ADDRESSING ALGORITHMIC BIAS IN HEALTHCARE AI
Course 3306
2 Hours
$99
Algorithmic bias in healthcare AI isn’t just a technical issue—it’s a matter of equity and patient trust. This course equips healthcare leaders and AI developers with the tools to recognize, mitigate, and prevent bias in AI systems, ensuring fair and ethical outcomes for all.
Target Audience
- Healthcare administrators and clinicians
- Data scientists and AI developers in healthcare
- Compliance officers
- IT and operations leaders
Course Format:
Live Virtual Lectures
Interactive Activities:
- Hands-on workshops, case studies, and scenario-based exercises.
Resources and Materials:
- Bias evaluation templates.
- Sample frameworks for equitable AI.
- Guidelines for curating diverse datasets.
Certificate of Completion
Payment Information: We accept credit card payments.
Course Objectives
- Understand what algorithmic bias is and its implications for healthcare outcomes.
- Learn how bias is introduced into AI systems and how to identify it.
- Gain tools and strategies to mitigate bias in AI development and deployment.
- Build a framework for ensuring fairness and equity in AI-powered healthcare solutions.
Course Modules Overview:
Understanding Algorithmic Bias in Healthcare AI
- Definition and types of algorithmic bias (data, societal, and systemic).
- Why addressing bias is critical for patient safety and trust.
Types of Bias in AI
- Data bias, algorithmic bias, and systemic bias
- How different biases manifest in AI systems
Identifying Bias in Data
- Techniques for detecting bias in datasets
- Case studies on biased data and its consequences
Mitigating Bias in Algorithms
- Methods for reducing bias in machine learning models
- Fairness-aware algorithms and techniques
Building a Framework for Fair and Equitable AI
- Steps to create policies that ensure fairness in AI tools.
- Cross-functional collaboration for identifying and addressing bias.
- Continuous monitoring and updating of AI systems to maintain equity.
Group Rates Available!
Bring your team and save – perfect for healthcare organizations looking to train multiple leaders.
Discounts available for organizations enrolling 5+ participants.
Class Schedule
Rates below are for each participant.
January 30, 2025
4:00 PM – 6:00 PM EST
Algorithmic Bias in Healthcare AI Virtual, EST
$99
Class ID: 3306
February 20, 2025
12:00 PM – 2:00 PM EST
Algorithmic Bias in Healthcare AI Virtual, EST
$99
Class ID: 3306
March 27, 2025
4:00 PM – 6:00 PM EST
Algorithmic Bias in Healthcare AI Virtual, EST
$99
Class ID: 3306
Frequently Asked Questions
What is algorithmic bias, and why is it important in healthcare AI?
Algorithmic bias occurs when AI systems produce unfair or inaccurate results due to flaws in data, algorithms, or societal biases. In healthcare, this can lead to unequal treatment, misdiagnoses, or missed opportunities for care, disproportionately affecting certain populations.
How does this course help healthcare organizations address bias?
The course provides practical tools for identifying, mitigating, and preventing bias in AI systems. You’ll learn how to evaluate datasets, adjust algorithms, and build frameworks to ensure equitable outcomes in your organization.
Is this course suitable for non-technical professionals?
Yes! This course is designed for both technical and non-technical professionals, focusing on the strategic, ethical, and operational aspects of addressing algorithmic bias, rather than requiring coding expertise.
What types of biases are covered in the course?
The course addresses:
- Data Bias: Gaps or imbalances in the data used to train AI systems.
- Algorithmic Bias: Errors in how algorithms are designed or deployed.
- Societal Bias: Systemic issues that influence AI outputs.
How can I ensure my organization’s datasets are representative?
We provide guidelines and tools to audit datasets for diversity and inclusivity, ensuring they accurately reflect the populations served by your organization.
Can bias be completely eliminated from AI systems?
While it’s challenging to eliminate bias entirely, this course focuses on minimizing and mitigating its impact through best practices in data management, algorithm design, and human oversight.
Does the course cover legal and ethical implications of bias?
Yes, the course explores regulatory considerations like HIPAA and GDPR, as well as the ethical responsibility to ensure fairness and transparency in AI-driven decisions.
What tools will I learn to use for detecting bias?
The course introduces bias detection tools and methodologies, such as fairness metrics, model explainability tools, and practical auditing frameworks.