Building AI for a Better World

Responsible AI is a set of principles and practices for designing, building, and deploying AI systems that are safe, fair, and transparent. As an ML engineer, it's your responsibility to ensure that your models are not only accurate but also ethical.

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1. Security

ML models are a new type of software, and they are vulnerable to new types of attacks. Some common security risks include:

• Data poisoning: An attacker can inject malicious data into your training set to corrupt your model.
• Model inversion: An attacker can use your model's predictions to infer sensitive information about your training data.
• Adversarial examples: An attacker can create inputs that are designed to fool your model into making incorrect predictions.

To mitigate these risks, you should:

• Use a secure data pipeline: Ensure that your data is encrypted at rest and in transit.
• Use a managed ML platform: Vertex AI provides a secure environment for training and deploying your models.
• Monitor your model for attacks: Use a tool like the What-If Tool to detect adversarial examples.

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2. Bias and Fairness

ML models can learn and amplify biases that are present in the training data. This can lead to unfair outcomes for certain groups of people. For example, a model that is trained on a dataset of resumes that is biased against women may be less likely to recommend women for jobs.

To mitigate bias and ensure fairness, you should:

• Use a diverse and representative training set: Your training set should reflect the diversity of the population that your model will be used on.
• Use a fairness-aware model: Some models are specifically designed to be fair, such as the MinDiff model.
• Use a tool like the What-If Tool to audit your model for bias: The What-If Tool can help you identify any fairness issues in your model.

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Knowledge Check