应用特定领域道德规范的算法问责框架:缅因州湾贝类毒性生态系统预测的案例研究

Isabella Grasso, David Russell, Abigail V. Matthews, Jeanna Neefe Matthews
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引用次数: 4

摘要

生态预报用于为可能对个人生活和生态系统健康产生重大影响的决策提供信息。这些预测或模型体现了它们的创造者的道德规范,以及在此过程中做出的许多看似武断的实现选择。它们可能包含实现错误,也反映了从过去有偏见的决策中获取数据集时学到的偏见模式。算法问责制的原则和框架允许广泛的利益相关者将模型和软件系统的结果置于上下文中。我们展示了算法问责框架和特定领域道德规范的结合如何帮助回应维护公平和人类价值观的呼吁,特别是在利用机器学习算法的领域。这有助于避免因部署“黑盒”系统来解决复杂问题而导致的许多意想不到的后果。在本文中,我们讨论了我们将算法问责原则和框架应用于生态系统预测的经验,重点是预测缅因湾贝类毒性的案例研究。我们调整了现有的框架,如数据集的数据表和模型报告的模型卡,从它们最初关注个人可识别的私人数据到包括公共数据集,例如那些经常用于生态系统预测应用程序的数据,以审计案例研究。我们展示了高层次的算法问责框架和领域层面的道德准则是如何相互补充的,从而在自动化决策系统中激励更多的透明度、问责制和公平性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Algorithmic Accountability Frameworks with Domain-specific Codes of Ethics: A Case Study in Ecosystem Forecasting for Shellfish Toxicity in the Gulf of Maine
Ecological forecasts are used to inform decisions that can havesignificant impacts on the lives of individuals and on the healthof ecosystems. These forecasts, or models, embody the ethics oftheir creators as well as many seemingly arbitrary implementationchoices made along the way. They can contain implementationerrors as well as reflect patterns of bias learned when ingestingdatasets derived from past biased decision making. Principles andframeworks for algorithmic accountability allow a wide range ofstakeholders to place the results of models and software systemsinto context. We demonstrate how the combination of algorithmicaccountability frameworks and domain-specific codes of ethics helpanswer calls to uphold fairness and human values, specifically indomains that utilize machine learning algorithms. This helps avoidmany of the unintended consequences that can result from deploy-ing "black box" systems to solve complex problems. In this paper,we discuss our experience applying algorithmic accountability prin-ciples and frameworks to ecosystem forecasting, focusing on a casestudy forecasting shellfish toxicity in the Gulf of Maine. We adaptexisting frameworks such as Datasheets for Datasets and ModelCards for Model Reporting from their original focus on personallyidentifiable private data to include public datasets, such as thoseoften used in ecosystem forecasting applications, to audit the casestudy. We show how high level algorithmic accountability frame-works and domain level codes of ethics compliment each other,incentivizing more transparency, accountability, and fairness inautomated decision-making systems.
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