Example Concept: Current AI systems often struggle with bias because they learn from historical data, which may contain prejudices. They also lack true understanding and reasoning, as they rely on pattern recognition rather than comprehension. Furthermore, AI models require vast amounts of data for training, which can be difficult to obtain and process, limiting their application in data-scarce environments.