# Navigating the risks of artificial intelligence and machine learning in low-income countries

https://blog.cynexia.com/navigating-the-risks-of-artificial-intelligence-and-machine-learning-in-low-income-countries

Author: [Rob Aleck](https://blog.cynexia.com/pages/about) · https://github.com/mnbf9rca · https://www.linkedin.com/in/robertaleck/ · https://family-foqos.app/

Published: 2018-05-31
Categories: LinkedIn cross-post


How do you ensure your technically interesting project is truly a force for good, not merely further entrenching existing biases, stereotypes, and social problems? This great set of rules, based on experiences working on AI solutions in low-income countries, can help, regardless of where you're working:

1\. Ask who's not at the table - are you truly inclusive?

2\. Let others check your work - fairness is subjective

3\. Doubt your data - does your data suffer from collection bias?

4\. Respect context - a model developed in one context may fail in others

5\. Automate with care - take baby steps, don't take people out of the loop too soon  
[https://techcrunch.com/2018/05/24/navigating-the-risks-of-artificial-intelligence-and-machine-learning-in-low-income-countries/](<https://techcrunch.com/2018/05/24/navigating-the-risks-of-artificial-intelligence-and-machine-learning-in-low-income-countries/>)On a recent work trip, I found myself in a swanky-but-still-hip office of a private tech firm. I was drinking a freshly frothed cappuccino, eyeing a mini-fridge stocked with local beer and standing…