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Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits

Jimin Mun, Liwei Jiang, Jenny Liang, Inyoung Cheong, Nicole DeCario, Yejin Choi, Tadayoshi Kohno, Maarten Sap

TL;DR

The paper presents PARTICIP-AI, a four-step framework that empowers laypeople to imagine current and near/far-future AI use cases, evaluate harms and benefits under development and not-developing scenarios, and render a development decision to inform democratic AI governance. It combines speculative design with a mixed-methods survey (n=295 US participants) using both open-ended qualitative coding and GPT-4-assisted closed coding, supplemented by quantitative analyses. Key findings show a public prioritizing personal-life and societal-application use cases, recognition of harms beyond expert taxonomies (notably distrust and mental-health concerns), and a strong influence of perceived harms/benefits of not developing on development judgments. The study demonstrates both the value and limitations of public input for AI governance, highlighting tensions between human-centric benefits and techno-solutionist risks, and calls for broader, more inclusive participatory approaches to guide responsible AI development and policy.

Abstract

General purpose AI, such as ChatGPT, seems to have lowered the barriers for the public to use AI and harness its power. However, the governance and development of AI still remain in the hands of a few, and the pace of development is accelerating without a comprehensive assessment of risks. As a first step towards democratic risk assessment and design of general purpose AI, we introduce PARTICIP-AI, a carefully designed framework for laypeople to speculate and assess AI use cases and their impacts. Our framework allows us to study more nuanced and detailed public opinions on AI through collecting use cases, surfacing diverse harms through risk assessment under alternate scenarios (i.e., developing and not developing a use case), and illuminating tensions over AI development through making a concluding choice on its development. To showcase the promise of our framework towards informing democratic AI development, we run a medium-scale study with inputs from 295 demographically diverse participants. Our analyses show that participants' responses emphasize applications for personal life and society, contrasting with most current AI development's business focus. We also surface diverse set of envisioned harms such as distrust in AI and institutions, complementary to those defined by experts. Furthermore, we found that perceived impact of not developing use cases significantly predicted participants' judgements of whether AI use cases should be developed, and highlighted lay users' concerns of techno-solutionism. We conclude with a discussion on how frameworks like PARTICIP-AI can further guide democratic AI development and governance.

Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits

TL;DR

The paper presents PARTICIP-AI, a four-step framework that empowers laypeople to imagine current and near/far-future AI use cases, evaluate harms and benefits under development and not-developing scenarios, and render a development decision to inform democratic AI governance. It combines speculative design with a mixed-methods survey (n=295 US participants) using both open-ended qualitative coding and GPT-4-assisted closed coding, supplemented by quantitative analyses. Key findings show a public prioritizing personal-life and societal-application use cases, recognition of harms beyond expert taxonomies (notably distrust and mental-health concerns), and a strong influence of perceived harms/benefits of not developing on development judgments. The study demonstrates both the value and limitations of public input for AI governance, highlighting tensions between human-centric benefits and techno-solutionist risks, and calls for broader, more inclusive participatory approaches to guide responsible AI development and policy.

Abstract

General purpose AI, such as ChatGPT, seems to have lowered the barriers for the public to use AI and harness its power. However, the governance and development of AI still remain in the hands of a few, and the pace of development is accelerating without a comprehensive assessment of risks. As a first step towards democratic risk assessment and design of general purpose AI, we introduce PARTICIP-AI, a carefully designed framework for laypeople to speculate and assess AI use cases and their impacts. Our framework allows us to study more nuanced and detailed public opinions on AI through collecting use cases, surfacing diverse harms through risk assessment under alternate scenarios (i.e., developing and not developing a use case), and illuminating tensions over AI development through making a concluding choice on its development. To showcase the promise of our framework towards informing democratic AI development, we run a medium-scale study with inputs from 295 demographically diverse participants. Our analyses show that participants' responses emphasize applications for personal life and society, contrasting with most current AI development's business focus. We also surface diverse set of envisioned harms such as distrust in AI and institutions, complementary to those defined by experts. Furthermore, we found that perceived impact of not developing use cases significantly predicted participants' judgements of whether AI use cases should be developed, and highlighted lay users' concerns of techno-solutionism. We conclude with a discussion on how frameworks like PARTICIP-AI can further guide democratic AI development and governance.
Paper Structure (53 sections, 4 figures, 16 tables)

This paper contains 53 sections, 4 figures, 16 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methods
  4. Particip-AI Framework
  5. Overview
  6. Question Design
  7. Input Collection and Analysis Methodology
  8. Participant Recruitment
  9. Qualitative Analysis
  10. Open Coding with Human Annotators
  11. Closed Coding with GPT-4 Augmentation
  12. Quantitative Analysis.
  13. Results
  14. RQ1. Current and Future Use Cases of AI
  15. Current and Future AI Use Cases
  16. ...and 38 more sections

Figures (4)

  • Figure 1: Distribution of the harms and benefits scale by use case theme and domain sorted in order of decreasing absolute mean difference of benefit and harm. White mark indicates median, and black box within indicates quartile.
  • Figure 2: Distribution of top few codes mentioned in groups impacted the most by the use case (Q9, Q12, Q15).
  • Figure 3: not developing use case scale
  • Figure 4: Distribution of top few codes mentioned in groups impacted the most by not developing (Q18, Q21).