Classification
AI Systems and Architectures
Overview
A Feedforward Neural Network (FNN) is a fundamental artificial neural network architecture where information moves in only one direction-from input nodes, through hidden layers (if any), to output nodes. There are no cycles or loops within the network, distinguishing FNNs from recurrent neural networks (RNNs). FNNs are commonly used for tasks such as classification, regression, and basic pattern recognition. Their simplicity makes them computationally efficient and relatively easy to interpret compared to more complex architectures. However, FNNs are limited in their ability to model sequential or temporal dependencies in data, making them less suitable for applications like language modeling or time-series analysis. Additionally, FNNs can struggle with very high-dimensional data or tasks requiring deep feature hierarchies, where deeper or more complex networks may be necessary.
Governance Context
From a governance perspective, the use of FNNs requires compliance with general AI risk management frameworks such as NIST AI RMF, which mandates transparency and documentation of model architectures. Under the EU AI Act, organizations deploying FNNs in high-risk contexts (e.g., biometric identification) must implement risk assessment procedures and maintain detailed records of model training and evaluation. Concrete obligations include: (1) conducting impact assessments to evaluate potential risks and harms, and (2) maintaining comprehensive documentation of model design, data sources, and performance metrics for auditability. Controls from ISO/IEC 23894:2023 also require organizations to monitor model performance and retrain models as necessary to mitigate bias or drift, even for comparatively simple architectures like FNNs. Ensuring data quality, fairness, and ongoing monitoring are essential governance practices.
Ethical & Societal Implications
While FNNs are relatively transparent compared to more complex models, their use in sensitive contexts can still introduce ethical risks, such as amplifying biases present in training data or making opaque decisions in critical applications. Inadequate oversight may lead to unfair or discriminatory outcomes, especially if input features correlate with protected characteristics. Additionally, the simplicity of FNNs can result in underfitting, leading to poor performance and potential harm if deployed without proper validation. Ensuring explainability, fairness, and regular auditing is crucial to mitigate these risks. Societal implications also include the need for accessible documentation and the risk of over-reliance on simple models in complex decision environments.
Key Takeaways
FNNs are foundational, unidirectional neural networks suitable for straightforward tasks.; They lack mechanisms to handle sequential or temporal dependencies in data.; Governance obligations include transparency, documentation, and risk assessment, even for simple models.; Failure to monitor or retrain FNNs can result in bias, drift, or poor performance.; Ethical deployment requires attention to fairness, explainability, and context-specific risks.; FNNs are more interpretable than deep or recurrent architectures but still require oversight.; Appropriate use of FNNs depends on the complexity and nature of the data/task.