# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Ethical and Societal Implications
#### 3.5.1 Ethical Considerations in the Use of AutoML  
The use of AutoML raises several ethical considerations that are critical for businesses and developers to address as they integrate these technologies into their operations. One of the primary ethical concerns is the potential for bias in the models generated by AutoML [87]. Machine learning models, including those produced by AutoML, are only as good as the data on which they are trained. If the training data contains biases, whether related to race, gender, socioeconomic status, or other factors, those biases can be perpetuated or even amplified in the model's predictions [88]. This can lead to unfair or discriminatory outcomes, particularly in sensitive applications such as hiring, lending, or healthcare. The automated nature of AutoML means that these biases can be introduced without the user's awareness, making it crucial for businesses to implement rigorous testing and validation procedures to ensure fairness and equity in the models they deploy.  
Another ethical issue is the transparency and interpretability of AutoML models. Unlike traditional machine learning, where data scientists have direct control over model development and can often explain how a model arrives at its predictions, AutoML automates much of this process, creating \"black box\" models [89]. These models may be highly accurate, but their decision-making processes are opaque, making it difficult for users to understand how specific predictions are made. This lack of transparency can be problematic, particularly in industries like finance or healthcare, where stakeholders need to trust and understand the rationale behind automated decisions [90]. Therefore, ensuring that AutoML platforms include tools for model interpretability and explainability is essential to maintaining ethical standards.  
The rise of AutoML has led to increased privacy concerns, especially when handling sensitive or personal data. As AutoML systems often require large datasets to function effectively, there is a risk of violating individuals' privacy rights if data is not handled properly [91]. Businesses must comply with data protection regulations and implement robust data anonymization and security measures. In addition, the automated nature of AutoML could lead to the unintended use of data in ways not originally intended or consented to by data subjects, raising further ethical questions about informed consent and data usage.  
Finally, the deployment of AutoML can lead to ethical dilemmas related to accountability. When automated systems make decisions, it can be challenging to determine who is responsible when something goes wrong, whether it is the developers who created the AutoML platform, the businesses that deployed it, or the algorithms themselves. This ambiguity in accountability can complicate efforts to address issues like discrimination or errors in automated decision-making. Therefore, it is crucial for businesses and regulators to establish clear guidelines on accountability and ensure human oversight in deploying AutoML technologies.