{"id":2425,"date":"2026-02-12T08:58:00","date_gmt":"2026-02-12T08:58:00","guid":{"rendered":"https:\/\/dbvp.nl\/?p=2425"},"modified":"2026-05-14T08:59:31","modified_gmt":"2026-05-14T08:59:31","slug":"ai-between-speed-and-wisdom-the-human-dimension-as-a-firm-boundary-condition","status":"publish","type":"post","link":"https:\/\/dbvp.nl\/en\/ai-between-speed-and-wisdom-the-human-dimension-as-a-firm-boundary-condition\/","title":{"rendered":"AI between speed and wisdom: the human dimension as a firm boundary condition"},"content":{"rendered":"

We show that AI only works when the human dimension remains above speed.
By setting boundaries and preserving human judgment, technology can serve the whole.<\/strong><\/p>\n\n\n\n

AI between speed and wisdom: the human dimension as a firm boundary condition<\/strong><\/p>\n\n\n\n

Artificial intelligence accelerates processes and expands our capabilities, but without clear boundaries, efficiency quickly overtakes wisdom. The question is not what is possible, but what is necessary and desirable. Those who place the human dimension at the center design and use AI in a way that strengthens dignity, autonomy, and connection.<\/p>\n\n\n\n

\n\n\n\n

Introduction<\/h3>\n\n\n\n

AI has, in a short time, entered healthcare, the judiciary, education, and business operations. Algorithms analyze behavior, make predictions, and steer choices. The promise is real: faster, more accurate, more accessible. At the same time, something feels uneasy. Who determines how decisions are made? Which biases travel along in data and models? What happens to professional expertise when we outsource tasks to systems that appear faster and more convincing than we are?<\/p>\n\n\n\n

The core issue is not a technical detail but a societal choice: how do we keep the human dimension leading in systems that, due to scale and computing power, can displace our capacity for judgment? This blog centers on one question: what do leaders, designers, and professionals need to do today to direct AI toward humanity and justice\u2014so that innovation does not collide with the values that sustain work and society?<\/p>\n\n\n\n

\n\n\n\n

Tension that matters<\/h3>\n\n\n\n

Proposition<\/strong>
In the AI era, \u201cmore control through systems\u201d is a temptation; what works is \u201cmore responsibility through people.\u201d Efficiency without ethics is acceleration without direction.<\/p>\n\n\n\n

Explanation<\/strong>
Algorithms optimize for what you ask them to optimize and for what you feed them. When the goal implicitly becomes speed or cost reduction, everything that is difficult to measure\u2014human dignity, context, relational harm\u2014receives too little weight. Bias in training data reinforces existing patterns: inequality becomes rationally packaged.<\/p>\n\n\n\n

Meanwhile, psychological contagion takes place: when systems speak convincingly, we are more inclined to adopt their outcomes, even when the underlying assumptions are unclear. \u201cThe model must be right\u201d signals a subtle shift of responsibility.<\/p>\n\n\n\n

Effective leadership reverses this dynamic: make goals explicit, separate tool and decision, and clarify where the boundary lies between advice and judgment. In this way, wisdom is not overtaken by speed. This is not only a moral choice; leading frameworks explicitly require demonstrable human oversight, explainability, and pathways for redress.<\/p>\n\n\n\n

\n\n\n\n

Application<\/h3>\n\n\n\n

Define in advance which decisions will, in principle, remain in human hands and on the basis of which values. Agree on three explicit criteria for each application (for example: justice, proportionality, recoverability) and refer to them in every decision.<\/p>\n\n\n\n

Ensure that \u201chuman in the loop\u201d is not merely procedural but real: professionals must have time and language to deviate, and deviations should not be punished but examined. The EU AI Act will enforce this step by step in the coming years, with stricter requirements for high-risk systems and mandatory human oversight. (Entered into force on August 1, 2024; first prohibited practices applicable from February 2, 2025.)<\/p>\n\n\n\n

\n\n\n\n

Making the undercurrent visible<\/h3>\n\n\n\n

Beneath the technology move roles, loyalties, and projections. Executives may project excessive hope onto technology as a \u201cneutral referee.\u201d Development teams may feel loyalty to speed and elegance, users to convenience, regulators to certainty.<\/p>\n\n\n\n

