Desafíos actuales de la Inteligencia Artificial

200 Desafíos actuales de la Inteligencia Artificial 1. INTRODUCTION The advent of artificial intelligence (AI) has initiated a paradigm shift in various sectors, notably in the corporate world and public administration (Samoili et al. 2021). The capa- bilities of AI to process vast amounts of data, learn from patterns, and make autonomous decisions have revolutionized traditional workflows and service delivery methods. These ad- vancements promise significant improvements in efficiency, accuracy, and personalization of services, positioning AI as a cornerstone of modern innovation. However, alongside these benefits, the integration of AI technologies also brings forth complex challenges, particularly concerning the ethical, social, and economic impacts, as well as legal and policy implications (Bankins and Formosa 2023). In the corporate environment, AI technologies are being deployed to automate routine tasks, optimize supply chains, enhance customer service through chatbots, and facilitate da- ta-driven decision-making processes (Almgren and Skobelev 2020). For instance, AI-driven analytics can uncover insights from big data that were previously unattainable, enabling com- panies to better understand market trends and consumer behavior. This, in turn, leads to more informed strategic decisions and competitive advantages. Similarly, in public adminis- tration, AI applications are streamlining operations, improving the efficiency of public ser- vices, and enabling more personalized interactions with citizens (Henman 2020). Examples include automated processing of administrative tasks, predictive analytics for public health management, and AI-powered tools for urban planning and resource allocation (Herath and Mittal 2022). Despite these promising developments, the rapid and widespread adoption of AI raises critical concerns (Hernández-Orallo 2017). Ethical considerations, such as the potential for bias in AI algorithms, the impact on employment due to automation, and overarching issues of privacy and data security, are at the forefront of public and academic discourse (Kazim and Koshiyama 2021). Additionally, the opaque nature of many AI systems, often described as “black boxes,” complicates efforts to understand and mitigate potential adverse effects. This lack of transparency can hinder the ability of stakeholders to assess the broader impli- cations of AI integration effectively. Legal and policy considerations also play a significant role in shaping the deployment of AI technologies. The need for robust regulatory frameworks to ensure fairness, accountability, and transparency in AI systems is imperative (Rodrigues 2020). Policymakers are tasked with balancing the innovation-driven benefits of AI with the protection of public interests, such as safeguarding privacy rights, preventing discrimination, and ensuring equitable access to technology. These regulatory frameworks must evolve in tandem with technological advance- ments to address emerging risks and challenges (Smuha 2021). In response to these challenges, there is a pressing need for tools that can facilitate a comprehensive and nuanced understanding of the impacts associated with AI technologies (Chatzimparmpas et al. 2020). One such tool is the Sustainability Awareness Diagram (Su- sAD) (Penzenstadler et al. 2018). The SusAD is designed to visualize the chains of effects