Do we measure right inequalities

Can we consider any society developed if the people have a deep sense of unfairness and believe that the ‘system is rigged’? Recent chapter by Avidit Acharya and John E. Roemer in “The Great Upheaval” argues that fairness entails equalizing opportunities rather than equalizing something else.

However, do we measure what matters?

In the end, all inequalities are unequal, but some are more unequal than others. We still use only one indicator—the Gini coefficient of income inequality—to judge them all. This chart illustrates possible approach in measuring equality of opportunity. It shows distributions of income among people, grouped by the levels of education of their two parents. These curves summarize the income opportunities available to its members. Inequality of opportunity for income appears to be a good deal higher in Indonesia than in Germany around a similar time. Measuring right inequalities could help policymakers to shape right policies.

7 Data Storytelling Tips From Centuries-Old Data Visualization

Data visualization has a long history, hundreds of years. Data designers coaxed numbers into telling stories and giving us insight into the world around us. Here are seven lessons learned:

1. Keep the Focus on the Data

2. Label for Comprehension

3. Choose the Most Effective Data Visualization

4. Tell a Comprehensive Story

5. Order data for comprehension

6. Compare for Context

7. Make it Beautiful

New-old human-machine interactions

Nowadays we store more than 99% of information in digital form, comparing to just 1% a couple of decades ago. Just 60% of Internet traffic is currently generated by human, the rest is coming from bots, good 15% and bad 25%. This is a new reality, which pose a lot of questions–how we, human, interact with algorithms? with each other using the technologies? what does this mean for human development and human security? This video illustrates new-old interactions–a grandma helping a small robot to cross a street, by holding cars.