{"id":30706,"date":"2025-05-20T13:51:43","date_gmt":"2025-05-20T11:51:43","guid":{"rendered":"https:\/\/www.fundacionbankinter.org\/noticias\/the-challenge-of-safe-ethical-and-transparent-physical-ai\/"},"modified":"2026-09-22T18:40:38","modified_gmt":"2026-09-22T16:40:38","slug":"challenge-of-safe-ethical-and-transparent-physical-ai","status":"publish","type":"noticias","link":"https:\/\/www.fundacionbankinter.org\/en\/news\/challenge-of-safe-ethical-and-transparent-physical-ai\/","title":{"rendered":"The challenge of safe, ethical and transparent physical AI"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Physical AI represents an evolutionary leap compared to traditional artificial intelligence, as it <strong>combines cognitive capabilities with the possibility of interacting tangibly with the environment<\/strong>. This approach enables machines to perceive, reason, and act in dynamic environments, which has been made possible by the convergence of technologies such as <a href=\"https:\/\/www.fundacionbankinter.org\/en\/news\/carme-torras-how-social-robotics-can-improve-peoples-lives\/\">robotics<\/a>, advanced sensors, and multimodal computing. This development not only opens up new application possibilities \u2013 from healthcare to industrial automation \u2013 but also raises crucial questions about the control and autonomy of these machines.  <\/p>\n\n<p class=\"wp-block-paragraph\">One of the main challenges of physical AI is <strong>to establish a regulatory framework that allows its enormous benefits to be harnessed<\/strong> without putting people&#8217;s safety at risk or violating fundamental ethical principles. To this end, the experts who participated in the <a href=\"https:\/\/www.fundacionbankinter.org\/en\/ftf-informes\/embodied-ai\/\">Bankinter Innovation Foundation&#8217;s Future Trends Forum, dedicated to Embodied AI, <\/a>highlighted the need for &#8220;responsible regulation&#8221; that serves as a bridge between innovation and the protection of users, pointing out that technology must evolve in a sustainable way and under strict security criteria. <\/p>\n\n<p class=\"wp-block-paragraph\">The debate on the regulation of physical AI is intensifying in the face of <a href=\"https:\/\/arxiv.org\/pdf\/2412.12140\">experiments<\/a> such as those carried out by researchers at Fudan University, in China, where the <strong>ability of AI systems to self-replicate<\/strong> was evidenced. So how do you control a machine that can replicate autonomously? What mechanisms can be implemented to prevent these systems from crossing &#8220;red lines&#8221; in their autonomy? The ability to self-replicate adds to the debate on the limit between the emulation of human behavior and the possible emergence of a self-&#8220;will&#8221; in machines.   <\/p>\n\n<p class=\"wp-block-paragraph\">In a<a href=\"https:\/\/vcresearch.berkeley.edu\/news\/how-keep-ai-killing-us-all\"> <\/a><a href=\"https:\/\/vcresearch.berkeley.edu\/news\/how-keep-ai-killing-us-all\">In an interview<\/a> with <em>Berkeley News<\/em>, experts such as Stuart Russell and Michael Cohen discussed the possibility that, by combining a vast amount of knowledge drawn from large language models with planning and coordination capabilities, <strong>AI systems can develop autonomy that exceeds desired limits<\/strong>. This debate is reminiscent of Isaac Asimov&#8217;s famous Three Laws of Robotics, which, although designed to prevent harm, are called into question when replication and self-learning become characteristic features of physical AI. <\/p>\n\n<p class=\"wp-block-paragraph\">Without falling into alarmism, the possibility of machines getting out of control and adopting misaligned behaviors \u2013 where their objectives deviate from human interests \u2013 is a recurring theme that requires a<strong> profound review of control rules and mechanisms<\/strong>. These must be based on ethical criteria and rigorous safety standards. <\/p>\n\n<p class=\"wp-block-paragraph\">First, the integration of intelligence into systems that interact with the real world implies the <strong>need for algorithms that are explainable and transparent<\/strong>. In fact, in critical contexts, such as the <a href=\"https:\/\/www.fundacionbankinter.org\/en\/news\/artificial-intelligence-in-the-automotive-sector\/\"> <\/a><a href=\"https:\/\/www.fundacionbankinter.org\/en\/news\/artificial-intelligence-in-the-automotive-sector\/\">autonomous driving<\/a> or medical assistance, it is not enough for the machine to act effectively; It is imperative that the reasoning that led to that decision be explained in an understandable way.