The following are some of the drawbacks that make predictive analytics more of a human-like approach, despite its might. One major constraint is data dependency: it relies on historical data which may not be very reliable in the current and future business environment since the business environment is continually evolving. Similar to humans, predictive models may also be prejudiced and this prejudice is manifested in the data used to train the model hence; unfair outcomes may be observed. These models also have issues with context and other related factors, and sometimes they cannot understand something that a human being might immediately get. Also, predictive analytics can suffer from overfitting, where models are too optimized to specific scenarios and may not work under any other circumstances. Finally, deep learning and many other AI models are inherently difficult to interpret, which hinders their ability to provide clear reasoning behind certain actions or decisions, just as human judgment can be hard to understand. These limitations therefore justify the need for human intervention and analysis in spite of the predictive algorithms.
Comments
Post a Comment