Without language for this undercurrent, pseudo-objectivity arises (\u201cthe system says so\u201d) or defensiveness (\u201cwe can\u2019t do anything about it\u201d). Name who carries which responsibility, where interests collide, and which values outweigh metrics. This makes the conversation honest and prevents moral judgment from evaporating into a black box. In the judiciary, this has been made explicit in European principles: fundamental rights, non-discrimination, quality and safety, transparency, and user control.<\/p>\n\n\n\n

\n\n\n\n

Practical example<\/h3>\n\n\n\n

Situation<\/strong>
A hospital explores AI support for diagnostics. The model recognizes patterns in scans and provides recommendations. Physicians appreciate the speed but fear that doubt and context will receive less space.<\/p>\n\n\n\n

Intervention<\/strong>
The hospital defines a clear objective: faster and better triage without loss of medical judgment. A decision framework is introduced with three fixed questions for each recommendation: what is the model\u2019s certainty, what contextual information is missing, and what does this mean for risk and recoverability?<\/p>\n\n\n\n

A deviation log is created in which physicians can record why they deviate and what they weigh differently. A monthly interdisciplinary meeting, including patient representation, discusses patterns, bias signals, and effects.<\/p>\n\n\n\n

Effect<\/strong>
Diagnostic turnaround times decrease, but above all trust increases: physicians feel supported rather than replaced, and patients experience better-explained decisions. Deviations prove instructive and lead to concrete improvements in both model and process. This aligns with international healthcare ethics guidelines: technology supports, the human decides, with attention to justice, inclusion, and recovery.<\/p>\n\n\n\n

\n\n\n\n

A framework: the three B\u2019s of the human dimension in AI<\/h3>\n\n\n\n

Purpose (Bedoeling), Boundaries (Begrenzing), and Proof Loop (Bewijslus).<\/strong><\/p>\n\n\n\n

    \n
  • Purpose<\/strong>\u00a0makes explicit which human good the technology serves\u2014not \u201cas fast as possible,\u201d but for example \u201cfair access to care\u201d or \u201cbetter substantiation of decisions.\u201d<\/li>\n\n\n\n
  • Boundaries<\/strong>\u00a0establish red lines: decisions that will not be automated, groups that receive extra protection, and contexts in which use is undesirable.<\/li>\n\n\n\n
  • Proof Loop<\/strong>\u00a0closes the circle: we systematically test for bias and impact, provide objection and redress mechanisms, and visibly learn from deviations.<\/li>\n<\/ul>\n\n\n\n

    This lens operationalizes international principles: human-centered, transparent, accountable, and continuously risk-managed.<\/p>\n\n\n\n

    \n\n\n\n

    What do norms and supervision require\u2014and what does that mean tomorrow?<\/h3>\n\n\n\n

    Europe has adopted a comprehensive AI Regulation. Its core elements include risk-based requirements, documentation and logging, data quality, human oversight mechanisms, and transparency. The European Commission has established an AI Office for coordination and enforcement. Organizations working with AI in Europe must now ensure governance, documentation, and meaningful human oversight.<\/p>\n\n\n\n

    Beyond legislation, international frameworks offer guidance for daily practice. The NIST AI Risk Management Framework provides practical language and processes for risk identification, emphasizing transparency, validation, monitoring, and human-centered goals.<\/p>\n\n\n\n

    The OECD AI Principles (2019, updated 2024) provide anchor points: human-centered, robust, transparent, accountable, and safe. UNESCO\u2019s Recommendation on the Ethics of Artificial Intelligence (2021) anchors human rights and societal impact.<\/p>\n\n\n\n

    Sector-specific guidance adds further detail. The WHO formulates six guiding principles for AI in healthcare. UNESCO (2023) provides global guidance for generative AI in education. In the judiciary, the Council of Europe\u2019s CEPEJ Ethical Charter defines legitimacy and rule-of-law conditions.<\/p>\n\n\n\n

