Artificial Intelligence (AI) has become a popular buzzword in recent years, with almost every product claiming to be “AI-powered”. However, a closer look reveals that true AI is rarely present in most cases. Unfortunately, the lines between AI, Machine Learning (ML), and Data Analytics have become blurred in conversations, leading to inflated expectations and misunderstandings.
However, what is even more concerning is the absence of the “D” in the conversation: Data. Few seem to care about data, even though it is the most challenging component when considering AI. While developing systems and algorithms is difficult, the tools for most common problems are readily available. The biggest challenge is often finding relevant, unbiased, and sufficient high-quality data with the necessary rights to use it. Without sufficient data, we cannot train systems adequately, and with poor or biased data, we risk getting misleading or inaccurate results.
In fact, biased data and a blind belief in AI can have severe consequences. One example is the use of algorithms in the criminal justice system that are trained on biased data, leading to unfair treatment of certain groups. Therefore, we have a responsibility as engineers, managers, leaders, and citizens to ensure that these technologies are applied responsibly, and we must continue asking the tough questions.
It is essential to be aware of the challenges and ask questions when implementing these technologies. While analytics, AI, and machine learning can be valuable and powerful tools, we must use them responsibly and ensure that we are working with high-quality, unbiased data. Only then can we truly unlock the potential of AI and its applications.