Category: 2. Disadvantages

Automation through machine learning can lead to job losses in certain sectors. Roles involving repetitive tasks, such as data entry or assembly line work, are particularly vulnerable. While ML creates new opportunities, reskilling the workforce remains a significant challenge.

If the training data contains biases, the ML model may perpetuate or amplify these biases, leading to unfair or unethical outcomes, especially in sensitive applications like hiring or lending.

Many machine learning models especially deep learning systems, operate as “black boxes.” Their decision-making processes are difficult to interpret or explain, raising ethical concerns in critical fields like healthcare or finance.

Developing and deploying machine learning models requires significant investment in infrastructure, computational resources, and skilled professionals. Small businesses may find these costs prohibitive.

The performance of ML models heavily depends on the quality and quantity of training data. Poor-quality or biased data can lead to inaccurate predictions or unfair outcomes. For example, biased datasets in hiring algorithms can perpetuate discrimination.