# How Automated Machine Learning Can Improve Business
## 3 Results and Discussion
### Ethical and Societal Implications
#### 3.5.2 The Broader Societal Impact of AutoML on Business and Employment  
The broader societal impact of AutoML extends beyond the ethical considerations of its use, influencing the business industry and employment in significant ways. One major impact is the democratization of machine learning [92]. Small and medium-sized enterprises (SMEs), which may lack the resources to hire specialized data scientists, can now use AutoML to gain insights from their data and compete more effectively with larger corporations [85]. This democratization fosters innovation and competition since a widerrange of businesses can access the tools needed to optimize operations, improve customer experiences, and develop new products.  
However, the widespread adoption of AutoML also raises concerns about the future of employment, particularly for professionals in data science and related fields. As AutoML platforms become more sophisticated, they can automate many tasks traditionally performed by data scientists, such as model selection, feature engineering, and hyperparameter tuning [93; 94]. While this automation can increase efficiency and cost savings for businesses, it also risks reducing the demand for data science professionals. In the long term, this could lead to job displacement and require a shift in the skillsets needed in the workforce [95]. This impact of AutoML on employment extends to other industries where machine learning is used to automate tasks that humans previously performed. For example, in manufacturing, AutoML can optimize production processes and predictive maintenance, potentially reducing the need for manual labor. As a result, there has been increased fear related to increased unemployment levels and related negative implications.  
In sum, the results show that AutoML automates the machine learning workflow, making it accessible to non-experts through no-code or low-code features. It allows businesses to leverage data insights without focusing on technical model development. Key stages include auto pre-processing, feature engineering, model selection, hyperparameter tuning, and model training. Since [PERSON]'s algorithm selection in 1976, AutoML has evolved significantly. Platforms like Google's AutoML and Microsoft's Azure Machine Learning introduced neural architecture search (NAS), and open-source frameworks have improved usability. Today, AutoML handles complex data, focuses on interpretability, and benefits from ongoing research in meta-learning, transfer learning, and explainable AI. AutoML enhances efficiency, decision-making, cost management, and sustainability by automating tasks like data preprocessing and model selection. It scales operations, improves decision-making, and is cost-effective. AutoML provides real-time insights, supports sustainability, enhances fraud detection, marketing, and performance measurement, and enables predictive maintenance. However, AutoML presents ethical challenges, such as biases from training data, transparency issues, privacy concerns, and accountability for decisions. Businesses must rigorously test models, ensure interpretability, comply with data protection regulations, and establish clear accountability guidelines to address these challenges.