Joshua C. Yang

Hello 👋🏽 I am Josh, a postdoc researcher at ETH Zurich 🇨🇭 investigating how AI can be designed to serve society, and democracy in particular (yes, a pro-active take). I finished my PhD in summer 2025 and decided this mission was far too interesting to stop.

Coming from Computer Science, I approach my research through the lens of responsible and societal AI, combining insights from Complex Systems, Computational Social Choice, and Human-Computer Interaction. My research moves in two main directions. The first focuses on Digital Democracy, studying how methods such as agentic AI, LLMs, voting algorithms and recommender systems can support how people vote, deliberate, and make collective decisions — including how AI agents can be appropriately used for social simulation to model and study collective behaviour. The second explores AI Pluralistic Alignment, developing ways for AI systems to understand and represent diverse human values rather than converging on a single viewpoint.

I try to bring my research to the real world as much as possible. I frequently advise governments and organisations on their digital participatory processes. In Switzerland, I have worked closely with the city of Aarau on the StadtIdee Participatory Budgeting program and also Kultur Komitee Winterthur in their annual citizen assembly to fund art and cultural projects.

Outside of work, I am an active member in the Taiwanese 🇹🇼 civic tech g0v community, participating mainly in the vTaiwan project, using AI & digital tools to find consensus among the public in multistakeholder governance. We are constantly exploring new tools and methods that enhance deliberation and consensus finding.

During my Computer Science (NLP) master’s at the University of Melbourne 🇦🇺, I conducted fieldwork in the Northern Territory, focusing on Speech Recognition for Indigenous languages. My research explored a human-in-the-loop transcription pipeline that combined respeaking and word-spotting to improve recognition for endangered Indigenous languages.

I was born in Australia and grew up in Taiwan. I speak Mandarin with a Taiwanese accent, English with an Australian one, and a few words of Swiss German reserved exclusively for SBB train inspectors.

I’m based in Zürich and would love to connect if you find yourself in this part of the world! 🥾

news

Mar 10, 2026	We just ran another round of Kultur Komitee in Winterthur! This year, we followed a multi-step Method of Equal Shares (MES) process that focused more on helping participants learn about the projects in smaller subsets.
Mar 01, 2026	We have a first-authored paper appearing at the MemAgent Workshop at ICLR! We incorporated Bayesian updating into the memory structure of AI agents for social simulation, leading to more stable and realistic opinion shifts.
Feb 01, 2026	Our first-authored poster paper on our Kultur Komitee fieldwork got accepted to CHI Barcelona! We tested the idea of a “Priceable” method to support both voting and deliberation, along with Voting Receipts. See you there in April!
Oct 01, 2025	I became a postdoc in the Computational Social Science team at ETH Zurich! (Feeling old now) 🥸
Jul 01, 2025	Our study on how AI models vote compared to humans was covered by the Swiss public broadcaster RTS (Radio Télévision Suisse)! The coverage highlights our findings that AI models show more uniform behavior and conform to dominant discourses (WEIRD bias) compared to diverse human preferences.

latest posts

Jun 30, 2025	Living Democracy from Australia, Taiwan to Switzerland

selected publications

AIES
LLM Voting: Human Choices and AI Collective Decision Making

Joshua C Yang, Marcin Korecki, Damian Dailisan, Carina I Hausladen, and Dirk Helbing

In 7th AAAI Conference on AI, Ethics, and Society , 2024

Abs Bib

This paper explores how Large Language Models (LLMs) can participate in and support human collective decision making. Through experiments comparing human and LLM voting behaviors, the work investigates alignment, bias, and interpretability in AI-assisted democratic processes.
@inproceedings{yang2024llm, title = {LLM Voting: Human Choices and AI Collective Decision Making}, author = {Yang, Joshua C and Korecki, Marcin and Dailisan, Damian and Hausladen, Carina I and Helbing, Dirk}, booktitle = {7th AAAI Conference on AI, Ethics, and Society}, year = {2024}, }

ACM

Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting

Joshua C Yang, Carina I Hausladen, Dominik Peters, Evangelos Pournaras, Regula Hänggli Fricker, and Dirk Helbing

Digital Government: Research and Practice, 2024

Abs Bib

@article{yang2024designing,
  title = {Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting},
  author = {Yang, Joshua C and Hausladen, Carina I and Peters, Dominik and Pournaras, Evangelos and H{\"a}nggli Fricker, Regula and Helbing, Dirk},
  journal = {Digital Government: Research and Practice},
  volume = {5},
  number = {3},
  pages = {1--30},
  year = {2024},
  publisher = {ACM},
  url = {https://dl.acm.org/doi/10.1145/3665332},
}

ACM
Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee

Joshua C Yang, and Fynn Bachmann

In ACM Conference on Fairness, Accountability, and Transparency (FAccT) , 2025

Abs Bib

Based on field experiments in Taiwan and Switzerland, this paper presents hybrid algorithmic designs that integrate deliberation and voting for digital democracy.
@inproceedings{yang2025bridging, title = {Bridging Voting and Deliberation with Algorithms: Field Insights from vTaiwan and Kultur Komitee}, author = {Yang, Joshua C and Bachmann, Fynn}, booktitle = {ACM Conference on Fairness, Accountability, and Transparency (FAccT)}, year = {2025}, url = {https://arxiv.org/abs/2502.05017}, }
arXiv
Komitee Equal Shares: A Hybrid Algorithm for Priceable Participatory Funding

Joshua C Yang, Noemi Scheurer, and Dirk Helbing

2025

Abs Bib

Public funding processes demand fairness, learning, and outcomes that participants can understand. We introduce \emphKomitee Equal Shares, a priceable virtual-budget allocation framework that integrates two signals: in \textitvoter mode, participants cast point votes; in \textitevaluator mode, small groups assess proposals against collectively defined impact fields. The framework extends the Method of Equal Shares by translating both signals into virtual spending power and producing explicit “voting receipts.” We deployed the framework in the 2025 Kultur Komitee in Winterthur, Switzerland (N=38), allocating CHF 380,000. Our results show that this hybrid approach generates a more diverse portfolio than simple aggregation and that participants value the fair process over individual control. The framework offers a model where citizens act as both voters and evaluators within a single, proportional, and explainable allocation.
@misc{yang2025komitee, title = {Komitee Equal Shares: A Hybrid Algorithm for Priceable Participatory Funding}, author = {Yang, Joshua C and Scheurer, Noemi and Helbing, Dirk}, journal = {CHI Extended Abstracts}, url = {https://dl.acm.org/doi/10.1145/3772363.3798958}, year = {2025}, }

Joshua C. Yang

news

latest posts

selected publications

real-world projects

vTaiwan Agentic Citizen Assembly

Kultur Komitee Winterthur

Stadtidee Aarau Participatory Budgeting