Data is indispensable for an e-commerce business, especially if that business wants to take advantage of a machine learning-based customer engagement platform to optimize the customer experience. Analytics Insight brought this topic to us in their article, “How Does Poor Data Impact AI Systems?“
In today’s e-commerce landscape, the customer experience is everything. In order to convert browsers into buyers, vendors want to identify their intent. Having a wealth of data is what yields such insights and this is key for e-commerce businesses wishing to compete and grow.
When the algorithm feeds on training data to discover patterns, data quality challenges develop at the beginning of the process. For instance, when given access to unfiltered social media data, an artificial intelligence (AI) system may detect abuse, racist statements, and misogynistic remarks, as demonstrated by Microsoft’s AI bot. Recently, it was also thought that incomplete data was to blame for AI’s failure to identify people with dark skin.
Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
