Guidelines for Responsible AI

A financial services company uses Amazon Rekognition to verify customer identities. To ensure responsible AI practices, which measure should the company prioritize?

A healthcare startup deploys a model to predict patient readmission risk using Amazon SageMaker. After deployment, the model shows higher false-positive rates for a specific age group. What is the most responsible first step?

A company uses an AI system to automate loan approvals. The model uses demographic features and achieves high accuracy, but the company wants to ensure compliance with responsible AI guidelines. Which practice best balances performance and fairness?

A retail company uses a recommendation system that occasionally suggests inappropriate products to minors. Which responsible AI practice should be applied?

A company uses Amazon Comprehend to analyze customer sentiment. They discover the model performs poorly on text with slang from underrepresented groups. What is the most responsible action?

A bank uses an AI system to detect fraudulent transactions. The model has high precision but low recall for small transactions, potentially missing fraud. Which approach aligns with responsible AI?

A company develops a chatbot using Amazon Lex. To ensure transparency, what should the chatbot do when it cannot answer a question?

Which TWO actions are most aligned with responsible AI practices when deploying a model that makes decisions affecting individuals? (Choose 2)

Which THREE considerations are essential for ensuring responsible AI in a model that predicts employee performance? (Choose 3)

Which TWO practices help ensure transparency in AI systems? (Choose 2)

An AI team uses the IAM policy shown in the exhibit to control endpoint creation. Why does this policy support responsible AI?

A data scientist runs the SageMaker Clarify job shown in the exhibit for a credit risk model. After reviewing the results, they find a high bias metric for the gender facet. Which action is most consistent with responsible AI?

A financial services company is deploying a generative AI chatbot to assist customers with account inquiries. The company wants to ensure the chatbot does not generate biased or harmful responses. Which combination of AWS services and practices should the company implement to monitor and mitigate these risks?

A healthcare organization is developing a clinical decision support system using Amazon Bedrock with a large language model (LLM) to analyze patient symptoms and suggest potential diagnoses. The system must comply with HIPAA and internal responsible AI guidelines. During testing, the model occasionally generates diagnoses that are inconsistent with established medical guidelines and shows a tendency to recommend more aggressive treatments for patients from certain demographic groups. The team has already implemented data encryption, access controls, and basic content filtering. They need to further reduce biased and unsafe outputs without delaying the deployment timeline. What should the team do next?

A data scientist wants to detect potential bias in a binary classification model before deployment. Which AWS service can analyze the model's predictions across different demographic groups?

A team is deploying a regression model for loan approval. To ensure transparency for regulators, they need to explain individual predictions. Which interpretability method can provide local explanations by approximating the model with a simpler surrogate?

A healthcare company must train a model on sensitive patient data while complying with privacy regulations. They want to add noise to the training process to prevent re-identification. Which technique should they implement?

After deploying a model, a company notices that the distribution of the input features has shifted compared to the training data. Which feature of Amazon SageMaker Model Monitor can alert them to this change?

A company uses Amazon SageMaker Ground Truth to label a dataset for a binary classifier. To reduce labeling bias, which workforce configuration is most appropriate?

A machine learning team is building a credit risk model and discovers that the training data has a significant imbalance in loan approval rates between two demographic groups. They decide to reweight the training samples using a preprocessing technique. Which SageMaker Clarify feature can help compute the appropriate sample weights to achieve demographic parity?

Which of the following is a key principle of responsible AI according to AWS?

A company wants to ensure accountability for its machine learning models by tracking all changes to the model and its training data. Which AWS feature should they use?

A security team is concerned about adversarial attacks on their image classification model deployed on Amazon SageMaker. They want to test robustness against carefully crafted inputs that cause misclassification. What approach should they use?

Which TWO actions should a data scientist take to evaluate fairness of a binary classification model using Amazon SageMaker Clarify? (Choose two.)

Which THREE considerations are important when implementing responsible AI for a production NLP system? (Choose three.)

Which TWO techniques provide interpretability for machine learning models at a local (per-prediction) level? (Choose two.)

Refer to the exhibit. A data scientist runs an Amazon SageMaker Clarify bias analysis on a binary classifier. The pre-training ClassImbalance is 1.5 and the post-training DPPL is 0.15. What should the data scientist conclude?

Refer to the exhibit. A team is creating an IAM policy for a SageMaker notebook user. The user needs to access training data in an S3 bucket and create models. Which responsible AI concern is most relevant to this policy?

Refer to the exhibit. A developer is reviewing CloudWatch Logs for a deployed model and notices the same input appears multiple times with slightly different probabilities. What responsible AI concern does this pattern suggest?

A company uses Amazon Rekognition for facial analysis. They want to ensure the model doesn't exhibit bias based on skin tone. What should they do?

