From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems

• URL: http://arxiv.org/abs/2412.04655v2
• Date: Thu, 02 Jan 2025 17:21:22 GMT
• Title: From Models to Systems: A Comprehensive Fairness Framework for Compositional Recommender Systems
• Authors: Brian Hsu, Cyrus DiCiccio, Natesh Sivasubramoniapillai, Hongseok Namkoong,
• Abstract summary: Real-world recommendation systems are built on multiple models and even multiple stages. We propose a holistic framework for modeling system-level fairness. We empirically demonstrate the effectiveness of our proposed framework on synthetic and real datasets.
• Score: 6.295527354699332
• License:
• Abstract: Fairness research in machine learning often centers on ensuring equitable performance of individual models. However, real-world recommendation systems are built on multiple models and even multiple stages, from candidate retrieval to scoring and serving, which raises challenges for responsible development and deployment. This system-level view, as highlighted by regulations like the EU AI Act, necessitates moving beyond auditing individual models as independent entities. We propose a holistic framework for modeling system-level fairness, focusing on the end-utility delivered to diverse user groups, and consider interactions between components such as retrieval and scoring models. We provide formal insights on the limitations of focusing solely on model-level fairness and highlight the need for alternative tools that account for heterogeneity in user preferences. To mitigate system-level disparities, we adapt closed-box optimization tools (e.g., BayesOpt) to jointly optimize utility and equity. We empirically demonstrate the effectiveness of our proposed framework on synthetic and real datasets, underscoring the need for a system-level framework.

Related papers

• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• A Survey on Inference Optimization Techniques for Mixture of Experts Models [50.40325411764262]
  Large-scale Mixture of Experts (MoE) models offer enhanced model capacity and computational efficiency through conditional computation. deploying and running inference on these models presents significant challenges in computational resources, latency, and energy efficiency. This survey analyzes optimization techniques for MoE models across the entire system stack.
  arXiv  Detail & Related papers  (2024-12-18T14:11:15Z)
• LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations [51.76373105981212]
  Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. We introduce a comprehensive reranking framework, designed to seamlessly integrate various reranking criteria. A customizable input mechanism is also integrated, enabling the tuning of the language model's focus to meet specific reranking needs.
  arXiv  Detail & Related papers  (2024-06-18T09:29:18Z)
• Streamlined Framework for Agile Forecasting Model Development towards Efficient Inventory Management [2.0625936401496237]
  This paper proposes a framework for developing forecasting models by streamlining the connections between core components of the developmental process. The proposed framework enables swift and robust integration of new datasets, experimentation on different algorithms, and selection of the best models.
  arXiv  Detail & Related papers  (2023-04-13T08:52:32Z)
• Learning Optimal Fair Scoring Systems for Multi-Class Classification [0.0]
  There are growing concerns about Machine Learning models with respect to their lack of interpretability and the undesirable biases they can generate or reproduce. In this paper, we use Mixed-Integer Linear Programming (MILP) techniques to produce inherently interpretable scoring systems under sparsity and fairness constraints.
  arXiv  Detail & Related papers  (2023-04-11T07:18:04Z)
• Switchable Representation Learning Framework with Self-compatibility [50.48336074436792]
  We propose a Switchable representation learning Framework with Self-Compatibility (SFSC) SFSC generates a series of compatible sub-models with different capacities through one training process. SFSC achieves state-of-the-art performance on the evaluated datasets.
  arXiv  Detail & Related papers  (2022-06-16T16:46:32Z)
• Slimmable Domain Adaptation [112.19652651687402]
  We introduce a simple framework, Slimmable Domain Adaptation, to improve cross-domain generalization with a weight-sharing model bank. Our framework surpasses other competing approaches by a very large margin on multiple benchmarks.
  arXiv  Detail & Related papers  (2022-06-14T06:28:04Z)
• Towards a Fairness-Aware Scoring System for Algorithmic Decision-Making [35.21763166288736]
  We propose a general framework to create data-driven fairness-aware scoring systems. We show that the proposed framework provides practitioners or policymakers great flexibility to select their desired fairness requirements.
  arXiv  Detail & Related papers  (2021-09-21T09:46:35Z)
• Characterizing Fairness Over the Set of Good Models Under Selective Labels [69.64662540443162]
  We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance. We provide tractable algorithms to compute the range of attainable group-level predictive disparities. We extend our framework to address the empirically relevant challenge of selectively labelled data.
  arXiv  Detail & Related papers  (2021-01-02T02:11:37Z)
• Optimal by Design: Model-Driven Synthesis of Adaptation Strategies for Autonomous Systems [9.099295007630484]
  We present Optimal by Design (ObD), a framework for model-based requirements-driven synthesis of optimal adaptation strategies for autonomous systems. ObD proposes a model for the high-level description of the basic elements of self-adaptive systems, namely the system, capabilities, requirements and environment. Based on those elements, a Markov Decision Process (MDP) is constructed to compute the optimal strategy or the most rewarding system behaviour.
  arXiv  Detail & Related papers  (2020-01-16T12:49:55Z)

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.