AI-Facilitated Collective Judgements

• URL: http://arxiv.org/abs/2503.05830v1
• Date: Thu, 06 Mar 2025 00:06:22 GMT
• Title: AI-Facilitated Collective Judgements
• Authors: Manon Revel, Théophile Pénigaud,
• Abstract summary: This article unpacks the design choices behind longstanding and newly proposed computational frameworks aimed at finding common grounds across collective preferences.<n>We explore AI-facilitated collective judgment as a discovery tool for fostering reasonable representations of a collective will, sense-making, and agreement-seeking.<n>At the same time, we caution against dangerously misguided uses, such as enabling binding decisions, fostering gradual disempowerment or post-rationalizing political outcomes.
• Score: 1.3812010983144802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article unpacks the design choices behind longstanding and newly proposed computational frameworks aimed at finding common grounds across collective preferences and examines their potential future impacts, both technically and normatively. It begins by situating AI-assisted preference elicitation within the historical role of opinion polls, emphasizing that preferences are shaped by the decision-making context and are seldom objectively captured. With that caveat in mind, we explore AI-facilitated collective judgment as a discovery tool for fostering reasonable representations of a collective will, sense-making, and agreement-seeking. At the same time, we caution against dangerously misguided uses, such as enabling binding decisions, fostering gradual disempowerment or post-rationalizing political outcomes.

Related papers

• Bridging Prediction and Intervention Problems in Social Systems [71.41519966787386]
  We consider the ways in which we must shift from a prediction-focused paradigm to an interventionist paradigm when considering the impact of automated decision systems (ADS)<n>We argue this requires a new default problem setup for ADS beyond prediction, to instead consider predictions as decision support, final decisions, and outcomes.<n>We highlight how this perspective unifies modern statistical frameworks and other tools to study the design, implementation, and evaluation of ADS systems.
  arXiv  Detail & Related papers  (2025-07-07T17:29:13Z)
• Resource Rational Contractualism Should Guide AI Alignment [69.07915246220985]
  Contractualist alignment proposes grounding decisions in agreements that diverse stakeholders would endorse.<n>We propose Resource-Rationalism: a framework where AI systems approximate the agreements rational parties would form.<n>An RRC-aligned agent would not only operate efficiently, but also be equipped to dynamically adapt to and interpret the ever-changing human social world.
  arXiv  Detail & Related papers  (2025-06-20T18:57:13Z)
• Consensus in Motion: A Case of Dynamic Rationality of Sequential Learning in Probability Aggregation [0.562479170374811]
  We propose a framework for probability aggregation based on propositional probability logic. We show that any consensus-compatible and independent aggregation rule on a non-nested agenda is necessarily linear.
  arXiv  Detail & Related papers  (2025-04-20T14:04:39Z)
• Policy Aggregation [21.21314301021803]
  We consider the challenge of AI value alignment with multiple individuals with different reward functions and optimal policies in an underlying Markov decision process. We formalize this problem as one of policy aggregation, where the goal is to identify a desirable collective policy. Key insight is that social choice methods can be reinterpreted by identifying ordinal preferences with volumes of subsets of the state-action occupancy polytope.
  arXiv  Detail & Related papers  (2024-11-06T04:19:50Z)
• Diverging Preferences: When do Annotators Disagree and do Models Know? [92.24651142187989]
  We develop a taxonomy of disagreement sources spanning 10 categories across four high-level classes. We find that the majority of disagreements are in opposition with standard reward modeling approaches. We develop methods for identifying diverging preferences to mitigate their influence on evaluation and training.
  arXiv  Detail & Related papers  (2024-10-18T17:32:22Z)
• Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysis [48.14390493099495]
  How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance.<n>This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos.
  arXiv  Detail & Related papers  (2024-09-15T03:17:38Z)
• EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders [5.216732191267959]
  We propose a new framework, EARN Fairness, which facilitates collective metric decisions among stakeholders without requiring AI expertise.<n>The framework features an adaptable interactive system and a stakeholder-centered EARN Fairness process to explain fairness metrics, Ask stakeholders' personal metric preferences, Review metrics collectively, and Negotiate a consensus on metric selection.<n>Our work shows that the EARN Fairness framework enables stakeholders to express personal preferences and reach consensus, providing practical guidance for implementing human-centered AI fairness in high-risk contexts.
  arXiv  Detail & Related papers  (2024-07-16T07:20:30Z)
• Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning [73.77288647011295]
  This paper introduces BI-Directional DEliberation Reasoning (BIDDER) to enhance the decision rationality of language models. Our approach involves three key processes:. Inferring hidden states to represent uncertain information in the decision-making process from historical data;. Using hidden states to predict future potential states and potential outcomes;. Integrating historical information (past contexts) and long-term outcomes (future contexts) to inform reasoning.
  arXiv  Detail & Related papers  (2024-07-08T16:48:48Z)
• ABI Approach: Automatic Bias Identification in Decision-Making Under Risk based in an Ontology of Behavioral Economics [46.57327530703435]
  Risk seeking preferences for losses, driven by biases such as loss aversion, pose challenges and can result in severe negative consequences. This research introduces the ABI approach, a novel solution designed to support organizational decision-makers by automatically identifying and explaining risk seeking preferences.
  arXiv  Detail & Related papers  (2024-05-22T23:53:46Z)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems [3.7846812749505134]
  We envision a hybrid participatory system where participants make choices and provide motivations for those choices.<n>We focus on situations where a conflict is detected between participants' choices and motivations.<n>We propose methods for estimating value preferences while addressing detected inconsistencies by interacting with the participants.
  arXiv  Detail & Related papers  (2024-02-26T17:16:28Z)
• Bridging the gap: Towards an Expanded Toolkit for AI-driven Decision-Making in the Public Sector [6.693502127460251]
  AI-driven decision-making systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. These systems face the challenge of aligning machine learning (ML) models with the complex realities of public sector decision-making. We examine five key challenges where misalignment can occur, including distribution shifts, label bias, the influence of past decision-making on the data side, as well as competing objectives and human-in-the-loop on the model output side.
  arXiv  Detail & Related papers  (2023-10-29T17:44:48Z)
• Online Decision Mediation [72.80902932543474]
  Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior. In clinical diagnosis, fully-autonomous machine behavior is often beyond ethical affordances.
  arXiv  Detail & Related papers  (2023-10-28T05:59:43Z)
• On the Complexity of Adversarial Decision Making [101.14158787665252]
  We show that the Decision-Estimation Coefficient is necessary and sufficient to obtain low regret for adversarial decision making. We provide new structural results that connect the Decision-Estimation Coefficient to variants of other well-known complexity measures.
  arXiv  Detail & Related papers  (2022-06-27T06:20:37Z)
• Inverse Active Sensing: Modeling and Understanding Timely Decision-Making [111.07204912245841]
  We develop a framework for the general setting of evidence-based decision-making under endogenous, context-dependent time pressure. We demonstrate how it enables modeling intuitive notions of surprise, suspense, and optimality in decision strategies.
  arXiv  Detail & Related papers  (2020-06-25T02:30:45Z)
• Contestable Black Boxes [10.552465253379134]
  This paper investigates the type of assurances that are needed in the contesting process when algorithmic black-boxes are involved. We argue that specialised complementary methodologies to evaluate automated decision-making in the case of a particular decision being contested need to be developed.
  arXiv  Detail & Related papers  (2020-06-09T09:09:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.