What’s algorithmic bias? It’s the idea that bias buried in computer code can perpetuate social inequalities. Unfortunately, social biases baked into data can often go unnoticed until the results are already in play, says writer Li Zhou.
A 2016 report from the Office of Science and Technology Policy warned that the impact of artificial-intelligence-driven algorithms on workers has the potential to disadvantage individuals in a host of fields.
As marketers, are social biases inherently hard-wired into our data? Are there steps data marketers can take to help weed out biases so as to not undermine data?
In general, bias is in fact embedded into both algorithms and data. With the appropriate controls, however, bias can actually be a good thing because it drives performance based on the context within which the algorithm is being built.
Let’s consider bias on the data level. What’s collected and whose data is being collected naturally gravitates to those most familiar or knowledgeable with computers and the internet. That tends to be a more affluent and age-specific population.
On the algorithmic level, biases of the developers of these models naturally include partialities within their own thinking in the models. This most often can be seen in models developed by parent companies in one country and then applied to other global markets without a strong understanding of cultural and/or social differences.
Partnering with in-market resources to improve performance and ensure a checks and balances principle around what’s being measured/monitored/applied can blunt bias to a certain degree.