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IRIS Research

Critically reflecting on intelligent systems in politics, economics, education, literature, and society.
[Photo: fotografierende from Pexels]

IRIS Research

IRIS research is interdisciplinary and interfaculty. It focuses on the societal impacts of intelligent systems on society within politics, literature, machine learning, economics, and education. In addition to the three IRIS3D independent research groups and two IRIS researchers, there are projects funded by SimTech and organized by other IRIS members. IRIS also connects with the Participation and Deliberation Labs of ZIRIUS and the Platform for Reflection of SimTech.

DALL-E generated photo of triple exposure of Computational Digital Psychology, Ethics of generative AI systems, and Diversity-Aware NLP Intelligent Systems together.
DALL-E generated photo of triple exposure of Computational Digital Psychology, Ethics of generative AI systems, and Diversity-Aware NLP Intelligent Systems together.

Research Topics in IRIS Include

  • Ethical and societal challenges of intelligent systems
  • Risks and benefits of automated decision-making in various fields of application
  • Participative/constructive technology assessment (p/cTA)
  • Scenarios analysing contexts and preconditions of the implementation of intelligent systems in society
  • Prerequisites for a socially desirable use of learning systems and autonomous decision support or decision-making
  • Responsible forms of Human-Computer-Interactions
  • Socioecological impacts of digitization
  • Limits and critical aspects of datafication, artificial intelligence, learning systems, and smartification
  • Investigation of how contemporary literary narratives featuring AI and robots can be used as tools for critically reflecting upon intelligent systems

IRIS3D Independent Research Groups

Research Focus:

  • Interpretability and analysis of NLP models
  • Recognizing harmful behaviors of language systems
  • Applications of stylometry and authorship attribution techniques for preventing biases in NLP architectures
  • Computational modeling of subtle linguistic biases
  • Cross-lingual structure prediction

Team:

Project Details:

Research Focus:

  • Investigating the ramifications of intelligent systems, in particular generative AI, for different fields of society
  • Research on AI ethics and governance, with a particular focus on how ethics can be efficiently put into practice in organizations
  • Exploring the overlap between machine learning approaches and insights from cognitive science
  • Empirically analyzing machine behavior and emergent abilities in large language models

Team:

Project Details:

Research Focus:

  • Intelligent systems’ role in the provision of politically relevant
    information, and its consumption/processing by different individuals
  • Relations between the degree of homogeneity of individuals’ information environment and political opinion formation
  • The spread of "fake news" and "disinformation" online and effects on opinion formation
  • Creation of intelligent systems fostering individuals’ democratic capabilities
  • Digital Phenotyping and Political Microtargeting
  • Data economy and digital business models

Team:

Project Details:

 

IRIS3D Seed-Funded Projects

Project Focus

Using thought experiments as a context for creating and exploring a novel artificial intelligence (AI) system that captures dynamics of human cognition in the form of mental models. A representation and prediction system is trained to emulate the basic causal chain-of events occurring when humans engage in thought experiments. Basic operations are cast into simple dynamical rules and objects, defined in a similar way as in cellular automata and
inspired by production rules in symbolic cognitive architectures, using an autoencoder-like architecture for enhanced abstraction. Potential outcomes of thought experiments can thus be “played out”, generated by the physical dynamics, and iteratively refined. These “AI augmented” thought experiments provide a direct means for self-reflection and critique of one’s mental models (i.e., unconscious biases and presumptions), and stimulate a creative process. At the same time, this enables building an AI model consistent with our own understanding. Cross-disciplinary implications are discussed upon evaluating the system from the perspectives of cognitive science, physics of non-equilibrium, complex systems, as well as for AI performative aspects like knowledge transfer.

Project Members

Project Focus

Online political discussions may help to weigh arguments and shape opinions potentially including considerations from all stakeholders. Thereby, discussion platforms may act as intelligent brokers for a fruitful exchange of opinion. In practice, resentful discussions abound and often they disparage specific groups from equal participation. In this research project, we plan to develop methods of natural language processing and network data analysis to identify 
such unfair treatment. Using the Reddit Politosphere dataset as a case study, we will provide specific statistical analyses and evaluation of developed methods. Based on such methods and analyses, we plan to inform discussion platforms about problems and potential means of containment.

Project Members

Project Focus

The construction industry needs to urgently become more productive and sustainable. Automation is a common approach to increase efficiency. But this approach is challenged by a lack of qualified workers in the construction industry. This project at the intersection of architectural computing with social science addresses this challenge with a novel, AI-based method of human-robot collaboration that (1) replaces demands for human physical labor with demands for technical skills and that (2) engages workers by stimulating creativity and ensuring agency. Shifting from physical endurance to professional input and intellectual contributions opens construction to broader demographics that are otherwise excluded and allows them to contribute more meaningfully and with higher-value skills.

