PhD position on Decoding Value Structures through Computational Exploration at the University of Amsterdam
Giovanni Sileno
PhD position on Decoding Value Structures through Computational Exploration at the University of Amsterdam
Are you passionate about investigating fundamental research questions in computer science, information and/or systems theory, and natural/artificial forms of intelligence? Are you motivated by the idea of understanding both social and artificial systems and enhancing their interconnected influence? This project aims to investigate methods and computational models to improve our understanding of values embedded in social systems, to guide socio-technical interventions. The PhD candidate position is embedded within the Socially Intelligent Artificial Systems (SIAS) group of the Informatics Institute (IvI) of the University of Amsterdam (UvA).
Project description
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In AI research and
development, system evaluation is typically guided by a single core
question: Does this solution outperform its alternatives? Yet, critical
AI discourse makes clear that there is a prior
question that need to be answered: What does this solution work for? As
computational systems become deeply intertwined in global
socio-technical ecosystems, having a better understanding of the network
of distributed expectations, perceptions, and transformations
of value(s) revealed through social interactions is an essential basis
for addressing adequate interventions. From a computational perspective,
this "reverse engineering" pursuit can be supported by several lines of
research. Two alternative directions that
could be investigated through this PhD position are:
Value structures discovery on simulated social systems. To
what extent can contemporary computational methods help us to identify
the inner functioning of distributed socio-technical systems? Constructs
issued from agent-based modelling, complex
adaptive systems, multi-agent and normative systems literature allow us
to create increasingly sophisticated informational, motivational, and
governance mechanisms. These models can mirror established
socio-economic models, drawn for instance from historical
reconstructions or model-based theoretical frameworks, and can be
executed to generate synthetic data. Data-driven computational
techniques can then be applied for the detection of various value
translations inherent to or emerging from the underlying socio-economic
model. These techniques may rely on approaches currently developed in
AI (eg. causal discovery, reinforcement learning, explainable AI
methods), in computer science (eg. model construction through
compression), or in computational science (eg. neural differential
equations, Markov's blankets). Limitations observed in this benchmark
will be instrumental in proposing methods tailored to the reverse
engineering task.
Processing user narratives for value ascriptions. How can human experiences be leveraged as primary source of knowledge to investigate the behaviour of systems? Narratives play a primary role in constructing and securing the mechanisms of intentionality, both at individual and collective level; various contributions in cognitive science argue that they provide the highest level of integration of an individual’s knowledge system. Contemporary natural language processing (NLP) techniques may provide instruments to automatically extract insights from narratives of people detailing individual interactions with eg. a device, a service, an organization. By disentangling these micro-level scenarios in an adequate representational model, we may construct a broader qualitative behavioural model of the system. This artefact can serve as a platform for investigations, both with respect to the real system in itself (eg. for stress testing), and to involve people further (eg. to test expectations on scenarios not yet accounted, or to frame requests for other past experiences). This approach becomes particularly relevant when data or models are not available to direct inspections, and provides a more systematic role to socially-distributed "anecdotal evidence" for computational research and development, in analogy to the use of case studies in medicine.
Embedding
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This PhD project
will be conducted at the Socially Intelligent Artificial Systems group,
under the supervision of Giovanni Sileno, within the theme People,
Society and Technology of the Informatics Institute
at the Science Faculty of the University of Amsterdam.
The University of Amsterdam (UvA) is the Netherlands' largest university
(42,000 students, 6,000 staff members and 3,000 PhD candidates),
offering the widest range of academic programmes. The Socially
Intelligent Artificial Systems research group (SIAS) in
the Informatics Institute (IvI) of the UvA studies how to advance
people's everyday life and society in general through AI research,
education and impact. The group engages in incremental trust building
and value learning with stakeholders across various scientific
disciplines and application domains, and on topics that are relevant
both socially and academically.
Practical information
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More information, requirements and application instructions can be found here: https://vacatures.uva.nl/UvA/job/PhD-Candidate-on-Decoding-Value-Structures-through-Computational-Exploration/783019302/
Do you have any further questions on the project?
Please contact: Giovanni Sileno <g.si...@uva.nl>
If you feel the profile fits you, and you are interested in the job, we look forward to receiving your application. We accept applications until and including 5 January 2023.
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Giovanni Sileno
Assistant Professor | Socially Intelligent Artificial Systems (SIAS)
Informatics Institute | Faculty of Science | University of Amsterdam
https://gsileno.net || @gsileno