Introduction

Artificial intelligence (AI) has transcended the realms of specialized research to become deeply embedded in everyday life. Its presence is realized in diverse interactions, from assistance with household chores to the optimization of complex processes in professional settings (Parra Ferreras, 2020) and Lope Salvador et al., 2020). This integration modifies habits and expectations, substantially transforming the human experience (2020).

Contextualizing artificial intelligence in everyday life

AI manifests itself in multiple widely used devices and services, facilitating communication and access to information (Gunkel et al., 2017) and Nathalie Landeta Bejarano, 2019. This technology reshapes aspects of domestic life, urban mobility, and social interactions (Pérez Contreras, 2011). Its widespread adoption redefines the interfaces between humans and the digital environment, as well as expectations for efficiency and personalization in services (Lope Salvador et al., 2020).

Problem statement and analysis objectives

The permeability of AI in everyday life raises questions about its long-term implications. A rigorous examination of this situation requires understanding both the opportunities it enables and the challenges it imposes (Corvalán, 2018) and (2020). This analysis identifies the mechanisms by which AI is integrated into everyday life and its multidimensional effects. It seeks to offer a well-founded assessment of its influence and project possible future scenarios for balanced integration (2020).

Social, technological and ethical importance

The relevance of studying AI in daily life is undeniable, given its transformative impact. From a social perspective, it alters the dynamics of interaction and access to opportunities (Álvarez Calderón & Ramírez Pedraza, 2020). Technologically, it drives innovation and efficiency in various sectors (Ruano Enríquez et al., 2019). Ethically, it generates crucial debates about privacy, autonomy, and equity (Valderramas, 2020) and Cerrillo i Martínez, 2019. A deeper understanding of these aspects is essential to guide its development responsibly (Gallo Aponte et al., 2020) and Santos Jr. et al., 2019).

Thematic overview of artificial intelligence in everyday life

The incorporation of AI into everyday life encompasses a wide range of applications that shape human interaction with technology (Lope Salvador et al., 2020). The evolution of these systems reflects a progression from simple computational tasks to more advanced cognitive capabilities, redefining the human experience in diverse contexts.

Historical and conceptual evolution of artificial intelligence applied to the domestic and social environment

The trajectory of AI, since its conceptual origins in the 20th century, has moved toward the optimization of human processes and activities (Chiappin et al., 2020). Initially, AI focused on automating calculations and solving logical problems. Over time, it diversified to include pattern recognition and natural language processing (Sancho Escrivá et al., 2020). Currently, there is a strong trend toward personalization and intuitive interaction, which is integrated into domestic and social life (García, 2015). Interaction with these systems even transforms everyday communication and recreational activities (Andrade Albán et al., 2018).

Dominant models and architectures in everyday AI applications

Everyday AI relies on diverse models and architectures designed to meet specific user needs (Sergei A. Vorontsov et al., 2019). These systems process large volumes of data to deliver personalized and efficient solutions. The effectiveness of these models depends on their ability to learn and adapt based on constant interaction with users.

Virtual assistants and home automation

Virtual assistants, such as Alexa or Google Assistant, use natural language processing (NLP) to interpret voice commands and perform tasks (Sancho Escrivá et al., 2020). These systems control smart home devices, manage schedules, and provide real-time information. Home automation improves comfort and energy efficiency (Nathalie Landeta Bejarano, 2019).

Intelligent systems in mobility and transportation

AI optimizes transportation routes, manages traffic, and contributes to the development of autonomous vehicles (Nathalie Landeta Bejarano, 2019). Navigation systems use predictive algorithms to avoid congestion and reduce travel times. In public transportation, AI optimizes schedules and resource allocation (De Souza & Soares, 2018).

AI in personal information and communication services

AI personalizes information flows, from content recommendations to email filters (Andrade Albán et al., 2018). Machine translation systems and chatbots improve global communication. Social media employs AI to moderate content and personalize the user experience, affecting how information is consumed and shared (Gunkel et al., 2017).

Integration of AI in educational, work and healthcare contexts

AI is being integrated into educational, work, and healthcare contexts, generating transformations in service delivery and methodologies (Lope Salvador et al., 2020). This integration seeks to improve efficiency, personalization, and accessibility in critical areas of human life. However, its implementation requires an adaptation of traditional practices and a reevaluation of the skills needed to interact with these systems (Barrientos-Avendaño & Areniz-Arévalo, 2019).

Learning Personalization and Intelligent Tutoring

In education, AI facilitates the personalization of learning by adapting content to individual student needs (Elena Zotikovna Vlasova et al., 2019) (Boran Sekeroglu et al., 2019). Intelligent tutoring systems identify students’ difficulties and offer targeted feedback. This improves the quality of teaching and the productivity of the educational process (Elena Zotikovna Vlasova et al., 2019).