The project integrates perspectives from feminist technoscience—a transdisciplinary field offering distinct ways of thinking about societies, technologies, bodies, power, and environments—to develop a HRC method that attracts skilled staff and supports skills and decision-making while considering uncertainties inherent in construction. Specifically, the project uses methods from experimental democracy to ensure a fair and responsible development process for the HRC method that engages a diverse set of potential users

Project Members

Project Focus

The project investigates the potentials and challenges for democratic systems that come with the proliferation of generative AI applications (gAI) within the realm of public spheres and political deliberation. It focuses on three central aspects: First, the epistemic dimension of gAI both as a source of knowledge in political debate as well as its increased potential to further disinformation. Second, the ethical dimension of the quality of training data scraped from public sources and its implications for the representation of social groups and political opinions within these data and thereby also within answers provided by gAI models. And third, the democracy theoretical dimension of gAI applications as “participants” of political discourse and its repercussions for public deliberation and public reason.

With a genuinely interdisciplinary work plan, the project is able to analyze these three dimensions from a philosophical as well as a machine learning perspective and to discover crucial limitations as well as conditions for institutional and technology design to reap possible benefits of the technology. The overarching goal of the project is to develop and submit an interdisciplinary grant proposal (VW, ERC) for a larger project on the topic of “Generative AI and Public Reason”.

Project Members

Project Focus

Online platforms connect individuals from diverse backgrounds across the globe, fostering a sense of global community. However, this vast reach also carries a potential risk: the formation of echo chambers that inadvertently reinforce existing stereotypes and contribute to polarization. This project uses the revision history of the collaboratively edited wikiHow1 platform to investigate the extent to which such a risk can be evidenced by measurable dynamics and how artificial intelligence methods can be used to mitigate polarization. Specifically, we study instructional texts written in a version for women and a version for men and examine how audience-specific changes over time contribute to making articles more polarizing and in how far such changes actually suit the needs and preferences of the corresponding target groups. Finally, we test whether large language models (LLM) can remedy undesired polarization effects by merging articles written for specific audiences into more balanced articles for a general audience. We bring together methods from computational linguistics and experimental psycholinguistics to carry out the project and to measure its success.

Project Members

Project Focus

Feedback enables the maintenance of motivation and the correct self-assessment of learners' individual progress. As individual tutoring by instructors is not always possible due to limited resources, Intelligent Tutoring Systems (ITS) can supplement such instructors to a certain extent. However, ITSs so far only adapt to the status of the learner, but not to the learners' diverse backgrounds (e.g., culture, gender, and socioeconomic status). Furthermore, gamification can be a suitable tool to increase learners' motivation. To investigate the differential impact of adaptive feedback and gamified learning elements on diverse learners in terms of learning outcomes, learner acceptance, and learning motivation, an ITS with these characteristics will be developed in this project. Such an adaptive and gamified ITS aims to address diverse students' needs and thus reduce course heterogeneity, thereby offering new opportunities for inclusive teaching. Therefore, this project forms the basis for future research on the ethically guided usage of ITS in classrooms, cyber-human learning, and the trustworthiness of AI in education.

Project Members

Research projects related to critically reflecting on intelligent systems

Funding: German Research Foundation (DFG) and the Research Council of the University of Stuttgart.

PI: Dr. Curtis Runstedler

Duration: 04/2022 - 12/2025

Research Questions:

  • How can literary narratives function as critical apparatuses for investigating concerns and ideas about intelligent systems?

  • How are robots and AI commodified and exploited within their environment in these narratives?

  • Do these narratives present hopeful outcomes or solutions for the commodification of the robotic or AI character?

Funding: German Research Foundation (DFG) and the Research Council of the University of Stuttgart.

PI: Lukas Erhard

Duration: 04/2022 - 12/2025

Research by IRIS Members

Funding: Volkswagen Foundation (funding scheme "Artificial Intelligence - Its Impact on Tomorrow's Society")

Applicants (related to IRIS): Prof. Dr. Jonas Kuhn

Duration: 10/2021 - 09/2025

More Information: Project description

Institute for Modelling and Simulation of Biomechanical Systems

This research focuses on human physical interaction with the environment using digital human body models.

Research Questions:

  • Do these models allow for unbiased and ideology-free decision-making?
  • Do these models enable improved human-centered and barrier-free design of the built environment?

IRIS Members Involved: Prof. Dr. Syn Schmitt

More Information: Research in Computational Biophysics and Biorobotics

Department of Teaching and Learning with Intelligent Systems

The equal professional participation of people with special needs (e.g. people on the autism spectrum or attention deficit disorder) enriches collaboration through diverse perspectives and strengths. However, difficulties in social interaction often lead to the failure of professional life plans despite high cognitive abilities. Innovative technology-supported training measures can take a preventive approach at this point and strengthen interpersonal skills through learning experiences in a protected setting.

