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Artificial intelligence from a social and environmental perspective

Artificial intelligence is a technology that is profoundly transforming the digital landscape and accelerating its development. However, beyond its role as a disruptive force, it is essential to carefully evaluate its social and environmental impact to maximize the opportunities it presents and effectively address the challenges it poses.

Find out more about artificial intelligence from a social and environmental perspective.

Isabel María Álvaro Alonso

Reading time: 6 min

The social impact of AI

Positive aspects

Artificial intelligence (AI) has become a pivotal tool in the development of new digital solutions that enhance the well-being of the population. For instance, AI is revolutionizing healthcare by enabling more accurate diagnoses, improving patient care, and accelerating drug development.

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By analyzing medical images, clinical data, and other types of information, AI can detect signs of disease that might otherwise go unnoticed by the human eye. This not only reduces the margin of error but also enables faster diagnosis. Additionally, AI-based tools allow for continuous and remote monitoring of patients, facilitating real-time problem detection and enabling timely interventions. Furthermore, by analyzing patients’ genetic and biomarker data, AI can aid in the development of medications specifically tailored to the unique biological characteristics of each individual.

In the workplace, AI also enhances human capabilities and improves productivity. It can serve as a tool for generating ideas, creating prototypes, or providing inspiration, leading to innovative outcomes through the collaboration between human imagination and AI’s technical execution. Moreover, AI can streamline the organization and analysis of data and information, as well as automate time-consuming routine tasks.

Negative aspects

AI has faced criticism regarding its ethical implications and its impact on the labor market. First, in terms of ethics, concerns about transparency, bias, and accountability are particularly prominent. AI algorithms often function as ‘black boxes,’ making it difficult to understand how they arrive at specific decisions or recommendations. This lack of transparency is troubling, as it prevents us from tracing the system’s reasoning process or understanding why a particular decision was made. Furthermore, AI systems are trained on data that may contain biases, reinforcing subjective opinions related to gender, nationality, age, or religion.

Recognizing the importance of ethics, Telefónica established and published its first code of ethics in 2018, which has guided the design, development, and implementation of commercial products and services, as well as the company’s internal processes that incorporate AI. In light of the new challenges posed by this technology, Telefónica’s AI principles were updated in 2024 and has implemented a Responsible AI governance model across the entire group to address these evolving issues.

Regarding changes in the labor market, the World Economic Forum’s Future of Jobs Report 2023 estimates that 19% of the workforce could have more than 50% of their tasks automated by AI. However, the report also predicts that the demand for AI and machine learning specialists will positively impact job creation, with a 40% increase in the need for specialists in this field, translating to approximately one million new vacancies worldwide.

The transformation of the employment landscape necessitates an urgent focus on retraining and skills upgrading. Therefore, it is crucial to implement training strategies that encompass multiple levels and approaches. In this context, Telefónica is one of fourteen companies leading the “Reskilling for Employment” initiative, part of the European Round Table’s efforts to offer new opportunities to Europe’s labor market and close the skills gaps in the digital transition, ensuring an inclusive, competitive, and prosperous future.

The environmental impact of AI

Positive aspects

AI’s analytical capabilities enable the interpretation of vast amounts of data in a short time. In a context where climate change is intensifying extreme weather events and negatively impacting biodiversity, AI can accurately analyze satellite imagery to map environmental changes and provide policymakers with valuable insights, aiding in the formulation of informed decisions that are responsive to the challenges we face. Additionally, the application of AI in environmental sciences could transform these disciplines into ‘big data’ sciences, driving innovation in climate change mitigation and adaptation efforts, as well as in biodiversity conservation.

Moreover, AI can monitor the condition of facilities and track ongoing processes. When combined with the Internet of Things (IoT), predictive maintenance allows for early intervention in potential failures, thereby extending the lifespan of equipment and reducing the generation of electronic waste. Similarly, when AI is integrated throughout a process, it can analyze the collected data and generate potential scenarios that optimize the efficiency of resource use, including energy, water, and raw materials. This enhanced efficiency not only leads to resource conservation but also contributes to the reduction of associated greenhouse gas emissions.

Negative aspects

Due to its data processing and storage requirements, AI is highly energy-intensive. For instance, according to the International Energy Agency, a single ChatGPT query consumes 2.9 watt-hours of electricity, compared to just 0.3 watt-hours for a Google search. While certain AI innovations may accelerate processing speeds without a proportional increase in electricity consumption, the overall expansion of AI usage will inevitably lead to greater energy demands.

AI is also expected to drive increased demand for new data centers, which, due to their stringent temperature and humidity control requirements, consume substantial amounts of energy. These centers operate 24 hours a day, 7 days a week, year-round. The vast storage and processing needs, coupled with strict environmental controls, make these facilities extremely energy- and water-intensive. The high resource consumption is primarily attributed to IT demands, cooling systems, lighting, power distribution, and other operational necessities. Currently, data centers worldwide account for 1-2% of total energy consumption, but this figure is projected to rise to 3-4% by the end of the decade. Goldman Sachs Research forecasts a 160% increase in data center energy demand by 2030.

Telefónica, as part of its AI principles, is committed to minimizing the environmental impact of this technology in its operations to reduce its carbon footprint and optimize the energy efficiency of AI systems. Additionally, in light of the increasing trend in energy consumption, the company recognizes the importance of promoting energy efficiency in the digital ecosystem. In this regard, Telefónica has been promoting energy efficiency in its activities for years, which has allowed it to reduce energy consumption by 8.6% since 2015, despite an 8.6-fold increase in traffic.


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