| My Initial Roadmap: See Notes for interactive version |
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Market forecasts indicate that the total amount of data created, captured, shared, and consumed globally was over 64 zettabytes in 2020, and projected to surpass 180 zettabytes by 2025. Data scientists will now have unprecedented levels of access to user and activity data on a global scale ...
With great power comes great responsibility. Every advance in science and technology brings with it a measure of the unknown, and an inherent tradeoff between the benefits ("how can it help society?") and the drawbacks ("how can it hurt society?"). With big data so prevalent in our everyday lives and decision-making processes, it's critical that we evaluate tradeoffs responsibly.
Ethics have always been one approach to ensuring that these tradeoffs are made with a set of moral principles in mind - acceptable societal norms on right-vs-wrong that we follow voluntarily to ensure collective good...
But what's the equivalent approach for data science at individual, organizational, and global industry scale? This project explores the topic of data ethics, with references (and notes) from secondary research to support self-guided exploration. The content is organized into the high-level topics indicated in the roadmap below.
The notes here were collected as part of my secondary research in the context of two projects I undertook in 2021:
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Completing the Data Ethics Course from the University of Michigan. Highly recommend it!
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Contributing to the Data Ethics lesson in the open-sourced Data Science for Beginners curriculum from Microsoft.
I share them in the hope that my efforts find value to other students exploring this fascinating space. They are not meant for commercial or for-profit use.
If your content or resource is cited, and you would prefer it not be - or if you need to correct my interpretation of what I took away -- please do DM me @nitya and I will be happy to make corrections.
There's never been a more important time to educate ourselves on data ethics, and apply these meaningfully in work and daily life. Happy learning.