IRIS Members Involved: Jun.-Prof. Dr. Maria Wirzberger

More Information: UFO Project

Institute for Natural Language Processing

This research focuses on Natural Language Processing applications to support (and understand) deliberative processes (from political discourse to e-deliberation) and the Interpretability (and cognitive plausibility) of Natural Language Processing models.

IRIS Members Involved: Dr. Gabriella Lapesa

More Information: Powering up E-DELIBeration: towards AI-supported moderation

Research Projects with SimTech

Project Description

As new technologies sweep the world, they are poised to bring extensive changes that will affect all aspects of our society and life, as well as see organizations increasingly adopt artificial intelligence (AI) to optimize various aspects of their operations. While AI offers new opportunities, it also presents unintended challenges that societal and organizational leaders must carefully navigate. Numerous predictions have been made about the forthcoming AI revolution, ranging from optimistic ones that predict a science fiction utopia driven by nanotechnology and AI, to pessimistic ones that see AI endangering the very survival of human beings, to pragmatic ones that believe AI technologies can be controlled through effective regulation, and finally the doubting ones stating that the AI will never have human-like capabilities and will never become a threat.

This project aims to develop a deeper understanding of systemic structures that govern dynamics of AI adoption within society and organizations, which will help us identify potential risks and opportunities, as well as propose actionable policies that will work for the betterment of our socio-economic systems. We aim to investigate this topic through conceptual analysis supported by systems thinking, system dynamics modeling and computer simulation, and laboratory experiments.

Project Information
Project Name

Reflecting on societal and organizational impacts of AI

Project Leader

Maria Wirzberger

Project Member

Ivan Dula, PostDoc Researcher

Project Partners

Ksenia Keplinger, Independent Research Group “Organizational Leadership and Diversity,”

Max Planck Institute for Intelligent Systems, Stuttgart, Germany

Publications

Ðula I, Berberena T, Keplinger K, Wirzberger M (2023) Hooked on artificial agents: a systems thinking perspective. Frontiers in Behavioral Economics.  2:1223281.

https://www.frontiersin.org/articles/10.3389/frbhe.2023.1223281/full

Ðula I, Berberena T, Keplinger K, Wirzberger M (2023) Conceptualizing responsible adoption of artificial agents in the workplace: A systems thinking perspective. In 20th Conference of the Italian Chapter of AIS (Association for Information Systems) (21).

http://www.itais.org/itais2023-proceedings/pdf/ItAIS2023_paper_21.pdf

Ðula I, Berberena T, Keplinger K, Wirzberger M (2023) Hooked on artificial agents: A simulation study. In International Conference onData-Integrated Simulation Science (SimTech2023). University of Stuttgart.

https://www.simtech2023.uni-stuttgart.de/documents/Theme-4/Dula-Ivan.pdf 

Duration

09/2022 - 08/2024

Funding

The project is funded by the German Research Foundation within the Cluster of Excellence "Data-Integrated Simulation Science" (EXC 2075).

Link to Publication

Project focus

The transmission of infectious diseases depends on human behavior and their relations. However, current epidemiological models consider social structures only at a highly abstract level. To increase the predictive capability and explanatory power, models of human behavior incorporating social complexity are therefore urgently needed. We address this gap by developing an agent-based approach that utilizes comprehensive micro-level data of complete households. This allows us to create artificial societies that are representative for underlying social structures and contact networks. Based on comprehensive COVID-19 data, we then utilize Bayesian model calibration to estimate unknown parameters and quantify their uncertainty. Conducting various simulation experiments will then allow us to identify super-spreaders and assess the efficiency of interventions. Thus, the project makes not only a substantial contribution to a holistic ”Digital Human Model”, but is also a methodological response to the increasing demand for empirically-calibrated simulation models. However, computational models always bear the risk of incorporating biases. We will tackle this challenge, which is enhanced by potential stigmatization of super-spreaders, by incorporating sensitivity analyses and so pave the way for the development of systematic ”methods of reflection”.

Duration

07/2022 – 12/2025

Involved IRIS Members
Funding

The project is funded by the German Research Foundation within the Cluster of Excellence "Data-Integrated Simulation Science" (EXC 2075).

IRIS Research Updates


IRIS3D - Research Group Leaders

Contact: IRIS Researchers

This image shows Curtis Runstedler

Curtis Runstedler

Dr.

Wissenschaftlicher Mitarbeiter

This image shows Lukas Erhard

Lukas Erhard

M.A.

Academic staff member

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