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Social science meets AI

Imagine an AI that learns from you, understands your needs, and works alongside you like a true partner. That's the vision behind PEER, merging social science and AI to redefine decision-making. We're trying to go beyond traditional AI to create an AI that's truly useful and trustworthy.

Why the social science connection?

It's all about understanding human needs, values, and expectations. By integrating this understanding into AI development, PEER:

  • Empowers end-users
  • Tracks trust and acceptance
  • Delivers real-world usefulness

PEER achieves this through a cross-disciplinary approach:

  • Co-creation workshops: These workshops bring together users and AI researchers to engage in collaborative design and discussion, ensuring user needs are reflected in the AI's development.
  • Wizard-of-Oz prototyping: This technique simulates human-AI interaction, allowing users to experience the AI and provide feedback before formal deployment.
  • Pilot experimentation: By offering users direct interaction with early versions of the AI, PEER gathers valuable insights and iteratively improves the system based on user feedback.

Why is this important?

The challenge of limited user trust and acceptance remains a critical hurdle in deploying sequential decision-making AI systems. To address this, PEER focuses on co-creation, facilitating deeper bidirectional interaction between humans and AI.


This fosters:

  • Enhanced mutual learning and reasoning: By directly engaging with the AI, users become active participants in shaping its development and capabilities.
  • More productive collaborative work: The co-creation process allows for seamless knowledge exchange and collaborative problem-solving, leading to superior outcomes.
  • Increased user trust and acceptance: Through active participation and transparency, users develop a deeper understanding of AI capabilities and limitations, fostering trust.


  • Co-creation workshops
  • Explainable AI
  • Natural-language, graphical, and interactive interfaces
  • Design framework for human-centric AI methods
  • Evaluation and assessment framework for human-centric AI systems
  • Human-centric AI methods for sequential decision-making

Co-creation workshops

The Bigger Picture

The co-creation workshops in PEER aim to involve users and stakeholders in defining human-AI collaborations at the outset of AI development. This process allows participants to identify social, ethical, and technical requirements to enhance interactions between end-users and AI systems.

Tue, CU, JU, AI Lab

Human-centric AI methods for sequential decision-making

Imagine AI that doesn't just answer your questions, but chats with you about the best answer. That's what PEER's human-centered AI is all about: making joint decisions with you, especially for complicated ones.


Evaluation and assessment framework for human-centric AI systems

Building Trustworthy AI: Putting the User in Focus

AI technology is rapidly advancing, but ensuring its trustworthiness is crucial. While researchers focus on ethical principles for development and deployment, the human element – trust and acceptance – is often overlooked.

Tue, CU, JU, AI Lab

Explainable AI

Artificial intelligence (AI) has become increasingly prevalent across various domains, from healthcare to retail, finance, and beyond. However, as AI systems play a more significant role in decision-making processes that impact our lives, issues around user trust and acceptance have emerged.

One reason for this is the lack of understanding and transparency in AI decision-making, leading to user mistrust. Additionally, current AI systems often fail to comprehend user needs and preferences, resulting in solutions that are not adequately personalized or tailored to individual situations. As a result, users may feel that AI systems hinder rather than empower them, reducing their autonomy.

Int, EUT

Natural-language, graphical,
and interactive interfaces

Empowering Users in Sequential Decision-Making with Natural Language Processing, Interactive Visualizations, and Adaptive Explanations.

Traditional Explainable AI (XAI) systems often struggle with sequential decision-making tasks, offering rigid options and limited explanations. PEER breaks this mold by integrating a novel Multimodal XAI framework that fosters dynamic user interaction and trust.

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