With the rapid development of artificial intelligence (AI), the transparency and understandability of algorithms used in various areas of life, from health decision-making to law enforcement monitoring systems, is becoming an increasingly important topic.
Artificial intelligence (AI) algorithms are a set of instructions and rules used by a computer program to solve specific artificial intelligence tasks. These algorithms allow computers to analyze data, perform tasks, make decisions, and even learn from experience.
Artificial intelligence algorithms can be diverse, depending on the specific task. Here are some types of AI algorithms and their examples:
1. Methods of machine learning:
- Tutored learning: The model is trained on examples to predict the output. Examples include classification and regression algorithms.
- Unsupervised learning: The model learns the data structure without labels or categories. Examples include clustering and data dimensionality reduction.
- Subtraining (partial teacher training): The model is trained on a partial set of labels and unlabeled data.
- Reinforcement learning: The model learns by interacting with the environment and determining optimal actions. An example is the Q-learning and reinforcement learning algorithms.
2. Evolutionary algorithms: Algorithms simulating natural selection processes for optimization. An example is genetic algorithms.
3. Neural networks: Models that mimic the structure and functions of the brain for solving machine learning tasks. Examples are regular artificial neural networks, convolutional neural networks, and recurrent neural networks.
4. Logic programming: Uses rules and logical operations to solve problems. An example is classification algorithms using decision trees.
Building artificial intelligence algorithms can include various steps, such as data collection and preparation, model selection, parameter tuning, model training, and performance evaluation. In addition, this process may require an understanding of mathematical and statistical concepts, as well as programming.
Transparency and clarity of algorithms
Transparency and comprehensibility of algorithms are important elements to ensure compliance of algorithms with ethical, social and legal standards. However, what regulatory requirements apply to these aspects?
Firstly, we need to clearly define the terms "transparency" and "understandability" in the context of AI. Transparency means the availability of information about how algorithms work, including their structure, parameters, and data sources. Comprehensibility refers to how easily users can understand and interpret algorithm actions and decisions.
Regulatory requirements may include requirements for public availability of data related to the development and use of AI algorithms. This may include publishing documentation, open access to data for testing and auditing, and reporting on the use of algorithms in specific situations.
Regulatory requirements may also require audits and validation of algorithms to verify their transparency and comprehensibility. This may include independently reviewing algorithms for compliance with ethical and legal standards, as well as checking for internal and external validation.
Regulations may set requirements for documentation accompanying the development and use of AI algorithms. This may include documentation requirements for algorithm architecture, data processing methods, model parameters, and other key aspects.
Education programs and training courses can be part of regulatory requirements aimed at ensuring that professionals working with AI algorithms have sufficient knowledge and understanding of the ethical and social aspects of using such technologies.
Transparency and comprehensibility of AI algorithms are becoming increasingly important aspects in today's digital world. Regulatory requirements regarding these aspects should contribute to ensuring compliance of algorithms with ethical, social and legal standards, as well as maintaining user trust in new technologies.
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Serhii Floreskul
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Violetta Loseva
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