A financial services company deploys a generative AI chatbot for customer support. They want to prevent the chatbot from generating harmful or misleading information. Which AWS service can help monitor and filter responses?

A healthcare startup uses Amazon SageMaker to train a model predicting patient readmission. They need to ensure the model's predictions do not discriminate based on protected attributes like age or race. Which SageMaker feature allows them to monitor and mitigate bias during training?

A media company uses Amazon Transcribe for automatic speech recognition. They discover the model has higher error rates for non-native English speakers. Which Responsible AI principle are they failing to uphold?

An e-commerce company uses a recommendation system built with Amazon Personalize. They want to explain to customers why certain products are recommended. Which AWS service can provide model explanations?

A government agency uses Amazon Rekognition for identity verification. They want to ensure the model is robust against adversarial attacks. What should they do?

A social media company uses Amazon Comprehend to moderate user comments. They want to avoid censoring legitimate speech while catching hate speech. Which approach aligns with responsible AI governance?

A startup uses Amazon Lex to build a chatbot for mental health support. They must ensure user conversations are private and not used for model improvement. Which AWS service can help anonymize text data before storage?

A research lab uses Amazon SageMaker to train a deep learning model for medical diagnosis. They need to ensure the model's decisions are interpretable to clinicians. Which SageMaker feature provides local and global feature importance?

Which TWO actions help ensure fairness in an AI system deployed on AWS? (Select two.)

Which THREE practices support transparency in AI systems? (Select three.)

Which TWO actions can help mitigate bias in a face recognition model trained on AWS? (Select two.)

Refer to the exhibit. An AWS customer runs SageMaker Clarify to evaluate bias in their training data. The report shows multiple metrics with status 'violated'. What should the customer do next?

Refer to the exhibit. A team is configuring a SageMaker Model Bias job. The baseline job has been completed. However, the bias job fails with a resource not found error. What is the most likely cause?

Refer to the exhibit. An ML team finds that their training data is stored in two subfolders under s3://my-bucket/train/. They need to ensure that the dataset is balanced for training a classification model. What should they do?

A company uses Amazon SageMaker to build a binary classification model for loan approvals. After training, the data science team wants to evaluate the model for potential bias against a protected group. Which AWS service should they use to compute bias metrics?

A data scientist is using Amazon SageMaker to train a model and wants to understand the contribution of each feature to individual predictions. Which technique should they use to generate local explanations?

A company deploys a deep learning model for image classification using Amazon SageMaker. They are concerned about adversarial attacks that could misclassify images with small perturbations. Which of the following is the most effective approach to improve model robustness?

A healthcare company is training a model on sensitive patient data using Amazon SageMaker. They need to ensure that individual patient data cannot be reverse-engineered from the model. Which technique should they implement during training?

A team is developing an AI system and wants to document key information such as intended use, performance benchmarks, and limitations. According to AWS best practices for responsible AI, what should they create?

A large enterprise has multiple teams deploying ML models on AWS. To ensure governance and accountability, they need to enforce that all models pass a fairness review before production deployment. Which SageMaker feature should they use to implement this approval workflow?

An e-commerce company uses an Amazon Lex chatbot to handle customer inquiries. They want to implement human oversight for sensitive interactions, such as when the chatbot cannot provide a confident response. Which AWS service should they integrate?

Which of the following is NOT one of the core principles of responsible AI as defined by AWS?

A financial services company must comply with regulatory requirements that mandate explainability of credit scoring models. They have deployed a model using SageMaker and need to generate reports showing feature importance for each prediction. Which combination of services should they use to automate this?

A data science team is building a resume screening model and wants to ensure it does not exhibit gender bias. Which TWO actions are most effective for mitigating bias? (Choose TWO.)

A company is deploying an AI-based diagnostic system in healthcare. Which THREE practices align with AWS responsible AI guidelines? (Choose THREE.)

A team is using Amazon Comprehend to analyze customer feedback for sentiment. They want to detect and mitigate potential bias against certain demographic groups. Which TWO approaches should they consider? (Choose TWO.)

Refer to the exhibit. A data scientist runs SageMaker Clarify on a training dataset and receives the above JSON output. Which bias metric exceeds its threshold?

Refer to the exhibit. An AWS CloudTrail log shows the creation of an IAM policy for a SageMaker execution role. Which responsible AI concern does this configuration raise?

Refer to the exhibit. An AWS administrator sets up a SageMaker Model Monitor schedule for bias detection. What is the primary purpose of this configuration?

A retail company is deploying a chatbot to handle customer inquiries. During testing, they notice the chatbot occasionally uses offensive language when responding to certain user inputs. Which responsible AI principle is being violated?

A financial institution uses a machine learning model to approve loan applications. The model is trained on historical data that includes biased lending practices. What is the most effective first step to address potential bias?

