Part I
Transparency

In This Part

Welcome to Part I, “Transparency.” Our society today is obsessed with using technology. The power that technology brings to people's ability in completing perceived mundane tasks is astonishing and lightning fast. The “hurry up and complete more tasks in a fixed time frame” mindset overlooks the cascading and compounding influences of those activities on the human condition. But this isn't the first time the outcomes overshadowed the people. There's an uncomfortable U.S. history of sidelining the humanity of certain people so that others can thrive. The journey starts at this uncomfortable place when technology wasn't prevalent. How we as a society are living through this technology era isn't new, but simply a reincarnation of how the world has operated in the past. Being transparent about the pre-tech era illuminates how tech systems came to be designed with so many flaws. The data, code, algorithms, systems, and platforms that make the tech industry thrive are a wicked web of assumptions, presumptions, and opaqueness.

  • Transparency (n) – Revealing your data and code. Bad for proprietary and sensitive information. Thus really hard; quite frankly, even impossible. Not to be confused with clear communication about how your system actually works.

    —Karen Hao, in “Big Tech’s Guide to Talking About AI Ethics,”
    www.technologyreview.com/2021/04/13/1022568/big-tech-ai-ethics-guide1

    Transparency (n) – Revealing outcomes and impact of the data, code, algorithms, and systems by companies, organizations, and groups.

    Revised Definition by Brandeis Hill Marshall

Data is the fuel that runs our algorithms and systems. Chapters 14 have us get inside data as a construct, structure, and limiting factor when digitized. We struggle to wrap our minds around data within and outside of digital infrastructures. Ultimately, it's up to the collective we to be clear-eyed in our expectations of data and its uses.

A tweet from Dr. Brandeis Marshall.

Source: https://twitter.com/csdoctorsister/status/1315376187632476161

Note

  1. 1.  Hao, Karen. “Big Tech’s Guide to Talking About AI Ethics.” MIT Technology Review, 2021. www.technologyreview.com/2021/04/13/1022568/big-tech-ai-ethics-guide.
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