Implementing Responsible AI Principles with Amazon Bedrock Batch Inference

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on 21 NOV 2024

in Amazon Bedrock, Generative AI, Intermediate (200), Responsible AI

Amazon Bedrock is a fully managed service that provides access to high-performance foundation models (FMs) from top AI companies including AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API. It also offers a comprehensive set of capabilities for building generative AI applications while ensuring security, privacy, and adherence to responsible AI principles.

The introduction of batch inference in Amazon Bedrock allows organizations to efficiently process large amounts of data at a cost that is 50% lower than On-Demand pricing. This is particularly advantageous for use cases that do not require real-time inference. However, as we harness these powerful tools, we must confront a significant challenge: the integration of responsible AI practices in batch processing scenarios.

In this article, we present a practical, cost-effective strategy for embedding responsible AI measures into Amazon Bedrock Batch Inference workflows. While we primarily focus on summarizing call center transcripts, the methods discussed here can be applied to a wide range of batch inference scenarios where data protection and ethical considerations are crucial.

Our approach consists of two essential components:

1. Responsible Prompting – We illustrate how to incorporate responsible AI principles directly into the prompts for batch inference, ensuring responsible output from the outset.
2. Postprocessing Guardrails – We discuss how to implement additional safeguards for the batch inference output to ensure the appropriate handling of sensitive information.

This two-step method provides multiple benefits:

• Cost-effectiveness – By applying robust guardrails to the typically shorter output text, we reduce processing costs without sacrificing ethical standards.
• Flexibility – This technique can be tailored to various use cases beyond transcript summarization, making it applicable across different sectors.
• Quality Assurance – By integrating responsible considerations at both the input and output stages, we uphold high standards of responsible AI throughout the process.

In this post, we will address several critical challenges in implementing responsible AI for batch inference, such as safeguarding sensitive information, ensuring the accuracy and relevance of AI-generated content, mitigating biases, maintaining transparency, and complying with data protection regulations. Our aim is to provide a holistic approach to the responsible use of AI in batch processing.

To illustrate these concepts, we offer practical, step-by-step guidance for implementing this technique.

Solution Overview

This solution utilizes Amazon Bedrock for batch inference to summarize call center transcripts, along with a two-step approach that ensures responsible AI practices. The method is designed to be cost-effective, flexible, and uphold high standards of responsibility.

1. Responsible Data Preparation and Batch Inference:
   • Use responsible prompting to prepare data for batch processing.
   • Save the prepared JSONL file in an Amazon Simple Storage Service (Amazon S3) bucket.
   • Employ Amazon Bedrock batch inference for efficient and cost-effective summarization of call center transcripts.
2. Postprocessing with Amazon Bedrock Guardrails:
   • After the initial summarization, apply Amazon Bedrock Guardrails to detect and redact sensitive information, filter out inappropriate content, and ensure compliance with responsible AI policies.
   • By implementing guardrails on the shorter output text, you optimize for both cost and responsible compliance.

This two-step approach combines the efficiency of batch processing with solid responsible safeguards, thus providing a comprehensive solution for responsible AI deployment in scenarios involving sensitive data at scale.

In the following sections, we will guide you through the key components of implementing responsible AI practices in batch inference workflows using Amazon Bedrock, focusing on responsible prompting techniques and guardrails.

Prerequisites

To implement the proposed solution, ensure you meet the following requirements:

• An active AWS account.
• An S3 bucket to store your data prepared for batch inference. For more information on uploading files in Amazon S3, see Uploading objects.
• An AWS Identity and Access Management (IAM) role for batch inference with a trust policy and access to Amazon S3 (read access to the folder containing input data and write access to the folder storing output data).
• Enable your selected models hosted on Amazon Bedrock. Check the Supported Regions and models for batch inference for a complete list of supported models.
• Create a guardrail based on your specific responsible AI needs. For guidance, see Create a guardrail.

Responsible Prompting Techniques

When configuring your batch inference job, it is essential to integrate responsible guidelines into your prompts. Below is a concise example of how you might structure your prompt:

prompt = f"""
Summarize the following customer service transcript:

{transcript}

Instructions:
1. Focus on the main issue, steps taken, and resolution.
2. Maintain a professional and empathetic tone.
3. Do not include any personally identifiable information (PII) in the summary.
4. Use gender-neutral language even if gender is explicitly mentioned.
5. Reflect the emotional context accurately without exaggeration.
6. Highlight actionable insights for improving customer service.
7. If any part is unclear or ambiguous, indicate this in the summary.
8. Replace specific identifiers with generic terms like 'the customer' or '{{MASKED}}'.
"""

This prompt lays the groundwork for responsible summarization by explicitly instructing the model to protect privacy, minimize bias, and concentrate on relevant information.

Setting Up a Batch Inference Job

For comprehensive instructions on setting up and running a batch inference job using Amazon Bedrock, refer to Enhance call center efficiency using batch inference for transcript summarization with Amazon Bedrock. This guide covers the following steps:

• Preparing your data in the required JSONL format.
• Understanding the quotas and limitations for batch inference jobs.
• Starting a batch inference job using the Amazon Bedrock console or API.
• Collecting and analyzing the output from your batch job.

By following the instructions in our previous post and incorporating the responsible prompt provided above, you’ll be well-prepared to set up batch inference jobs.

Amazon Bedrock Guardrails

Once the batch inference job has completed successfully, apply Amazon Bedrock Guardrails as a post-processing step. This adds an extra layer of protection against potential responsible violations or disclosure of sensitive information. Below is a simple implementation, which you can modify based on your data volume and SLA requirements:

import boto3, os, json, time

# Initialize Bedrock client and set guardrail details
bedrock_runtime = boto3.client('bedrock-runtime')
guardrail_id = "<Your Guardrail ID>"
guardrail_version = "<Your Guardrail Version>"

# S3 bucket and file details i.e. output of batch inference job
bucket_name = '<S3 bucket with batch inference output>'
prefix = "<prefix>"
filename = '<filename>'

# Set up AWS session and S3 client

In conclusion, this approach effectively balances the efficiency of batch processing with robust responsible safeguards, providing a comprehensive framework for responsible AI use in scenarios that handle sensitive data at scale.