|||

TYE: We touched on data provenance earlier, but I want to come back to it from the perspective of quantitative data

TYE: We touched on data provenance earlier, but I want to come back to it from the perspective of quantitative data. In particular, I think it is critical to keep in mind that the systems that generate quantitative data are necessarily embedded in socio-technical systems. The technological elements of those systems (electronic sensors, software-based telemetry, etc.) are designed, manufactured, and maintained by sociocultural factors. So, a data scientist who is diligently trying to understand where their data comes from in order to interpret it, will sooner or later need to understand sociocultural phenomena that produced data, even if that understanding is more meta-data than data. It would make sense to co-develop rubrics for assessing the quality of data generated by socio-technical systems. Shining a bright light on the deepest lineage of data that impacts business or design decisions is important for everyone involved. Such assessments could lead to more cautious ways of using data, or be used in efforts to improve the explainability of technical systems. — https://www.epicpeople.org/data-science-and-ethnography/
Up Next Next → there’s a lot of potential in collaborating to illuminate the systems that create data https://www.epicpeople.org/data-science-and-ethnography/ ← Previous DAWN: I’m always curious about how data scientists measure the consistency or sensitivity of results from datasets https://www.epicpeople.org/data-science-and-ethnography/
Latest posts
▵  Twitter announces Bluesky: a team seeking and developing an open standard for social media
▵  Elon Musk attempts to explain Twitter to normal people in court
▵  TED and YouTube launch global climate initiative
▵  Embracing multilingualism to enhance complexity sensitive research
▵  The ‘Amazon effect’ is flooding a struggling recycling system with cardboard
▵  John Kerry, Arnold Schwarzenegger wage ‘World War Zero’ on climate change
▵  Combining semantic and term frequency similarities for text clustering
▵  Bad RCS implementations are creating big vulnerabilities, security researchers claim
▵  2019 Tech Trends Report — The Future Today Institute
▵  Medical Crowdsourcing: Harnessing the “Wisdom of the Crowd” to Solve Medical Mysteries
▵  Report Launch - OPSI Primer on AI for the Public Sector
▵  “Level Up”: Leveraging Skill and Engagement to Maximize Player Gameplay
▵  Beautiful is Good and Good is Reputable: Multiple-Attribute Charity Website Evaluation and Initial Perceptions of Reputation Under the Halo Effect
▵  Piret Tõnurist & Systems Change: how to get started and keep going?
▵  IBM expert Tamreem El Tohamy on bridging the skills gap in Africa
▵  The changing work of innovation for public value and social impact
▵  Former Go champion beaten by DeepMind retires after declaring AI invincible
▵  What part of “viral” content makes platforms want to encourage its spread?
▵  MTA floods NYC subway entrance because ‘climate change is real’
▵  The Demon Haunted World
▵  How to recognize AI snake oil
▵  A Systemic View of Research Impact
▵  Nobel Economics Prize Goes to Pioneers in Reducing Poverty
A brief, informal guide to doing grounded theory
▵  Adam Savage on Lists, More Lists, and the Power of Checkboxes
▵  Systems Practice, Abridged
▵  Fukushima reinvents itself with a $2.7 billion bet on renewables
▵  How Tesla’s first Gigafactory is changing Reno, Nevada
▵  “This is Sticking with Them:” Professor Explores Benefits of Model-Based Learning
Keeping the buzz in buzzwords
▵  README.txt: Introducing Into the Dataverse, the article series