Ethics and data science book

9.11  ·  2,120 ratings  ·  239 reviews

Data science, ethics, and the 'massive scumbags' problem | ZDNet

This post was published originally on LinkedIn on May 30, Though based in math and statistics, data-driven models are anything but impervious to the bias, prejudice and downright malevolent nature of humans. Because behind every data point, every binary expression, every formula, function and statistical model are actual human beings. Sometimes we forget this at our peril. She debunks the implied equity of standardize testing which is neither standard or up to a fairness test as a tool for evaluating teachers.
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The Ethics of Data - Personal Data & Privacy

Data is infinite. Digital is inevitable. Sheila Colclasure This week we are talking about the efforts underway around the world to promote ethical, accountable data use, the promise and terror of AI, the need for a universal translator, and much more.

Ethics and Data Science

Eventbrite, and certain approved third parties, use functional, analytical and tracking cookies or similar technologies to understand your event preferences and provide you with a customised experience. By closing this banner or by continuing to use Eventbrite, you agree. For more information please review our cookie policy. Wed, 18 September , — BST. Think that discussing the ethics and impacts of data science is important, but also, kind of fun? Our next data science ethics bookclub is on AI and gender and we'd love you to join the discussion.

The book is written in the style and spirit that has inspired many sci fi authors. Sounds about right! Any guesses who wrote this raving review? A machine learning algorithm. The text has some obvious errors but it is a window into the future. If AI can generate human-like output can it also make human-like decisions?

What is machine learning? Everything you need to know. Read More. I was involved with a startup, and we were meeting to discuss what the business plan needed to look like. The basic requirement, we were told, was a growth chart that went up and to the right at an acceptable rate. Investors needed to see that they'd get the return they'd expect. It didn't matter what the startup actually did.

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As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes:. Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes.


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