## 4 Discussion
Companies in the sector must implement social responsibility management systems that align with the 17 Sustainable Development Goals (SDGs), including a commitment to social well-being and inclusion, as well as best practices in the oil industry. The oil and gas sector plays a crucial role in socioeconomic progress. The creation of direct and indirect jobs, combined with tax collection and investment in infrastructure, boosts local economies and establishes favorable conditions for sustainable development. These measures align with Sustainable Development Goals 8 (Decent Work and Economic Growth), which promotes fair working conditions, and 9 (Industry, Innovation and Infrastructure), which emphasizes the importance of robust infrastructure for economic growth [15; 40]. In the exploration field, [PERSON] et al. [20] demonstrated the use of convolutional neural networks (CNNs) in supervised seismic interpretation, including the construction of elastic geological models. This approach is instrumental in the automatic classification of sedimentary facies from well logs, replacing time-consuming manual analyses and enabling greater accuracy and scalability in reservoir characterization. Regarding the downstream segment, although less addressed in traditional literature, [PERSON] et al. [37] highlight the use of machine learning techniques for process optimization in refineries, particularly in modeling complex processes and controlling critical variables in real time. This application enables the reduction in energy losses and improvement of operational efficiency through intelligent systems that continuously adjust process parameters, thereby meeting safety and productivity criteria. Thus, the inclusion of these studies enriches the review by showing that the gains provided by AI are not limited to the middle of the production chain but extend from the discovery of reservoirs to the final transformation of hydrocarbons. Furthermore, the implementation of ESG in the sector fosters a community relationship based on dialogue and transparency. Through guidelines such as the Code of Ethics for Suppliers and its commitment to diversity, inclusion, and human rights, the industry has made progress on issues including gender equality, racial equality, and the inclusion of individuals with disabilities. This aligns with global commitments, such as the UN Global Compact and the United Nations Empowerment Principles for Women [40; 41]. In the environmental area, [PERSON] et al. [36] proposed a hybrid model to predict internal corrosion rates in multiphase pipelines, which contributes to preventing leaks and mitigating environmental impacts, such as soil and aquifer contamination. Similarly, [PERSON] et al. [31] developed predictive models for rapid assessment of ideal good positioning, which reduces unnecessary drilling and, consequently, the carbon footprint associated with the mobilization of equipment and intensive use of natural resources. These applications demonstrate how AI can optimize drilling routes and minimize associated emissions, aligning with the climate commitments assumed by companies in the sector. Regarding the prediction of environmental impacts of techniques such as hydraulic fracturing, [PERSON] et al. [32] applied machine learning to dynamic risk analysis in critical industries, suggesting models that can be adapted to anticipate ecological, seismic, or social risks arising from these operations. These systems enhance the capacity for proactive responses to potential environmental damage, promoting transparency and responsible resource management. However, the incorporation of AI into operational and strategic decisions also poses significant ethical challenges. One example is the risk of algorithmic bias in resource allocation, such as in the automated prioritization of infrastructure maintenance or replacement in different areas, which can reflect or exacerbate social inequalities. As highlighted by [PERSON] et al.