Intro
Organizations are being rewritten by data. Decisions that once rested on judgment and conversation now pass through dashboards, scores, and models, and the technologies of knowledge that we build to see more clearly also decide what is worth seeing at all.
CODES studies that shift, and studies it up close. We work at the point where data, artificial intelligence, and emerging technologies meet the ordinary business of running an organization: how work gets designed, how professions defend their expertise, how boards govern what they cannot fully inspect, and how a well-meant system quietly changes who has a say. Our starting conviction is straightforward. These technologies are never neutral instruments. They encode assumptions, redistribute power, and create new forms of visibility and new forms of invisibility in organizational life.
That makes our questions transdisciplinary by necessity rather than by fashion. Organization theory, ethics, critical data studies, science and technology studies, design, management, and law each hold part of the picture, and none of them holds all of it. We would rather be usefully complicated than neatly wrong.
Our work clusters into five themes. They overlap, and deliberately so.
Theme 1: Datafication of Organizations
What changes when data becomes the main way an organization knows itself.
Turning social and organizational life into data is never a simple act of measurement. It is an act of translation, and translations lose things. We study the politics of quantification inside real organizations: which activities become countable, which stay invisible, what happens to a strategy once it is expressed as a metric, and how the people being measured learn to respond to the measure rather than the aim behind it.
This is the founding concern of the center, and the one we keep returning to. It also produces the questions leaders tend to raise first, usually some version of: our reporting has never been better, so why do we feel we understand less?
Related areas: Datafication of Organizations; Data Ontologies in Corporate and Societal Contexts; Critical Datafication Studies; Methodologies for Studying Datafication.
Theme 2: Algorithmic Management and the Future of Work
What happens to management, expertise, and working life when algorithms enter the decision.
Algorithmic systems are moving from the back office into the heart of managerial work: allocating tasks, ranking performance, scheduling shifts, flagging risk, and shaping the choices a manager believes are available. We study what that does to the craft of management, to the emotional labor of leading people, and to the professional identity of the middle manager in particular, a role that absorbs much of the friction and gets little of the attention.
We are equally interested in professions. Doctors, engineers, teachers, caseworkers, and auditors all hold expertise that is hard to formalize. When parts of that expertise are encoded into systems, the profession changes, sometimes gaining reach and sometimes losing the very judgment that made it trustworthy. We study both outcomes, without assuming which one is coming.
Related areas: Algorithmic Management; Algorithmification of Professions and Innovation; Digital Transformation.
Theme 3: Applied AI and Data Ethics
Ethics as a practical and political question, not a compliance exercise.
Most organizations now have AI principles. Rather fewer can say what those principles changed on a Tuesday afternoon. We work on data ethics and AI ethics as they are actually lived: in procurement decisions, in system design, in the meeting where someone has to decide whether the model is good enough to act on, and in the accountability that follows when it was not.
A distinctive strand here concerns what data and AI systems fail to capture. We call these data absences, and they include data deserts, data asymmetries, epistemic blind spots, and the systematic exclusion of certain kinds of knowledge from datafied systems. Absences are hard to study, because the evidence of them is precisely what is missing, so part of this work is methodological: building ways to investigate the silences and gaps that data-intensive environments produce.
Related areas: Applied AI and Data Ethics; Ethics of Digital Technologies; Critical Datafication Studies.
Theme 4: Innovation, Engineering, and Emerging Technologies
Getting to the ethical questions while the technology is still soft enough to change.
Ethics arrives too late when it arrives after deployment. We work with technology-intensive organizations on innovation and product development under data-driven conditions, on engineering management, and on the organizational readiness that emerging technologies demand well before those technologies become routine. Quantum computing is a current example. So is the fast normalization of generative AI inside firms that have not yet decided what quality means for its output.
This theme also carries a teaching commitment. CODES develops research-based engineering ethics education for the Faculty of Engineering at SDU, on the conviction that engineers building data and AI systems need genuine ethical formation rather than a compliance module. Our students meet the field’s real complexity, because that is what they will meet at work.
Related areas: AI and Innovation in Technology-Intensive Organizations; Engineering Management; Product Development and Innovation in a Data-Driven Context.
Theme 5: Policy, Governance, and Public Debate
Bringing organizational reality into the rooms where the rules get written.
Regulation of AI and datafication is being drafted now, and much of it will succeed or fail on organizational realities that policy documents rarely see. We contribute empirical research on how governance actually lands inside firms and public institutions, and we take part in the wider public conversation about data, AI, and technology in working life.
We regard this as an obligation rather than an extra. The questions the center studies are matters of public concern, so we publish in journals and we also write, speak, and argue in places where people who are not researchers can find us. We are just as committed to applying that scrutiny to ourselves: a center that studies the ethics of datafication has no business being vague about its own assumptions, limitations, and interests.
Related areas: Policy and Governance of AI and Datafication; Industry-Academia Collaboration in Digital Transformation.
Projects
ADD: Algorithms, Data, and Democracy
Our anchor project is ADD, a ten-year national research initiative examining how algorithms and data are reshaping democratic society, supported by a DKK 100 million grant from the Villum Foundation and the Velux Foundation. CODES leads and contributes to subprojects that run across the full range of our research interests, from algorithmic management and professional expertise to data ethics and governance.
A project of that length is unusual, and it is what makes some of our work possible: certain organizational changes only become visible over years, not quarters, and ADD gives us the runway to follow them.
Further projects will be added to this page as they get underway. If you would like to hear what is currently in motion, we are always happy to talk.
Working With Us
CODES works with companies, public authorities, NGOs, and international research partners, and much of our best research has started with an organization telling us about a problem it could not quite name.
That collaboration takes several forms. Some partners host research access and get an outside, rigorous read on their own datafication. Some commission analysis, or bring us in for keynotes, board sessions, and executive briefings. Some co-develop projects and funding applications with us from the start. Others simply want a conversation with people who have thought hard about a decision they are facing.
We are candid about what we offer. We are researchers, not consultants, and we will tell you what we find rather than what would be comfortable. In our experience that is exactly why organizations come back.
If any of the above speaks to a question on your own desk, please get in touch.
Professor Alf Rehn Director, CODES aamr@iti.sdu.dk