Optimization of professional processes and assisted decision-making

AI optimizes processes in the workplace, from automating repetitive tasks to assisting with complex decision-making (Orantes Kestler, 2020). AI-powered data analysis tools offer valuable insights for business management and strategy (Ruano Enríquez et al., 2019). This leads to increased efficiency and productivity across various sectors (Álvarez Calderón & Ramírez Pedraza, 2020).

Medical diagnosis, monitoring, and digital well-being

In the healthcare sector, AI supports medical diagnosis through the analysis of images and clinical data (Vinagre et al., 2020). Smart monitoring devices enable continuous health and wellness tracking (Sancho Escrivá et al., 2020). Although AI is an auxiliary tool, it improves the accuracy and speed of medical care (Vinagre et al., 2020).

Critical Analysis: Impact and Systemic Implications of AI in Daily Life

The spread of AI in everyday life generates a series of complex and multidimensional impacts. These effects are not limited to technical improvements but permeate social, ethical, and economic structures. Evaluating these changes requires a systemic perspective, considering both the benefits and potential disruptions.

Social and cultural transformations induced by the massive adoption of AI

The adoption of AI is redefining social interactions and cultural practices (Lope Salvador et al., 2020). There has been a shift in the way people manage their information, communicate, and access services (Nathalie Landeta Bejarano, 2019). Growing digitalization, driven by AI, is shaping a new reality that impacts lifestyles (Lope Salvador et al., 2020) (Marín García, 2020). This impacts domestic organization, recreational activities, and the perception of time and space (Pochintesta, 2019) (Cataldo Díaz, 2020).

Ethical, legal, and privacy challenges in the everyday deployment of AI

The proliferation of AI in everyday life poses significant ethical, legal, and privacy challenges (Valderramas, 2020) and Cerrillo i Martínez, 2019. AI regulation seeks to ensure its reliable and human rights-compliant use (Gallo Aponte et al., 2020).

Opaque algorithms and implicit biases

The opacity of AI algorithms and the presence of implicit biases are a central concern (Cerrillo i Martínez, 2019). These biases, often derived from training data, can perpetuate or amplify existing inequalities (Hartmann, 2020). The lack of algorithmic transparency makes accountability and the identification of discrimination difficult (Corvalán, 2018).

Surveillance, data protection and individual autonomy

The massive collection of personal data by AI systems raises concerns about surveillance and privacy protection (Lope Salvador et al., 2020). Individual autonomy is affected by AI’s ability to influence decisions and behaviors (Santos González, 2017). Personal data protection legislation is essential to mitigate these risks (Gallo Aponte et al., 2020).

Access gaps, technological inequality and digital exclusion

The expansion of AI can exacerbate access gaps and technological inequality (Orantes Kestler, 2020). The availability and access to AI technologies are uneven, leading to digital exclusion for marginalized communities (Perezgrovas Garza, 2016). Asymmetries in technological development require an approach that promotes inclusive innovations (Corvalán, 2018).

Sustainability and environmental effects of domestic AI consumption

The growing use of domestic AI raises questions about its sustainability and environmental impacts. The energy demands of data centers and smart devices are considerable (Alonso-Calpeño et al., 2019). The production and disposal of AI-related hardware also contribute to the ecological footprint. Sustainable development of AI requires considering these impacts (Corvalán, 2018).

Conclusion

The integration of AI into daily life constitutes a profound transformation, altering human interactions and processes in diverse areas. This permeability brings with it both substantial opportunities and complex challenges, requiring a balanced approach to its management and future development. Understanding its social, ethical, and technological implications is essential to guiding its evolution in a way that benefits society.

Argumentative summary: balance of opportunities and risks

AI offers significant opportunities in personalizing services, optimizing processes, and improving quality of life, especially in education and healthcare (Boran Sekeroglu et al., 2019) and Vinagre et al., 2020). However, risks related to algorithmic opacity, inherent biases, data privacy, and potential digital exclusion have been identified (Cerrillo i Martínez, 2019) and Hartmann, 2020). The balance suggests that AI can be a powerful tool, but its development requires constant and ethical oversight (Valderramas, 2020).

Future projections and recommendations for responsible integration

Projections indicate a more autonomous and pervasive AI (Santos González, 2017). For responsible integration, the following is suggested:

• Promote transparency and auditability of algorithms (Corvalán, 2018).
• Establish robust regulatory frameworks for data protection and privacy (Gallo Aponte et al., 2020).
• Promote digital literacy and equitable access to technology (Lope Salvador et al., 2020).
• Evaluate the environmental impacts of AI infrastructure (Alonso-Calpeño et al., 2019).

These measures are key to ensuring that AI contributes to sustainable human and social development.

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