Artificial intelligence (AI) has rapidly evolved to become a driving force behind innovation, automation and efficiency across various industries. However, the success of AI systems is heavily dependent on the quality of the data they are trained on. In the realm of AI, the adage “garbage in, garbage out” holds true – the effectiveness of AI functions is directly proportional to the quality of the data it receives. Information Week brought this topic to us in their article, “Feasting on High-Quality AI Data.”

Quality data is essential for minimizing biases and preventing unintended consequences, fostering AI systems that are ethical and unbiased. Ensuring data quality throughout the entire lifecycle of an AI system allows it to adapt to changing environments and evolving challenges.

The success of AI functions hinges on the quality of the data they are exposed to during training and operation. Investing in data quality not only enhances the performance of AI applications but also instills confidence in their reliability and effectiveness. As AI continues to play a pivotal role in shaping the future of technology, the need for high-quality data remains paramount for realizing its full potential and delivering positive impacts across diverse sectors.

The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, changing search to found.