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Investigación & Desarrollo

versión On-line ISSN 2518-4431

Resumen

AZABACHE SANTOS, José Diego; ANGELES PIEDRA, Nelson Alejandro  y  MENDOZA DE LOS SANTOS, Alberto Carlos. IMPACT OF IT GOVERNANCE INTEGRATION ON ARTIFICIAL INTELLIGENCE ADOPTION. Inv. y Des. [online]. 2023, vol.23, n.2, pp.153-162.  Epub 31-Dic-2023. ISSN 2518-4431.  https://doi.org/10.23881/idupbo.023.2-9e.

We are currently witnessing a totally changing environment and adoption of artificial intelligence technologies. In this context, it is necessary to know how IT governance is impacting the adoption of artificial intelligence, to know exactly if the regulatory frameworks are adapting to the changes, if specific frameworks already exist or what approaches exist for artificial intelligence, as well as such as the ethical aspects that are being taken into account and also what ethical challenges organizations or governments face when adopting artificial intelligence. Obtaining results that this integration facilitates strategic decision making by aligning IT operations with business objectives and current regulations. Regarding frameworks, they include COBIT, ISO 38500, CMMI, ITIL, TOGAF, PMBOK, PRINCE2 and SCRUM, with COBIT standing out as one of the most used in this regard. In ethical issues, it was found that transparency, accountability and citizen participation are considered essential for an ethical and equitable approach to AI and the main ethical challenges are algorithmic bias, the alignment of human values and control challenges. human. In addition, it is mentioned that the training and inclusion of employees is important regarding the different fears and prejudices that they usually have regarding adoption. Finally concluding that the adoption of AI is a multidimensional process that involves ethical, legal, technical and social issues. Responsible adoption of AI requires thorough consideration of all these facets to ensure its success in various applications.

Palabras clave : Information Technology Governance; IT Governance; Artificial Intelligence; AI; Machine Learning.

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