Learn how to master responsible AI. Learn fairness, bias mitigation, explainable AI, and data privacy to design ethical AI systems. Future-proof your skills in trustworthy AI practices.

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This responsible AI course provides an in-depth exploration of ethical AI development, equipping you with tools and strategies to build transparent, fair, and secure AI systems.

Begin by understanding the core principles of responsible AI, including fairness and transparency. Explore real-world examples to identify and mitigate biases across the AI life cycle, ensuring equitable solutions in critical domains like healthcare.

Next, dive into explainable AI techniques to interpret and communicate AI model decisions, enhancing trustworthiness and accountability. Learn strategies to safeguard data privacy and mitigate risks, ensuring security in AI development.

Conclude by exploring innovations in responsible AI, such as synthetic data generation and active learning, to stay ahead in the evolving field of ethical AI. After completing this course, you’ll have the knowledge and skills to design and deploy trustworthy AI systems.

Responsible AI: Principles and Practices

# Bias in the data preparation phase

Bias may enter the system even before we have started the model-building steps. 

During problem formation itself, we need to validate if AI is an ethical solution to this problem.

Data collection and preparation is the stage when a lot of biases creep into the solutions. We need to validate if the data is free of any selection bias and is representative of different segments; there should be no biases in data labels and features getting created.

Let’s explore a few of these using some practical examples:




* **Selection bias:** This refers to a type of bias that occurs when the process of selecting data for analysis or study results in a nonrandom or unrepresentative sample. It arises when certain characteristics or factors influence the inclusion or exclusion of data points, leading to a skewed or biased representation of the population of interest. Imagine a model that is developed to predict the success of a new smartphone based on surveys conducted with customers who purchased that specific phone. However, the surveys only include feedback from customers who bought that particular phone and do not include any input from customers who chose to purchase a different brand or model. As a result, the model’s predictions may be biased toward positive outcomes because it lacks information about the experiences and preferences of customers who opted for a different phone.

* **Biased features:** This occurs when certain features become disproportionately influential in the decision-making process, leading to biased outcomes. For example, if the AI system assigns a higher weight to the educational background feature and the training data shows that individuals from a particular race or socioeconomic group tend to have higher levels of education in that region, it can result in biased decisions. 

* **Biased labels:** These are labels or categories assigned to data instances in a dataset that introduces unfairness or discrimination. In the context of machine learning and AI, labels are typically assigned to training data to indicate the desired output or target variable. However, if the process of labeling data is influenced by biased or unfair criteria, it can lead to biased outcomes in the AI model’s predictions.

* **Active bias:** This implies a conscious effort to manipulate or skew the outcomes of an AI system in a biased manner. This could occur through biased data selection, biased algorithm design, or biased decision-making during the development or implementation of the AI system. It is important to note that intentionally introducing bias into AI systems is generally considered unethical and goes against the principles of responsible AI development.


* **Interaction bias:** This refers to a type of bias that can occur in user-based systems or platforms, where the behavior or preferences of users influence the system’s outputs or recommendations. It arises when the data or feedback collected from users is biased or unrepresentative of the entire user population, leading to skewed results or biased decision-making.

* **Latent bias:** This refers to biases that are hidden or not immediately apparent in a dataset or system. It refers to biases that exist in the underlying structure, assumptions, or representations of the data but may not be explicitly captured or observable. This can arise from various sources, such as the data collection process, the design of algorithms or models, or the inherent limitations and biases present in the real-world phenomena being studied. These biases may not be intentionally introduced but can still influence the outcomes and decisions made by AI systems.


Learn about the various biases that can occur in the AI solution development process.

Introduction to Responsible AI

Fairness of AI Solutions

Explainable AI

Data Privacy, Safety, and Security for Responsible AI

Innovations in Responsible AI: Charting New Frontiers

Final Project

Conclusion

Understanding the Bias in Model Development

Bias in the data preparation phase