Seven principles for a Responsible AI

1-Social responsibility

The responsibility for AI use requires effective regulations accepted and supported by society. It is necessary to be conscious of the benefits and dangers of its use, promoting citizen participation in public politics formulation. An indiscriminate use of AI could standardize human creativity.

2-Assure diversity

Considering ethnic, cultural, and generational perspectives from a multiple and trans disciplinary approximation in all development periods to achieve emphatic interaction of society with AI systems.

3- Preserve vulnerable groups

Children, active technology users, must receive special attention in the AI system´s design to guarantee no harmful interactions that impact their cognitive and emotional development by the link replacement. It must consider people with different abilities and advanced ages challenges, among other groups.

4-Bias control design

Like any intelligence, AI also has bias effects. Its decisions turn out biased by the cultural information it has been trained on. This situation can result in operational and discriminating risks. Multidisciplinary experts must work together to identify and mitigate these risks, from design to implementation.

5-Civil rights guarantee

AI could have a significant impact on civil rights by preventing personal identification, manipulation, or unequal access to opportunities. The balance between technological innovation and civil rights preservation is fundamental in a requirement to achieve socially responsible AI.

6-Mental health control impact

Artificial Intelligence (AI) is employed for the identification and mitigation of mental health issues. Conversely, excessive exposure, link substitution, and the consideration of AI as universally comprehensive knowledge may impact human creative potential.

7-Guarantee human autonomy in decision-making

AI systems should be designed to augment human capabilities rather than replace them. This necessitates discretionary decision-making with implications for various domains, including family, education, and legal matters, among others. Humans, who are inherently beyond reducible statistical and algorithmic predictions, must play a crucial role in distinguishing the nuances of each decision area for AI. 


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