As a company that uses a lot of automation, optimization, and machine learning in their day-to-day business, Google is set on developing AI in a socially responsible way.
Fortunately for us, Google decided to share their principles and best practices for us to read.
Google’s Objectives for AI applications
The details behind the seven objectives below you can find
Be socially beneficial. Avoid creating or reinforcing unfair bias. Be built and tested for safety. Be accountable to people. Incorporate privacy design principles. Uphold high standards of scientific excellence. Be made available for uses that accord with these principles.
Moreover, there are several AI technologies that Google will not build:
Google’s best practices for Responsible AI
For the details behind these six best practices, read more
Use a Human-centered approach (see also here) Identify multiple metrics to assess training and monitoring When possible, directly examine your raw data Understand the limitations of your dataset and model Test, Test, Test, Continue to monitor and update the system after deployment
Oscar Baruffa collected over 100 books on the R programming language, in his . Big Book of R
Many of these books you will also find in
. my R resources list
Yet, Oscar sectioned them in neat categories, such as:
Oscar could still use some help maintaining this resource, so have a look at
to contribute. its github
My colleague prof. Jack van Wijk pointed me towards these great guidelines by Deloitte on how to design an effective dashboard.
Some of these rules are
more generally applicable to data visualization. Yet, the Deloitte 10 commandments form a good checklist when designing a dashboard.
interpretation of the 10 rules:
Know your message or goal Choose the chart that conveys your message best Use a grid to bring order to your dashboard Use color only to highlight and draw attention Remove unneccessary elements Avoid information overload Design for ease of use Text is as important as charts Design for multiple devices (desktop, tablet, mobile, …) Recycle good designs (by others)
In terms of recycling the good work by others operating in the data visualization field, check out:
I just love how Deloitte uses example visualizations to help convey what makes a good (dashboard) chart:
Screenshot from the Deloitte slidedeck
Screenshots from the Deloitte slidedeck
Screenshot from the Deloitte slidedeck
I came across another great set of curated resources by one of the teams at Google:
. People + AI Guidebook
The People + AI Guidebook was written to help user experience (UX) professionals and product managers follow a
human-centered approach to AI.
The Guidebook’s recommendations are based on data and insights from over a hundred individuals across Google product teams, industry experts, and academic research.
These six chapters follow the
product development flow, and each one has a related worksheet to help turn guidance into action.
The People & AI guidebook is one of the products of the major
(People & AI Research). PAIR project team
Here are the direct links to the six guidebook chapters:
Links to the related worksheets you can find
The Institute for Ethical Machine Learning compiled this
amazing that will help you curated list of open source libraries deploy, monitor, version, scale, and secure your production machine learning.
Direct links to the sections of the Github repo
is a think-tank that brings together with technology leaders, policymakers & academics to develop standards for ML. Institute for Ethical Machine Learning
Data types are one of those things that you don’t tend to care about until you get an error or some unexpected results. It is also one of the first things you should check once you load a new data into pandas for further analysis.
this short tutorial, Chris shows how to the
dtypes map to the
numpy and base Python data types.
A screenshot of the data type mapping.
Moreover, Chris demonstrates how to handle and convert data types so you can speed up your data analysis. Both using custom functions and anonymous
A snapshot from the original blog.
A very handy guide indeed, after which you will be able to read in your datasets into Python in the right format from the get-go!
Using data type casting, lambda functions, and functional programming to read in data in Python. Via pbpython.com/pandas_dtypes.html