A healthcare organization uses an AI model to predict patient readmission risks. The model's predictions are used by doctors to allocate follow-up care. The organization wants to ensure compliance with responsible AI guidelines. Which practice best supports explainability?

A company uses an AI system to screen job applications. The system was trained on resumes from previous hires, which predominantly came from a specific demographic. As a result, the system may unfairly filter out qualified candidates from other backgrounds. Which responsible AI practice should the company implement?

A company is deploying a generative AI model that produces text summaries of legal documents. To comply with responsible AI guidelines, which of the following is the most critical to ensure transparency?

An insurance company uses a machine learning model to adjust premiums. During a review, the model is found to be penalizing customers based on zip codes correlated with racial demographics, leading to potential discrimination. Which combination of actions best addresses this fairness issue while maintaining business value?

Which TWO actions are essential for ensuring accountability in AI systems according to AWS responsible AI guidelines?

Which THREE practices are recommended for promoting robustness and security in AI systems?

Which TWO of the following are key components of a responsible AI governance framework?

A startup is developing a mobile app that uses facial recognition to verify user identity for account access. The app is intended for a global audience, but the training data predominantly includes images of light-skinned individuals. During beta testing, users with darker skin tones report frequent verification failures, while light-skinned users have a high success rate. The startup wants to release the app soon and needs to address this fairness issue without delaying the launch too much. The team has limited resources. Which approach should they take to most effectively mitigate the bias while meeting the launch timeline?

A hospital uses an AI system to prioritize patients for organ transplant based on predicted survival rates. The system was trained on historical data that includes socioeconomic factors. A review reveals that the system systematically assigns lower priority to patients from lower-income neighborhoods, even when medical urgency is similar. The hospital's ethics board demands an immediate remedy. The data science team is small and must act quickly. What should the hospital do to address this fairness issue most effectively?

A large e-commerce company uses a recommendation system to suggest products to customers. Recently, a data scientist noticed that the model's recommendations for high-value luxury items are predominantly shown to users in affluent zip codes, while users in less affluent areas rarely see these items, even if they have searched for them. The company is concerned about fairness and wants to ensure all customers have equal access to recommendations regardless of location. The current model uses collaborative filtering on historical purchase data. The team needs to modify the system without sacrificing overall recommendation accuracy. Which action best addresses the fairness concern while maintaining performance?

A government agency is deploying an AI system to detect fraudulent benefit claims. The system uses a neural network trained on historical claims data, which includes a disproportionate number of false positives from a particular ethnic group due to historical over-policing. The agency must ensure the system does not perpetuate discrimination. They have a rigorous testing procedure but limited budget. The project lead wants to balance fairness with detection performance. Which combination of steps should they prioritize?

A financial services company uses a machine learning model to automatically reject credit card transactions suspected of fraud. The model was trained on transaction data from the past two years. Over the last three months, the model's false positive rate has increased significantly, causing legitimate transactions to be declined and leading to customer complaints. The company needs to restore the model's accuracy quickly. Initial analysis shows that the distribution of transaction amounts and locations has shifted compared to the training period. The data science team is under pressure to deploy an update within a week. Which approach should they take to most effectively address the issue while adhering to responsible AI guidelines?

A media company uses a generative AI model to automatically create image captions for user-uploaded photos. During quality assurance, testers discover that the model sometimes generates captions that include stereotypes based on gender and race, even when the photos do not contain people. For example, a photo of a kitchen produces captions like 'woman cooking,' and a photo of a sports car generates 'man driving.' The company wants to launch the feature soon but recognizes the reputational risk. They have a limited budget and need to implement a solution that reduces harmful stereotypes without overly restricting the captions' creativity. The team has access to the model's training data, which is a large public dataset of image-caption pairs. Which approach should the team prioritize?

A retail company is deploying a machine learning model to analyze customer reviews and predict sentiment. The team wants to follow responsible AI guidelines to ensure fairness, transparency, and accountability. Which TWO actions should the team take? (Choose TWO.)

Refer to the exhibit. A data scientist used SageMaker Clarify to evaluate bias in a binary classification model predicting loan approval. The exhibit shows bias metrics for the female facet. What does the analysis indicate about the model's impact on the female group?

A healthcare company uses Amazon SageMaker to train a model that predicts patient readmission risk based on electronic health records (EHRs) stored in Amazon HealthLake. The training dataset contains 2 million records from the past three years, with a significant gender imbalance: 70% male and 30% female. The model achieved high overall accuracy, but further analysis using SageMaker Clarify revealed that the precision for female patients is 0.65 while for male patients it is 0.88. Additionally, the model's false positive rate for female patients is significantly higher. The company must comply with healthcare regulations that require fairness and non-discrimination. The data science team has already used SageMaker Data Wrangler for initial preprocessing and SageMaker Clarify for bias detection. They need to take immediate action to mitigate the bias before deploying to production. Which course of action should the team take?