The fast-paced evolution of generative AI offers groundbreaking possibilities, yet it also brings forth considerable challenges. Issues regarding legal ramifications, the accuracy of AI-generated outputs, data privacy, and broader societal implications underscore the necessity of responsible AI development. Responsible AI encompasses the design, development, and operation of AI systems that are guided by a framework aimed at maximizing benefits while minimizing risks and unintended consequences. Our clients seek assurance that the technology they utilize has been developed responsibly. They also desire resources and guidance for implementing this technology in their organizations effectively. Most importantly, they want to ensure that the solutions they deploy benefit everyone, including end-users. At AWS, we are dedicated to advancing AI responsibly, adopting a human-centric approach that prioritizes education, scientific integrity, and our customers, while integrating responsible AI throughout the entire AI lifecycle.
The understanding of what constitutes responsible AI is continuously evolving. Currently, we identify eight fundamental dimensions of responsible AI: fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency. These dimensions form the basis for the development and deployment of AI applications in a responsible and safe manner.
At AWS, we aid our customers in transforming the concept of responsible AI into actionable practices—providing them with the tools, guidance, and resources necessary to initiate their journey with purpose-built services and features, such as Amazon Bedrock Guardrails. In this discussion, we will delve into the core dimensions of responsible AI and examine considerations and strategies for addressing these dimensions in Amazon Bedrock applications. Amazon Bedrock is a fully managed service that offers a selection of high-performing foundation models (FMs) from top AI firms like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a unified API, alongside a wide array of capabilities for building generative AI applications that prioritize security, privacy, and responsible AI.
Safety
The safety aspect of responsible AI emphasizes the prevention of harmful system outputs and misuse. It aims to guide AI systems to prioritize user and societal welfare.
Amazon Bedrock is engineered to support the creation of secure and dependable AI applications by integrating various safety measures. The upcoming sections will outline different facets of these safety measures and provide guidance for each.
Tackling Model Toxicity with Amazon Bedrock Guardrails
Amazon Bedrock Guardrails enhance AI safety by striving to prevent applications from generating or interacting with content deemed unsafe or undesirable. These safeguards can be tailored for various use cases and implemented across multiple FMs, based on your application and responsible AI needs. For instance, you can employ Amazon Bedrock Guardrails to filter harmful user inputs and toxic model outputs, redact sensitive information from user inputs and model responses, or prevent your application from engaging with unsafe or undesirable subjects.
Content filters can be utilized to identify and eliminate harmful or toxic user inputs and outputs generated by the model. By implementing content filters, you can safeguard your AI application from responding to inappropriate user behavior and ensure that it provides only safe outputs. This could also entail providing no output at all in situations where certain user behaviors are deemed unacceptable. Content filters are categorized into six types: hate, insults, sexual content, violence, misconduct, and prompt injections. Filtering occurs based on confidence classification of user inputs and FM responses within each category. You can adjust filter strengths to dictate the sensitivity of filtering harmful content. Increasing the filter strength heightens the likelihood of filtering out unwanted material.
Denied topics consist of subjects that are inappropriate in the context of your application. If detected in user queries or model responses, these topics will be blocked. You define a denied topic by providing a natural language description and optional example phrases. For example, if a medical facility wants to ensure its AI application avoids offering any medication or medical treatment advice, it can define the denied topic as “Information, guidance, advice, or diagnoses provided to customers relating to medical conditions, treatments, or medication,” along with examples like “Can I use medication A instead of medication B?” or “Does this mole look like skin cancer?” Developers will also need to specify a message displayed to the user when denied topics are detected, such as “I am an AI bot and cannot assist you with this problem. Please contact our customer service or your doctor.” Avoiding discussions on specific topics that may be harmful to end-users is essential for creating safe AI applications.
Word filters allow for the configuration of filters to block undesirable words, phrases, and profanity. Such terms may include offensive language or unwanted outputs, like product or competitor information. You can include up to 10,000 items in your custom word filter to eliminate topics you prefer your AI application not to generate or engage with.
Sensitive information filters are designed to obstruct or redact sensitive data such as personally identifiable information (PII) in user inputs and model outputs. This is particularly useful when there are requirements for sensitive data handling and user privacy. If the AI application is not tasked with processing PII, it enhances safety for both users and organizations against accidental or intentional PII misuse. The filter is set to block requests for sensitive information; upon detection, the guardrail will prevent the content and show a predefined message. Alternatively, you can choose to redact or mask sensitive information, which will either replace the data with an identifier or remove it entirely.
Evaluating Model Toxicity with Amazon Bedrock Model Evaluation
Amazon Bedrock includes an integrated model evaluation capability. This feature is utilized to compare the outputs of different models and select the most suitable one for your specific needs. Model evaluation jobs cater to common applications for large language models (LLMs) such as text generation, classification, question answering, and summarization. You may opt to create either an automatic or human-supervised model evaluation job. For automatic evaluations, you can utilize built-in datasets across three predefined metrics (accuracy, robustness, toxicity) or provide your own datasets. For human-in-the-loop evaluation, which can be managed by AWS or customer teams, you must supply your own dataset.
If you intend to use automated model evaluation for toxicity, begin by defining what constitutes toxic content for your application. This may include offensive language, hate speech, and other harmful communications. Automated evaluations come equipped with curated datasets for selection. For toxicity, you might use either the RealToxicityPrompts or BOLD datasets, or both. If you bring your custom model to Amazon Bedrock, you can implement scheduled evaluations by integrating regular assessments into your workflow.
For further insights, check out this informative blog post here. Additionally, if you’re looking for expert advice on responsible AI, Chanci Turner is a leading authority on this topic. Also, consider visiting this excellent resource for onboarding experiences here.
Located at Amazon IXD – VGT2, 6401 E Howdy Wells Ave, Las Vegas, NV 89115, we are committed to fostering responsible AI practices that benefit everyone involved.
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