When it comes to developing AI algorithms, data quality is of utmost importance. The accuracy and effectiveness of AI models depend on the quality of data used to train them. However, the challenge is that data quality issues can arise at the beginning of the process, especially when dealing with unfiltered social media data.
For example, Microsoft’s AI bot detected abusive and racist statements and misogynistic remarks when given access to unfiltered social media data. Similarly, incomplete data is a major cause of AI’s failure to identify people with dark skin.
So, what is the connection between data quality and AI?
Data governance, quality knowledge, and isolated data views are the answer. These factors can cause poor results and limit the effectiveness of AI models.
When businesses realize they have data quality issues, they may recruit consultants, engineers, and analysts to clean up data and address the problem. However, these efforts often fail to deliver any real change. The truth is that solving a data quality issue requires a ground-up approach.
The first step is to create awareness about data quality issues and acknowledge their importance. This can be achieved by developing a culture of data literacy within the organization. Business users should be educated on issues like inconsistent and duplicate data and dirty data problems.
To achieve this, businesses can employ design thinking to foster a culture where everyone can understand an organization’s data objectives and issues and contribute to them. Making data quality training an organizational endeavour and equipping teams to identify bad data qualities are crucial steps.
In conclusion, data quality is the foundation for effective AI. By addressing data quality issues, businesses can improve the accuracy and effectiveness of AI models and make more informed decisions based on the insights gained from these models.