Tobias Baer provides clear and concise examples of how Google uses the acquisition of select data to create bias, which leads to the dissemination of inaccurate information.
I’m an avid user of the navigation function of Google Maps. Every time I reach my destination, Google asks me for feedback on the navigation instructions. What could possibly be wrong with that? Well, I bet that the data and any analytics derived from that feedback often – and, vastly! – overestimates users’ satisfaction. Why is that?
The app is a perfect illustration of availability bias. I only am given this opportunity to provide feedback when I reach my destination. Which means that if I reach a river only to find that the ferry supposed to take me and my car to the other riverside has stopped operations an hour ago, or if after a few hours of cycling I find that the path indicated by the app leads straight into a gigantic military infrastructure that is fenced by barbed wires with large red signs threatening any trespasser to be shot (both has actually happened to me), and hence my only option is to abolish my route, exit the navigation, and go back to where I come from, no feedback is collected.
Points covered in this article include:
- The problem with creating algorithms quickly
- The lack of sufficient communication
- The challenge of creating objective, systematic assessment procedures
Read the full article, A Little Example How Google Creates Biases, 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