Examples of Data Bias in A.I.
The impact of biased data on applications such as A.I. is not always theoretical, or even subtle. A famous example is Microsoft’s Tay. Tay was a chatbot released by Microsoft in 2016 that used artificial intelligence technology to create and post Tweets. Soon after going live, Tay began posting Tweets with concerning content, much of it discriminatory in nature.
After deactivating Tay, the Microsoft team released a statement about the incident. This statement pointed to Twitter users intentionally spamming Tay’s conversational threads with inflammatory statements as the source of her behavior. Tay used those threads as a means of data mining to influence its output. Although this incident was at least partially perpetuated by intentional sabotage from users, it is an illustration of how discriminatory thoughts can take form in the data that is increasingly being utilized in our day-to-day lives.
The Impact of Biased Data
Because data-driven technology is now so omnipresent, biased data can have a wide range of consequences. Some of these consequences are the more obvious problems inherent to the marketing of a faulty product. However, there may also be more complex social repercussions.
As stated previously, machine learning can be even more susceptible to bias than humans are. The advantage that humankind has over machine learning is that humans, at least in groups, have the capacity for cultural evolution, and cultural evolution provides some level of check-and-balance against prejudice. However, if we are constantly feeding prejudice back into our cultural consciousness through the vehicle of data-driven technology, prejudices may be subconsciously reinforced, and the natural social stabilization process stunted.