    \n\n\n\n

    Law and rights: where the boundary lies<\/h3>\n\n\n\n

    The GDPR (Art. 22) grants individuals the right not to be subject solely to automated decision-making that produces legal effects or similarly significant impacts. This requires design choices: meaningful human oversight, explainability for the affected person, and genuine opportunities for objection and redress\u2014not merely \u201chuman in the loop\u201d on paper, but demonstrably in practice.<\/p>\n\n\n\n

    \n\n\n\n

    From principles to practice: documenting, testing, improving<\/h3>\n\n\n\n

    Two practical tools make transparency and accountability workable: datasheets for datasets and model cards for models. Datasheets record origin, composition, assumptions, and limitations; model cards document performance, intended use, and fairness findings. This makes explanation to boards, teams, and stakeholders concrete and enables auditing.<\/p>\n\n\n\n

    \n\n\n\n

    Pay attention to the scale of language models<\/h3>\n\n\n\n

    Large language models deliver impressive results but bring real risks: data leakage, plausible nonsense, reinforcement of bias, and ecological costs. Bender & Gebru et al. (2021) warn against \u201cstochastic parrots\u201d: convincing text without understanding. Recommendations include data documentation, clear usage boundaries, and research beyond the \u201cbigger is better\u201d path.<\/p>\n\n\n\n

    \n\n\n\n

    Governance and certification<\/h3>\n\n\n\n

    If you want to structurally ensure that AI remains \u201cunder control,\u201d a management system helps. ISO\/IEC 42001:2023 introduces the AI management system (AIMS), defining roles, processes, monitoring, and continuous improvement. Not a checklist, but a way to align goals, risks, and improvement cycles with regulation.<\/p>\n\n\n\n

    \n\n\n\n

    Public legitimacy requires public dialogue<\/h3>\n\n\n\n

    Legitimacy does not arise in the boardroom alone. Research by the Ada Lovelace Institute and The Alan Turing Institute shows that citizens want AI that is visibly fair, explainable, and testable; regulation increases comfort when it is tangible in lived experience. Organize participatory forms\u2014citizen panels, professional groups, client councils\u2014and make visible what you learn.<\/p>\n\n\n\n

    \n\n\n\n

    From reflection to movement<\/h3>\n\n\n\n

    Which decisions in our organization are too important to automate, precisely because dignity, justice, or trust are at stake?<\/p>\n\n\n\n

    In the coming week, consciously observe three moments when you or your team use \u201cthe system\u201d as an argument, and examine what that shorthand represents: time pressure, uncertainty, or unspoken interests.<\/p>\n\n\n\n

    For every new AI application, apply one decision check: can we explain in two sentences to a critical outsider how this strengthens the human dimension\u2014and which boundary we have drawn where the system does not decide?<\/p>\n\n\n\n

    \n\n\n\n

    Conclusion<\/h3>\n\n\n\n

    AI becomes directional where we give it direction. Choose one application today, name its purpose and its boundary, and start with a simple proof loop\u2014so that speed and wisdom reinforce each other, and the human dimension is not a side issue but the boundary condition under which innovation is allowed to accelerate.<\/p>\n\n\n\n

    Bronnen (APA)<\/strong><\/p>\n\n\n\n

    \u2022 Ada Lovelace Institute, & The Alan Turing Institute. (2023). How do people feel about AI?<\/em>https:\/\/www.adalovelaceinstitute.org\/wp-content\/uploads\/2023\/06\/Ada-Lovelace-Institute-The-Alan-Turing-Institute-How-do-people-feel-about-AI.pdf<\/p>\n\n\n\n

    \u2022 Ada Lovelace Institute, & The Alan Turing Institute. (2025). How do people feel about AI? 2025 survey findings.<\/em>https:\/\/attitudestoai.uk\/assets\/documents\/How-do-people-feel-about-AI-2025-Ada-Lovelace-Institute.pdf<\/p>\n\n\n\n

    \u2022 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT \u201921)<\/em>, 610\u2013623. https:\/\/doi.org\/10.1145\/3442188.3445922<\/p>\n\n\n\n

