your disability status, many employers and the government still fail to effectively address systemic and ableist barriers.
The data is sourced from the CDC API, specifically the Disability and Health Data System (DHDS). The used data set is titled Prevalence of Disability Status and Types by Demographic Groups, 2019, last updated May 26, 2021. It has 7,056 entries with the disability types: cognitive (serious difficulty concentrating, remembering or making decisions), hearing (serious difficulty hearing or deaf), mobility (serious difficulty walking or climbing stairs), vision (serious difficulty seeing), self-care (difficulty dressing or bathing) and independent living (difficulty doing errands alone). It also collects "identifier" data like age, race, location, and veteran status.
My initial ideas are sketched in the following storyboards:
I decided on the first idea since I personally have issues with how corporate America collects disability data and then does nothing to support disabled workers with paid leave, useful disability benefits, etc. Also, by sectioning out the data by "profiles", it individualizes each entry in a way we don't often see in data gathering.
The data is sourced from the CDC
The assignment can be found here from the Critical Computation course website.