Author: Paul van der Laken

Google’s Responsible AI Practices

Google’s Responsible AI Practices

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 here.

  1. Be socially beneficial.
  2. Avoid creating or reinforcing unfair bias.
  3. Be built and tested for safety.
  4. Be accountable to people.
  5. Incorporate privacy design principles.
  6. Uphold high standards of scientific excellence.
  7. 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 here.

  1. Use a Human-centered approach (see also here)
  2. Identify multiple metrics to assess training and monitoring
  3. When possible, directly examine your raw data
  4. Understand the limitations of your dataset and model
  5. Test, Test, Test,
  6. Continue to monitor and update the system after deployment
10 Tips for Effective Dashboard Design by Deloitte

10 Tips for Effective Dashboard Design by Deloitte

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.

Here’s my interpretation of the 10 rules:

  1. Know your message or goal
  2. Choose the chart that conveys your message best
  3. Use a grid to bring order to your dashboard
  4. Use color only to highlight and draw attention
  5. Remove unneccessary elements
  6. Avoid information overload
  7. Design for ease of use
  8. Text is as important as charts
  9. Design for multiple devices (desktop, tablet, mobile, …)
  10. 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
Screenshot from the Deloitte slidedeck
Google’s Guidebook for Developing AI Product Development

Google’s Guidebook for Developing AI Product Development

I came across another great set of curated resources by one of the teams at Google:

The 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 Guidebookโ€™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 PAIR project team (People & AI Research).

Here are the direct links to the six guidebook chapters:

Links to the related worksheets you can find here.

Repository of Production Machine Learning

Repository of Production Machine Learning

The Institute for Ethical Machine Learning compiled this amazing curated list of open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning.

๐Ÿ” Explaining predictions & models๐Ÿ” Privacy preserving ML๐Ÿ“œ Model & data versioning
๐Ÿ Model Training Orchestration๐Ÿ’ช Model Serving and Monitoring๐Ÿค– Neural Architecture Search
๐Ÿ““ Reproducible Notebooks๐Ÿ“Š Visualisation frameworks๐Ÿ”  Industry-strength NLP
๐Ÿงต Data pipelines & ETL๐Ÿท๏ธ Data Labelling๐Ÿ—ž๏ธ Data storage
๐Ÿ“ก Functions as a service๐Ÿ—บ๏ธ Computation distribution๐Ÿ“ฅ Model serialisation
๐Ÿงฎ Optimized calculation frameworks๐Ÿ’ธ Data Stream Processing๐Ÿ”ด Outlier and Anomaly Detection
๐ŸŒ€ Feature engineering๐ŸŽ Feature Storesโš” Adversarial Robustness
๐Ÿ’ฐ Commercial Platforms
Direct links to the sections of the Github repo

The Institute for Ethical Machine Learning is a think-tank that brings together with technology leaders, policymakers & academics to develop standards for ML.

Handling and Converting Data Types in Python Pandas

Handling and Converting Data Types in Python Pandas

Data types are one of those things that you donโ€™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 furtherย analysis.

Chris Moffit

In this short tutorial, Chris shows how to the pandas 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 lambda functions.

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