Absence of information in decision-making processes (ABSENCE)
The project examines the differences of human and machine decision-making processes through philosophical investigations of the role absence plays in these processes. Absence seems to be a productive force for human communication (omission, insinuation) and decision-making, but how does it influence algorithmic systems?
We propose that the investigations of absence in human decision-making processes will further our understanding of artificial intelligence and its limitations and will be especially beneficial in contexts of automated decision-making. Focusing on the absence of information enables us to address ‘that which is present without being present’ in algorithms in ways that move beyond current ethical concerns regarding discrimination, bias, and unfairness. To these ends, we propose the metaphor of Algorithmic Implicature as a way to explore and understand that which is present without being explicit in the automated domain – i.e. implicit attitudes, intentions, and assumptions. The grand question lying underneath is what it means to be human concerning ‘intelligent’ machines.
The project will conceptualize absence (of information) as a productive and inevitable force in algorithmic processes for decision-making by exploring human social intelligence.
Objective and research questions
- Exploring and delimiting algorithmic implicature
- How does absence of information condition social intelligence as manifested in communication?
- How does absence-based inference play into decision-making?
Approach
The project is situated within philosophy of information, an interdisciplinary field between philosophy, information studies, and computer science, including information ethics, privacy studies, surveillance studies, and philosophy of technology. Further, the project takes a language philosophic and epistemological approach inspired by recent developments in philosophy of technology focusing on conceptual analyses complemented by interviews with experts working with AI in order to explore what is possible when dealing with absence.
- How does absence of information condition social intelligence as manifested in communication?: Our objective is to explore further the pragmatic realm of human communication in relation to machines – how absence plays into communication. It speaks to the human ability of abstract thinking and concept formation. In this part of the project, the concept of absence takes center stage. How does absence of information work in communication? How can we conceptualize it?
- How does absence-based inference play into decision-making?: Given that human communication is replete with absence and we never have full information awareness, it is crucial to explore absence-based inference. We will advance the theoretical and philosophical work on absence-based inference – a very fundamental but undertheorized concept. The philosophical advancements will inform the metaphor of algorithmic implicature and will – together with expert interviews – inform and advance our understanding of automated decision-making.
- Prof. Michael Lynch (Philosohy, University of Connecticut)
- Prof. Thomas Bolander (AI, DTU)
- Assoc. Prof. Kira Vrist Rønn (Philosohy, SDU)
- Assoc. Prof. Christina Neumayer (Media Studies, UCPH)
- Researcher Gry Hasselbalch (DataEthics.eu).
Common sense for humans and machines: Making decisions in the absence of information
Artificial intelligence, deep neural networks, and machine learning systems all make use of data, lots of data. But what about the data or information that is not present? The absent?
People make everyday decisions while lacking information about the options and their outcomes. To do that, we engage abductive inference (taking the best guess). This type of thinking is often referred to as common sense and it works for humans. But what about machines? Can we make algorithms use human-level common sense? Should we?
At this seminar, we will be discussing what we can learn from human intelligence, rationality and types of decision-making, and how this knowledge can be used in developing algorithmic decision-making. Specifically, we will be focusing on the productive force of absence in decision-making processes.
Synthetic data: provocations and frictions of emerging data regimes
Synthetic data are defined as information created by computer simulations or algorithms reproducing structural and statistical properties of so-called “real-world” data. In recent years, synthetic data have emerged as a promise to fix such problems of the AI industry as more and better-quality data for training datasets, protecting personal data via anonymization, and mitigating data bias. A growing field of scholarship uncovers issues regarding the emergence of new data economies and labour practices in synthetic data industries. Furthermore, synthetic data impacts the level of policy, data regulation and notions of personal data. Although the idea of synthetic data has been known and well-used since the 1940s, its potential applications as training data for AI present new challenges and require thorough investigation from social sciences and the humanities.
By surfacing frictions and provocations of synthetic data, the seminar aims to create a fruitful dialogue within a nascent field. We invite contributions on synthetic data and the synthetic turn from the social sciences and humanities, such as e.g. critical data studies, cultural studies, cultural theory, philosophy, political economy, sociology and STS.
Absences. Information, Humans, and Technology
In this workshop, we invited scholars from a range of different disciplines to think with the concept of absence (or related concepts such as missingness, nothingness, emptiness, uncertainty, etc.) in relation to their own research. For instance, how can we think about or with absence in relation to intelligence, decision-making, the analogue/digital, informed consent, the informational person, data, large language models, artificial intelligence, etc.? How does, or could, absence (the missing, nothingness, etc.) play a role in their current research? In short, when thinking about their research how does absence or related concepts play in? What are the interesting questions that arise when thinking with the concept of absence? We took a broad perspective on the concept absence and how it might play out in different disciplines.
Einarsson, A.M., and Pashevich, E. (forthcoming). explain. write. edit. summarise: An Exploratory Study on Agency Negotiations in Student-Chatbot Conversations. Human-Machine Communication.
Thylstrup, N. B. and Søe, S. O. (forthcoming). Machine Unlearning and the Politics of Algorithmic Forgetting: Three Logics of Epistemic Reconfiguration, Information Society.
Søe, S. O. and Jørgensen, R. F. (2026). Engaging with non-minds and hybrid others. Philosophical perspectives on AI and automated decision-making, In: Olsen, H. P., Slosser, J. L., Ravn, S. A., Eddebo, J., and Rosenberg, J. H. (eds.), Artificial Intelligence, Humans and the Law, Routledge.
Pashevich, E. (2025). On reliable algorithmic absence-based inference. Philosophy & Technology, 38(101), 1-15.
Søe, S. O. (2025). Metaphors We Hide Behind. What Metaphors of Data, Information, and Technology Can Tell Us About Scandinavian Intelligence Practices. In: Rønn, K.V., Diderichsen, A., Hartmann, M., and Hartvigsen, M. (eds.) Intelligence Practices in High-Trust Societies. Scandinavian Exceptionalism? Routledge.
Researchers
| Navn | Titel | Telefon | |
|---|---|---|---|
| Pashevich, Ekaterina | Postdoc | +4535321157 | |
| Søe, Sille Obelitz | Lektor | +4535321409 |
