GenAI/LLM Fairness Workshop by Tonex
GenAI/LLM Fairness Workshop is a 2-day course where participants learn the concepts of fairness, bias, and ethics in AI as well as recognize sources of bias in generative AI and LLMs.
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The rise of generative AI (GenAI) and large language models (LLMs) has transformed industries, offering innovative ways to automate tasks, generate content, and improve customer interactions.
However, ensuring fairness in these models is critical to prevent biases that can lead to reputational damage, legal risks, and ethical concerns. Thankfully, several cutting-edge technologies can help businesses achieve this balance.
One of those important technologies involve bias detection and mitigation tools. Companies can leverage tools like IBM AI Fairness 360 (AIF360) and Microsoft’s Responsible AI dashboard to identify and address biases in their AI models. These frameworks use algorithms to analyze data inputs and outputs, flagging potential areas of bias. By providing actionable insights, they empower businesses to create more equitable AI systems.
Ensuring fairness also often requires diverse and representative training data. Synthetic data tools, such as Gretel.ai or Hazy, allow businesses to create artificial datasets that fill gaps in real-world data. These technologies enable companies to reduce bias without compromising data privacy or quality.
Then there’s explainability platforms. Explainability tools like LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) help businesses understand how GenAI and LLMs arrive at specific outputs. By identifying decision-making patterns, organizations can pinpoint areas where biases may arise, ensuring transparency in their AI applications.
It’s also essential for companies to understand that AI fairness is not a one-time effort; it requires ongoing evaluation. Platforms such as Fiddler AI and Arize AI provide real-time model monitoring, detecting anomalies and biases in live systems. These technologies alert businesses to potential fairness issues, enabling proactive adjustments.
Some platforms, like Fairlearn, integrate fairness policies directly into AI pipelines. These APIs ensure compliance with pre-set fairness standards, giving businesses a scalable way to maintain ethical AI practices.
GenAI/LLM Fairness Workshop by Tonex
The GenAI/LLM Fairness Workshop by Tonex offers an in-depth exploration of fairness and bias in generative AI and large language models (LLMs). Participants will learn how to identify, measure, and mitigate bias, ensuring ethical and inclusive AI applications. Through interactive modules, case studies, and practical exercises, attendees will gain actionable insights into fostering fair AI systems.
Learning Objectives:
- Understand the concepts of fairness, bias, and ethics in AI.
- Recognize sources of bias in generative AI and LLMs.
- Develop skills to evaluate and measure AI fairness.
- Learn techniques to mitigate bias in AI outputs.
- Explore real-world case studies of fairness in AI applications.
- Formulate strategies to implement fairness guidelines in AI projects.
Target Audience:
- AI and ML Engineers
- Data Scientists
- Product Managers
- Compliance and Ethics Officers
- Researchers in AI Ethics
- Policymakers and Regulators
Course Modules:
Module 1: Introduction to Fairness in GenAI/LLM
- Overview of Fairness in AI
- Key Ethical Principles in AI
- Bias vs. Fairness in Generative Models
- Legal and Ethical Standards
- Risks of Unfair AI Systems
- Defining and Measuring Fairness
Module 2: Understanding Bias in Generative AI Models
- Sources of Bias in AI
- Historical Biases in Data
- Bias Amplification in Model Training
- Impact of Bias on LLM Outputs
- Case Studies of AI Bias
- Tools for Detecting Bias
Module 3: Techniques for Measuring Fairness
- Fairness Metrics for AI
- Statistical Bias Detection Methods
- Bias Testing Frameworks
- Quantifying Bias in LLMs
- Benchmarking Model Fairness
- Limitations of Fairness Metrics
Module 4: Strategies to Mitigate Bias in LLMs
- Pre-processing Data for Fairness
- Fair Model Training Approaches
- Post-processing Techniques to Reduce Bias
- Algorithmic Fairness Techniques
- Handling Outliers and Sensitive Groups
- Human-in-the-Loop Strategies
Module 5: Practical Applications and Case Studies
- Case Study: Fairness in Healthcare AI
- Case Study: Bias in Financial AI Systems
- Case Study: Fairness in Recruitment AI
- Case Study: Bias Mitigation in Social Media AI
- Impact of Bias on Consumer Experience
- Lessons Learned from Real-world Implementations
Module 6: Developing Fairness Policies and Best Practices
- Creating Fairness Guidelines for AI Teams
- Policies for Fair AI Deployment
- Evaluating Third-Party AI Fairness Standards
- Building Inclusive AI Products
- Reporting and Transparency Standards
- Auditing and Monitoring Fairness in AI
Join the GenAI/LLM Fairness Workshop by Tonex to champion ethical, fair, and inclusive AI. Build skills that empower you to lead fairness initiatives and foster trust in AI applications. Enroll today!