# Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making

arXiv: https://arxiv.org/abs/2207.09525 · HTML: https://mlsanity.com/paper/2207.09525/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2022.

## Built on by (most cited first, 9 of 9)

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention](https://mlsanity.com/paper/2405.01820/) (2024)
- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles](https://mlsanity.com/paper/2308.04346/) (2023)
- [Going public: the role of public participation approaches in commercial AI labs](https://mlsanity.com/paper/2306.09871/) (2023)
- [Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts](https://mlsanity.com/paper/2407.17374/) (2024)
- [Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation](https://mlsanity.com/paper/2405.09679/) (2024)
- [Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making](https://mlsanity.com/paper/2502.14869/) (2025)
- [Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement](https://mlsanity.com/paper/2509.02624/) (2025)

## Abstract (arXiv)

With the increasing pervasiveness of algorithms across industry and government, a growing body of work has grappled with how to understand their societal impact and ethical implications. Various methods have been used at different stages of algorithm development to encourage researchers and designers to consider the potential societal impact of their research. An understudied yet promising area in this realm is using participatory foresight to anticipate these different societal impacts. We employ crowdsourcing as a means of participatory foresight to uncover four different types of impact areas based on a set of governmental algorithmic decision making tools: (1) perceived valence, (2) societal domains, (3) specific abstract impact types, and (4) ethical algorithm concerns. Our findings suggest that this method is effective at leveraging the cognitive diversity of the crowd to uncover a range of issues. We further analyze the complexities within the interaction of the impact areas identified to demonstrate how crowdsourcing can illuminate patterns around the connections between impacts. Ultimately this work establishes crowdsourcing as an effective means of anticipating algorithmic impact which complements other approaches towards assessing algorithms in society by leveraging participatory foresight and cognitive diversity.