<\/p>\n\n<p class=\"wp-block-paragraph\">On the other hand, advances in sensors and multimodal models \u2013 which integrate visual, auditory and tactile data \u2013 allow physical AI systems to have a complete perception of the environment. This has driven <strong>innovative applications in collaborative robotics<\/strong>, where robots (or <a href=\"https:\/\/www.fundacionbankinter.org\/en\/news\/elliq-the-ai-combating-loneliness-and-transforming-elderly-care\/\"><em>cobots<\/em><\/a>) work efficiently alongside humans. In the field of health, assistive robots \u2013 such as those designed to support patients in hospitals and nursing homes \u2013 have proven to be an essential complement to human care, reducing the workload of caregivers and offering precise assistance at critical moments.  <\/p>\n\n<p class=\"wp-block-paragraph\">However, <strong>transparency in the collection and management of data on these devices is critical to maintaining user trust<\/strong> and ensuring that privacy and security are not compromised. This will help prevent possible abuses and failures with serious consequences. In this sense, ethics in physical AI are mainly based on the protection of privacy, the guarantee of security and the elimination of bias in <a href=\"https:\/\/www.fundacionbankinter.org\/noticias\/que-son-los-datos-y-como-han-transformado-nuestra-sociedad\/\">data<\/a>.  <\/p>\n\n<p class=\"wp-block-paragraph\">Finally, <strong>human supervision is indispensable<\/strong>. While AI systems can automate repetitive processes and improve operational efficiency, human intervention is still essential to interpret results, adjust strategies, and ensure that critical decisions are made with the judgment and sensitivity that only a human can bring. <\/p>\n\n<p class=\"wp-block-paragraph\">Faced with these challenges and opportunities, <strong>it is urgent that governments, companies, research centers and international organizations collaborate <\/strong>to design a regulatory framework that contemplates both the need to innovate and the need to protect society. Experience in other technological fields suggests that regulation must be flexible and adaptive, allowing technology to evolve without becoming obsolete in the face of new advances. <\/p>\n\n<p class=\"wp-block-paragraph\">The experts consulted by the Bankinter Innovation Foundation recommend establishing permanent dialogue tables between key actors \u2013 including researchers, business managers, investors and regulators \u2013 to <strong>anticipate and mitigate risks<\/strong>, as well as to take advantage of the synergies derived from a shared vision of technology. Set limits without putting obstacles. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>The integration of artificial intelligence into the physical world represents one of the most revolutionary technological advances and also poses significant challenges.<\/p>\n","protected":false},"featured_media":25704,"template":"","meta":{"_acf_changed":false,"_oaas_summary":"Physical AI marks a significant advance over traditional AI by combining cognitive abilities with tangible interaction in the real world. Enabled by robotics, advanced sensors, and multimodal computing, these systems perceive, reason, and act in dynamic environments, opening applications from healthcare to industrial automation. However, new capacities\u2014such as self-replication demonstrated in some experiments\u2014raise urgent questions about control, autonomy, and whether machines might develop goals misaligned with human values. Experts emphasize the need for responsible regulation that balances innovation with safety and ethics.\n\nKey priorities include explainable, transparent algorithms\u2014especially in critical domains like autonomous driving and medical assistance\u2014robust data governance to protect privacy and prevent bias, and continued human supervision for judgment and sensitivity in decision-making. Advances in multimodal perception have enabled useful collaborative robots and assistive devices, but they also amplify risks if unchecked. To manage these challenges, governments, industry, research institutions, and international organizations should create adaptive regulatory frameworks and permanent multi-stakeholder dialogue platforms. Such cooperation can set clear limits without stifling innovation, anticipating risks while harnessing the social and economic benefits of physical AI.","_oaas_audio_url":"https:\/\/www.fundacionbankinter.org\/wp-content\/uploads\/2026\/09\/summary-30706-1790095243.mp3"},"categories":[204],"class_list":["post-30706","noticias","type-noticias","status-publish","has-post-thumbnail","hentry","category-science-and-technology"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The challenge of safe, ethical and transparent physical AI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.fundacionbankinter.org\/en\/news\/challenge-of-safe-ethical-and-transparent-physical-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" 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