Ease of Doing Business
Cheenu Seshadri shares an article that explains the ins and outs of doing business in India, including how the EODB (ease of doing business) metrics on India measure up.
When a client recently asked us if the investment climate had improved in India, we became curious ourselves and decided to dive in. Having lived through a tortuous investment climate for international investors in the telecom sector between 2009 and 2013, I knew first-hand that there were deep structural issues that could not be fixed within one-term of a business-friendly administration.
As we dug in, the first thing we came across was effusive praise in both the domestic and international media for the remarkable progress India had made in the Ease of Doing Business (EODB) ranking released by The World Bank Group annually. India had historically been in the bottom third of countries with an average ranking of 131 between 2007 and 2017. Since Prime Minister Narendra Modi made EODB improvement a key platform to communicate to the world that India was open for business, several reforms have been undertaken. The series of reforms have landed India on the top-10 improved list for 3 years in a row and it has risen from a lowly 130th in the 2017 report to 63rd in the 2020 report published in Oct 2019.
Areas covered include:
- Deficiencies in the EODB metric
- Where India stands relative to peers
- Has the EODB improvement had an impact
Read the full article, India’s EODB: Is it “Easy” to do Business in India?, on LinkedIn.
Data scientist and psychologist Tobias Baer (who recently published a book on algorithmic bias) is giving a talk on how to prevent algorithmic bias in the U.K. on Tuesday, 11 February 2020.
Algorithmic bias can affect us everywhere, from minor trivia such as our social media feeds to critical decisions where bias can wreak havoc with a person’s life dream or a company’s survival. Sources of algrorithmic bias are manifold – some, such as biased data and overfitting, sit squarely in the domain of data scientists themselves, while others only can be tackled by the business users and government agencies who use algorithms, be it through carefully crafted experiments that generate truly unbiased data or through deliberate tweaks of the decision-making process.
The discussion will include:
- The psychological and statistical sources of bias
- What business users and data scientists can do respectively to manage and prevent algorithmic bias.
- How regulators should think about algorithmic bias
To learn more about the event, visit: https://www.eventbrite.co.uk/e/how-to-prevent-algorithmic-bias-tickets-86670021367