On June 1, 2023, the The Deputy Secretary of the Information Technology Department of the Head of the Cabinet of the Nation of Argentina has issued a statement. arranged To establish recommendations and ethical principles for the development and use of artificial intelligence (IA).
A Significant growth in the development and implementation of artificial intelligence based projects. In this context, UNESCO published a Recommendation on the Ethics of Artificial Intelligence. Argentina joined Along with other countries.
Argentina’s Undersecretary of Information Technology participated in the first Global Forum on the Ethics of Artificial Intelligence. Ensuring the inclusion of AI in the world and promoting ethical, people-oriented, gender sensitive and human rights regulatory environments.
In this regard, a guide has been published With recommendations for AI use in Argentina. AI-based solutions enable greater levels of automation and decentralized, predictive systems for decision-making, which can contribute to better policy design, implementation and evaluation.
However, it is necessary to establish clear rules that guarantee the use of the benefits of any technological development by all sectors of society. It will strengthen the capacities for innovation, development and production of technological solutions in Argentina’s scientific and technological ecosystem.
Additionally, this arrangement highlights the importance of leveraging these capabilities within the framework of a broader strategy. Prioritizing technological sovereignty and making it possible to address social, productive and environmental issues of the country.
Expectations of use of recommendations
In the AI life cycle, steps must be taken to ensure the applicability of the model, you need to understand how it works and why it makes certain decisions. To achieve this, it is important to use transparent data and explain how it was used to train the model.
Techniques such as data visualization and model interpretation can also be used to understand how the model works. Additionally, documenting the entire model development process is valuable and accessible to all interested parties.
That’s what the rule says It is appropriate to take steps to avoid discrimination on the data used to train the AI model. Some steps can be taken:
1. Use representative data: It is important to ensure that the data used to train the model is representative of the population for which the model will be used.
2. Identify and remove dependencies: Before using the data to train the model, biases present in the data should be identified and removed.
3. Check data quality: Check the quality of data to ensure it is accurate, complete and relevant.
4. Use multiple data sources: It is advisable to use multiple data sources to reduce the risk of bias.
5. Carry out rigorous tests: Rigorous testing of the model should be done to identify any bias introduced during the training process.
6. Monitor the model regularly: It is important to continuously monitor the model after deployment to detect any biases that may arise over time.
It is also significant Auditability and auditing are important in the AI lifecycle because they help ensure transparency and accountability In the application of AI systems.
The Traceability refers to the ability to trace the origin and history of data used To train a model and the conclusions drawn by the model. Audit, on the other hand, refers to a systematic review of the development process. model and its subsequent performance.
Discovery and auditing are valuable because they allow you to identify potential biases or errors in the model, which can help improve its accuracy and reliability. In addition, they can help ensure that legal and ethical requirements related to the use of AI systems are met.
AI Adoption Map – Source: Arrangement 2/2023
In the same way, a map of the features in the recommendations has been included as a diagram so that developers take into account the way the Undersecretariat thinks the implementation of AI should be carried out. According to the recommendations of Artificial Intelligence Protocols issued by UNESCO.
For artificial intelligence to be reliable, They consider having a group of people diverse in perspectives, knowledge and experiences Differing in different areas, it helps to achieve a deeper understanding of users and their environments and thus approach AI challenges from different perspectives. This leads to more holistic and creative solutions, more intuitive and tailored to people’s real needs.
The central thrust of the document is to provide principles and recommendations for the ethical and responsible implementation of artificial intelligence projects.. This document addresses various aspects of the AI life cycle, from planning to evaluation, providing guidance on how to address the ethical and social challenges that arise at each stage.
In this sense, the following principles mentioned in the regulation can be enumerated, namely: Ethical principles guiding the development and implementation of artificial intelligence. The importance of building a diverse and diverse human team before starting the AI cycle.
It also implies the need to consider ethical aspects in data design and modeling. Importance of Incidents should be properly documented Occurs during the life cycle of AI.
It is recommended Use techniques like predictive analytics To identify potential problems and hazards before they occur. Similarly, the need for continuous evaluation of AI systems to ensure reliability, transparency and accountability is recommended.
They consider these elements fundamental to guaranteeing that artificial intelligence is developed and used for the public good, respecting fundamental rights, democratic values and ethical norms.
Amidst the strong rise of artificial intelligence, the provision clarifies: “Artificial intelligence can act only at the request of a human without any purpose of its own”.
It also highlights the rule before starting the AI life cycle or what is known as the development of this type of technology. It is relevant to consider whether the application of AI is exclusive to problem solving That’s what the development team thought.
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