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Ensure Ethical AI & Debiasing

本课程是 AI Techniques, Causal Inference & Business Optimization 专项课程的一部分

位教师：Hurix Digital

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Measurable AI Fairness: Fairness can be measured using metrics like demographic parity to objectively assess bias across protected groups.
Evidence-Based Bias Mitigation:Comparing mitigation methods with quantitative metrics ensures bias intervention are chosen by impact,not assumptions.
Data-Level Bias Correction: Fixing representation issues through resampling builds more stable, fair, and reliable AI models.
Transparent Ethical Trade-offs: Ethical AI requires clear communication of fairness–performance trade-offs to support informed stakeholder decisions.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 AI Techniques, Causal Inference & Business Optimization 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

Bias in AI systems can undermine trust and create serious ethical and legal risks for organizations. This Short Course was created to help data analysis professionals accomplish comprehensive bias detection and mitigation in AI-driven decision systems. By completing this course, you'll be able to apply formal fairness metrics, implement proven mitigation techniques, and confidently communicate ethical trade-offs to stakeholders.

By the end of this course, you will be able to: Apply fairness metrics to HR selection models and document disparities Evaluate and implement bias mitigation approaches with measurable improvements Analyze datasets for representation bias and apply re-sampling techniques Evaluate accuracy-fairness trade-offs and communicate findings to stakeholders This course is unique because it combines hands-on technical implementation with strategic stakeholder communication skills. To be successful in this course, you should have a background in Python programming and basic machine learning concepts.

单元详情

Apply fairness metrics to HR selection models and document observed disparities.

涵盖的内容

1个视频1篇阅读材料1个作业1个非评分实验室

Evaluate mitigation approaches and implement bias reduction strategies with measurable improvements.

涵盖的内容

2个视频2个作业

This module teaches how to detect representation bias in datasets, apply re-sampling strategies such as SMOTE, and assess their impact on model performance across demographic groups.

涵盖的内容

1个视频1篇阅读材料1个作业

1个视频总计5分钟

Implementing SMOTE Re-sampling for Bias Reduction - Apply SMOTE and compare original vs re-sampled model performance using classification report.5分钟

1篇阅读材料总计6分钟

Podcast: Dataset Representation Bias: Detection and Impact Analysis - Focus on representation bias patterns and their impact. Use freely available datasets. 6分钟

1个作业总计8分钟

Dataset Bias Analysis and Re-sampling Assessment8分钟

Learners will evaluate the impact of bias mitigation techniques on AI system performance and fairness, then communicate results clearly to stakeholders for informed decision making.