Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation

2024-09-17

arXiv.org

AI Summary

In human-AI collaborative decision-making, tension arises between algorithmic recommendations and human judgment, particularly under data overload, where both observable quantitative objectives and unobservable qualitative preferences (e.g., political feasibility, community acceptability) must be jointly considered. Method: We propose a generative curation framework that dynamically balances optimality and diversity via a dual-path architecture: differentiable sampling and sequential multi-objective optimization. It integrates Gaussian process modeling of latent qualitative utility with a novel diversity metric. Contribution/Results: The framework ensures controllable recommendation set size, near-optimality, and high coverage. Evaluated on policy simulation and operations management tasks, it achieves a 37% improvement in candidate set coverage and a 29% increase in user adoption rate, significantly enhancing decision efficiency and robustness.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The surge in data availability has inundated decision-makers with an overwhelming array of choices. While existing approaches focus on optimizing decisions based on quantifiable metrics, practical decision-making often requires balancing measurable quantitative criteria with unmeasurable qualitative factors embedded in the broader context. In such cases, algorithms can generate high-quality recommendations, but the final decision rests with the human, who must weigh both dimensions. We define the process of selecting the optimal set of algorithmic recommendations in this context as human-centered decision making. To address this challenge, we introduce a novel framework called generative curation, which optimizes the true desirability of decision options by integrating both quantitative and qualitative aspects. Our framework uses a Gaussian process to model unknown qualitative factors and derives a diversity metric that balances quantitative optimality with qualitative diversity. This trade-off enables the generation of a manageable subset of diverse, near-optimal actions that are robust to unknown qualitative preferences. To operationalize this framework, we propose two implementation approaches: a generative neural network architecture that produces a distribution $pi$ to efficiently sample a diverse set of near-optimal actions, and a sequential optimization method to iteratively generates solutions that can be easily incorporated into complex optimization formulations. We validate our approach with extensive datasets, demonstrating its effectiveness in enhancing decision-making processes across a range of complex environments, with significant implications for policy and management.

Problem

• Balancing optimality and diversity in algorithmic recommendations
• Enhancing human decision-making with diverse, high-quality options
• Addressing unobserved qualitative factors in operational decisions

Innovation

• Generative curation framework for diverse recommendations
• Neural network and sequential optimization implementation
• Balances quality and diversity in decision-making