CAREER: Causal Modeling for Data Quality and Bias Mitigation

职业：数据质量和偏差缓解的因果建模

• 批准号: 2340124
• 负责人: Babak Salimi
• 金额: $ 60万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-07-01 至 2029-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340124&HistoricalAwards=false
• 关键词: CAREER; Causal; Modeling; Data; Quality

This project presents a novel approach, inspired by database methodologies, to address the significant challenge of bias in algorithmic systems, particularly in sensitive domains such as credit scoring, medical diagnostics, predictive policing, and the criminal justice system. By recognizing that such biases often stem from the underlying data, the initiative redefines algorithmic bias as a data quality management issue. Emphasizing critical aspects of data quality management such as accuracy, completeness, and consistency, the project aims to develop methods that significantly enhance the trustworthiness and societal impact of these systems. Incorporating causal modeling with these essential data quality principles, it takes a strategic approach to identifying and addressing the root causes of algorithmic bias. This effort not only marks a significant advancement in the field of data science but also contributes substantially to national and public welfare by advocating for decision-making processes that are fair, accurate, and reliable, thereby promoting national health, prosperity, and well-being in a comprehensive manner. This plan envisions a wide-ranging dissemination of its motivation, approach, and artifacts through a diverse array of interdisciplinary colloquia, seminars, and co-curricular learning opportunities. This project addresses algorithmic bias through a fourfold approach: 1) Developing new, scalable algorithms for data repair, designed for repairing data concerning a special class of integrity constraints that can capture the statistical nuances of data used for training machine learning (ML) models. 2) Establishing a holistic data debiasing framework capable of addressing various data biases and quality issues. 3) Implementing methods to quantify uncertainty in algorithmic decision-making, particularly based on ML models, where the uncertainty stems from bias and data quality issues that cannot be fully recovered and removed due to incomplete information. 4) Lastly, the project focuses on developing methods for root-cause analysis to identify underlying issues and adaptive debiasing in dynamic data environments, incorporating proactive interventions in data processing pipelines for ongoing bias mitigation. This multifaceted strategy aims to advance the fields of data quality management, data cleaning for ML, and responsible data science, significantly enhancing the reliability, fairness, and accuracy of data-driven decision-making systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目提出了一种受数据库方法的启发的新颖方法，以解决算法系统中偏见的重大挑战，尤其是在敏感领域，例如信用评分，医学诊断，预测性警务和刑事司法系统。 通过认识到这种偏见通常源于基本数据，该计划将算法偏见重新定义为数据质量管理问题。该项目强调数据质量管理的关键方面，例如准确性，完整性和一致性，旨在开发可显着增强这些系统的可信赖性和社会影响的方法。将因果建模与这些基本数据质量原则结合在一起，它采用战略方法来识别和解决算法偏见的根本原因。这项努力不仅标志着数据科学领域的重大进步，而且还通过主张公平，准确和可靠的决策过程来为国家和公共福利做出了重大贡献，从而以全面的方式促进了国家健康，繁荣和福祉。 该计划设想通过各种各样的跨学科座谈会，研讨会和课外学习机会广泛地传播其动机，方法和人工制品。该项目通过四倍的方法解决算法偏差：1）开发用于数据修复的新的可扩展算法，该算法旨在修复有关特殊类别的完整性约束的数据，这些数据可以捕获用于培训机器学习（ML）模型的数据的统计细微差别。 2）建立一个能够解决各种数据偏见和质量问题的整体数据偏见框架。 3）实施方法来量化算法决策中的不确定性，尤其是基于ML模型，其中不确定性源于由于不完整的信息而无法完全恢复和删除的偏见和数据质量问题。 4）最后，该项目着重于开发根本原因分析的方法，以识别动态数据环境中的潜在问题和自适应性词，从而将积极的干预措施纳入了数据处理管道中，以进行持续的偏见缓解。这种多方面的策略旨在推进数据质量管理，ML数据清理以及负责任的数据科学领域，从而显着提高了数据驱动的决策系统的可靠性，公平性和准确性。该奖项反映了NSF的法定任务，并通过该基金会的智力优点和广泛的影响来审查NSF的法定任务，并被认为是值得的。