Ethics in data collection
Ethical matters regarding the collection of data are increasingly being raised by the public, especially as it concerns consumer privacy. While consumer data is used by CRM systems and similar technology to improve customer experience, companies can also use, buy, or sell such data in ways that bump up against the edge of what’s legal or ethical, eroding consumer trust across the board.
In fact, there is such widespread concern that many laws and regulations have been enacted on the subject across the globe, such as the European Union’s General Data Protection Regulation (GDPR). Those who want to work ethically with mined consumer data may find it helpful to seek out businesses that are compliant with GDPR and/or similar codes.
Data bias in AI
The impact of biased data on applications such as artificial intelligence 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 AI technology to create and post to Twitter. Soon after going live, Tay began tweeting 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 its behavior. Tay used those threads as a means of data mining to influence its output. Although this incident was at least partially caused by intentional sabotage from users, it illustrates how discrimination can take form in the data that is increasingly being put to work 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, including complex social repercussions. If we are constantly feeding prejudices back into our cultural consciousness through the vehicle of data-driven technology, these prejudices may be subconsciously reinforced, creating a loop we can only break with concerted effort. The advantage that humans have over machine learning is that humans, at least in groups, have the capacity for cultural evolution, providing some level of checks and balances against prejudice.