    \u2022 Council of Europe, CEPEJ. (2018). European Ethical Charter on the use of artificial intelligence in judicial systems and their environment.<\/em> https:\/\/www.coe.int\/en\/web\/cepej\/cepej-european-ethical-charter-on-the-use-of-artificial-intelligence-ai-in-judicial-systems-and-their-environment<\/p>\n\n\n\n

    \u2022 European Union. (2016). Regulation (EU) 2016\/679 (General Data Protection Regulation), Article 22. Official Journal of the European Union.<\/em> https:\/\/eur-lex.europa.eu\/eli\/reg\/2016\/679\/oj\/eng<\/p>\n\n\n\n

    \u2022 European Union. (2024). Regulation (EU) 2024\/1689 (Artificial Intelligence Act). Official Journal of the European Union.<\/em> https:\/\/eur-lex.europa.eu\/eli\/reg\/2024\/1689\/oj\/eng<\/p>\n\n\n\n

    \u2022 ISO\/IEC. (2023). ISO\/IEC 42001:2023 \u2014 Information technology \u2014 Artificial intelligence \u2014 Management system.<\/em>https:\/\/www.iso.org\/standard\/42001<\/p>\n\n\n\n

    \u2022 Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., \u2026 Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT<\/em> \u201919)*, 220\u2013229. https:\/\/doi.org\/10.1145\/3287560.3287596<\/p>\n\n\n\n

    \u2022 National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1).<\/em> https:\/\/nvlpubs.nist.gov\/nistpubs\/ai\/NIST.AI.100-1.pdf<\/p>\n\n\n\n

    \u2022 OECD. (2019\/2024). OECD AI Principles.<\/em> https:\/\/oecd.ai\/en\/ai-principles<\/p>\n\n\n\n

    \u2022 UNESCO. (2021). Recommendation on the ethics of artificial intelligence.<\/em>https:\/\/www.unesco.org\/en\/articles\/recommendation-ethics-artificial-intelligence<\/p>\n\n\n\n

    \u2022 UNESCO. (2023). Guidance for generative AI in education and research.<\/em>https:\/\/unesco.org.uk\/site\/assets\/files\/10375\/guidance_for_generative_ai_in_education_and_research.pdf<\/p>\n\n\n\n

    \u2022 World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance.<\/em>https:\/\/www.who.int\/publications\/i\/item\/9789240029200<\/p>\n\n\n\n

    \u2022 Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power.<\/em>PublicAffairs. https:\/\/www.hachettebookgroup.com\/titles\/shoshana-zuboff\/the-age-of-surveillance-capitalism\/9781610395694\/<\/p>\n\n\n\n

    \u2022 Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence.<\/em> Yale University Press. https:\/\/yalebooks.yale.edu\/book\/9780300264630\/atlas-of-ai\/<\/p>\n\n\n\n

    \u2022 Hildebrandt, M. (2020). Law for computer scientists and other folk.<\/em> Oxford University Press. https:\/\/academic.oup.com\/book\/33735<\/p>\n\n\n\n

    \u2022 Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daum\u00e9 III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64<\/em>(12), 86\u201392. https:\/\/dl.acm.org\/doi\/fullHtml\/10.1145\/3458723<\/p>\n\n\n\n

    Opmerking bij APA-stijl<\/em>: waar mogelijk zijn DOI\u2019s en offici\u00eble (primaire) publicatiepagina\u2019s gebruikt; sommige internationale richtlijnen hebben geen DOI en worden daarom met de offici\u00eble webpagina of het PDF-dossier geciteerd.<\/p>","protected":false},"excerpt":{"rendered":"

    We show that AI only works when the human dimension remains above speed.
    \nBy setting boundaries and preserving human judgment, technology can serve the whole.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[81],"tags":[],"class_list":["post-2425","post","type-post","status-publish","format-standard","hentry","category-english"],"_links":{"self":[{"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/posts\/2425","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/comments?post=2425"}],"version-history":[{"count":1,"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/posts\/2425\/revisions"}],"predecessor-version":[{"id":2426,"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/posts\/2425\/revisions\/2426"}],"wp:attachment":[{"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/media?parent=2425"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/categories?post=2425"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dbvp.nl\/en\/wp-json\/wp\/v2\/tags?post=2425"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}