One major limitation of AI is its dependence on data quality. AI systems heavily rely on the data they are trained on, and if the data is incomplete, biased, or of poor quality, the AI's performance will be adversely affected. Without high-quality data, AI cannot produce accurate or reliable results.
Another limitation is algorithmic bias, which refers to the biased decisions or outputs made by AI systems. Since AI is trained on historical data, it can unintentionally perpetuate and amplify existing biases present in the data. This can lead to discriminatory outcomes and reinforce societal inequalities if not carefully monitored and addressed.
Lastly, the "black box" nature of AI poses a challenge. AI algorithms often lack transparency as they make complex decisions that even their creators may not fully understand. This lack of interpretability raises concerns regarding accountability, explainability, and trust in AI systems. It becomes difficult to identify and rectify potential errors or biases when the inner workings of AI are not easily decipherable.