As data plays an increasingly pivotal role in customer experience (CX), the quality of your data has never been more important. AI is only as good as the data informing it, and AI investments won’t drive ROI if your data is missing the mark.
It’s important to collect as much data as you can in the contact center (ideally, from every interaction) but, beyond that, it’s crucial to ensure your data is unbiased. Data bias can creep in in various ways, compromising the quality of your data and undermining your AI-related efforts.
Know what bias is and what causes it
Bias occurs when incomplete or skewed data is used to train AI models or applications – when data that’s collected, used, or interpreted is not representative of the full population or subject it’s supposed to be covering.
Bias can enter the equation at various points of the data process: collection, processing, and analysis. When biased data is used to train AI, it skews the way AI “thinks” and the results it produces.
Data bias can be caused by lack of data, poor samples or training data, and human bias regarding how data is collected.
Labeling bias can occur, for instance, when human annotators label data with personal biases or cultural misperceptions. Annotators may subconsciously favor certain data (that’s why high-quality data annotators are essential). And sampling bias can arise when a dataset doesn’t represent the full population or subject it’s meant to model. A facial recognition model trained mainly on light-skinned individuals, for example, may perform poorly on other skin tones.
Biased data produces poor outcomes
When it comes to data, the stakes are high – the effectiveness of your AI hangs in the balance. Data bias can lead to various negative results, such as poor predictions related to sales approval decisions, poor identification of objects with computer vision models, and incorrect answers delivered by large language models (LLMs).
There are certain CX processes that are most susceptible to data bias:
- Call routing algorithms
- Conversation AI systems
- Customer feedback analysis
- Performance evaluation analytics
- Quality assurance processes
These processes often are key components of the contact center. They also rely heavily on the collection of data, so are ripe for potential bias if brands aren’t vigilant. When they’re compromised by bias they won’t deliver the results you need.
Mitigate bias with proactive solutions
You don’t have to be a victim of data bias; there are a growing number of ways you can nip it in bud before it derails your AI efforts.
Brands can mitigate data bias in the contact center by:
- Diversifying data sources (the wider and more diverse, the better)
- Implementing regular data audits and routine re-indexing of models
- Training staff on bias awareness
- Using bias-detection tools to test whether datasets are skewed
- Encouraging a culture of questioning assumptions and verifying the accuracy of your outputs
Managing your AI models should be an active process. A set-it-and-forget-approach won’t work. You need to regularly assess what’s working, what’s not, and where roadblocks are occurring – and devote resources to making sure that happens.
To reduce the likelihood of bias, CX teams should lean into their technical teams or partners. Ask technical teams to explain data weightings and provide visualizations of training data. This ensures there’s a clear understanding of where data comes from and how it’s being collected, labeled, and analyzed. It makes the whole process more transparent.
Seek expert help when you need it
Staying mindful about data bias – where it may happen and how to stop it – may seem daunting, especially with so many competing priorities in the contact center. But it’s an important topic to keep top of mind.
Addressing data bias isn’t just about meeting compliance standards; it’s about building trust, improving accuracy, and ultimately delivering better service to your customers.
If you’re not sure where to start or you lack the expertise in-house, working a CX partner that specializes in AI and data analytics can be a great way to tap into cutting-edge technology, expert support, and proven best practices.
