Uncovering the Hidden Structures of Complex Challenges
Overcoming Boundaries: Developing Innovations From Conflicting Perspectives
This research intends to develop a theoretical basis for the study of the phenomenon of Collective Inference in team work. We use machine learning techniques such as topic modelling to analyze inferential patterns in expert dialogues.
Research results indicate that AI is able to identify significant causal inferences from dialogues that were not articulated - and to enhance our human reasoning in this way. Practical objective is to formulate guidelines for setting up decision-making structures across all organizational scales.
Our Vision: Visualizing the Tacit Knowledge of Expert Groups
Our research objective is to better understand how we can collectively generate innovative new solutions and/or services vis-à-vis complex challenges. This is usually tricky, because stakeholders bring in their own perspectives and requirements, and operate under at times conflicting boundary conditions.
To this end, we have developed a method and an AI tool to analyze and visualize how diverse perspectives on complex dialogical problems interact. They capture the characteristic elements of each perspective, and explicate interrelations between distinctions and interactions.
Challenges We Address
1. Understanding Complex Challenges: Navigating the intricate web of divergent perspectives and conflicting boundary conditions for leveraging collective intelligence and to inform transformative decisions.
2. Collectively Making Intelligent Decisions: Fostering a unified approach to solution development, enabling comprehensive stakeholder integration in the process.
3. AI-Enhanced Decision-Making: Empowering managers and domain experts with AI tools for refining decision-making and ensuring smarter, more balanced, and better informed, decisions.
Our Goals
1. Evaluating and Enhancing Solution Strategies: The collective intelligence lens helps to visualize what was previously hidden, like unspoken expert perspectives and barriers to innovation. Once they are on the table, solution strategies and policies can be adapted to increase speed of innovation and quality of interactions between experts within and across companies.
2. Empowering Managers and Domain Experts: Our tool helps managers leverage collective intelligence in their projects, thereby enhancing innovation and sustainability of initiatives. Domain experts can reinforce their position and contributions to the team.
3. Developing a Cutting-Edge Digital Platform: Our platform concept that involves natural language processing and knowledge graphs is intended to undergird all cooperative project activities, aiding policy decisions, and fostering success of stakeholder integration.
The Ideal Partnership
We seek collaboration with organizations that manage a larger numbers of teams, or large groups. Our past projects for instance looked at software development teams in large automotive corporations.
Our goal is to analyze team work and interactions of team members as a lens to augment collective intelligence, and to craft an AI-based natural language model tailored to the domain context. Nonetheless, we are eager to engage with organizations of all sizes and from all business, industrial or social domains.
Join Us in Shaping the Future of Collaboration
The CAIR Institute invites all decision-makers, policymakers, and innovative minds to explore the potential of our Collective Inference Method. Together, we can navigate the complexities of complex collaborative projects, leveraging collective intelligence and AI to lay the foundations for sustainable and resilient ways to innovation. Connect with us to embark on this transformative journey, where the potential of your domain experts is realized.
About The CAIR Institute
The Collective and Artificial Intelligence Research (CAIR) Institute is a beacon of innovation, a virtual consortium of researchers dedicated to the exploration of collective intelligence and AI's potential. Our mission transcends academic inquiry, aiming to equip teams, organizations and communities with the tools to foster innovation, adaptation, and transformation.
Read about our Research
Researching digitalized work arrangements
In this paper, we develop the mathematical basis to study divergent perspectives on complex problems in expert dialogues.
Read the article here:
Rehm, S.-V., Goel, L., & Junglas, I. (2022) Researching digitalized work arrangements: A Laws of Form perspective, Information & Organization, 32(2), June 2022, 100391. DOI: 10.1016/j.infoandorg.2022.100391
Observing artifacts: How drawing distinctions creates agency and identity.
Our research presented in this paper explains how our dialogues oscillate around divergent interpretations - and how this allows us to create shared understandings.
Read the article here:
1. Rehm, S.-V., Goel, L., & Junglas, I. (2023). Observing artifacts: How drawing distinctions creates agency and identity. In Robert D. Galliers and Boyka Simeonova (Eds.), The Cambridge Handbook of Qualitative Digital Research, Cambridge University Press, DOI: 10.1017/9781009106436
Managing Networked Innovation on Digital Infrastructures:
A Cybernetic Method for Collective Sensemaking of Complex Dialogical Problems
In this article, we describe the origins and conception of our Collective Inference Method, and our pilot studies.
Read the article here:
Rehm, S.-V.; Bondel, G. (2021) Managing Networked Innovation on Digital Infrastructures: A Cybernetic Method for Collective Sensemaking of Complex Dialogical Problems. ACM Collective Intelligence Conference 2021 (CI2021), Copenhagen Business School, Denmark [Link]
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