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 ~70 open-source, industry-standard tools for accomplishing various RAI-related activities. These tools serve as illustrations of the kinds of tools that should be used to address the particular activities to which they were linked in the SHIELD Assessment. Many of these tools will be replaced by tools developed (or acquired) by the DoD RAI team or the RAI Working Council. For the non-DoD, open-source tools listed here, the inclusion of these open-source tools in the RAI Tools List should not be seen as an endorsement or approval of their use; DoD personnel should still go through normal approval processes before using them.

Tools that are being developed or acquired for the DoD will be indicated with a red subtitle in it’s description, such as: “[In development for the DoD].”

DISCLAIMER: THE TOOLS INCLUDED IN THIS TOOLKIT ARE A GUIDE INTENDED TO BE ILLUSTRATIVE TO DEVELOPERS AND USERS SEEKING TO ADDRESS RAI CONCERNS. THE LIST IS NOT EXHAUSTIVE. IMPORTANTLY, THE INCLUSION OF A TOOL ON THIS LIST DOES NOT CONSTITUTE AN ENDORSEMENT OR APPROVAL OF ANY LISTED TOOL BY CDAO, the DOD, or the US GOVERNMENT.

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RAI Activities

×^
AI Appropriateness Assessment
2
Assess Requirements, Statements of Concern, Mitigations, and Metrics
3
Decide to Proceed to Ideation
2
Determine the Use Case for the AI
6
Establish Incident Response Procedures
5
Identify and Engage Stakeholders
6
Identify Risks & Opportunities / Navigate Tradeoffs
29
Instrument AI to promote Assurance
41
Lessons Learned
5
Operational Testing
16
Perform Continuous Monitoring of the System and its Use, Context, & Ecosystem
21
Plans for System Retirement
4
Record Lessons Learned
4
Responsibility Flows and Governance
4
Revisit Documentation and Security
22
Revisit Documentation and Security / Roll-up into Dashboards
10
Test Components for Robustness and Resilience
17
Train Users
1
Tool Class

×^
Adversarial Attack Generation
3
AI Appropriateness Assessment
1
Algorithmic Auditing Tools
1
Assurance and Trust Instruments
1
Bias Red-Teaming Resources
1
Concept of Operations
1
Confidence Metrics
5
Counterfactual Example Generation
3
Crowdsourcing Methods for Measuring Bias
1
Data Card Template
2
Data Privacy Tools
2
Environmental Impact Estimation
1
Executive Dashboards
1
Feature Attribution Algorithms
10
Generalization Test Datasets
3
Group Parity Metrics
3
Group Parity Optimization
2
Human Bias Reduction Resources
2
Human-Machine Teaming Resources
3
Impact Assessments
2
Incident Response Guidance
1
Incidents and Failure Modes
3
Input Regularization Algorithms
1
Law- and Policy-Finding Tools
1
Model Card Template
2
Navigating Tradeoffs Tools
1
Out-of-Distribution Detection Tools
4
Public Affairs and Communications Tools
1
RAI Acquisition Tools
1
RAI Checklists
1
RAI Program Manager Review
1
Responsibility Flows Tools
1
Rollback Resources
1
Root Cause Analysis
1
Security Review Resources
1
Use Case Repository
1
User Experience Design Activities
5
Principles

×^
Apply Armory Testbed to evaluate the AI capability’s uncertainty on in-distribution inputs to help measure and demonstrate reliability (effectiveness of AI capabilities) and governability (fulfill intended functions).
1
Apply EQUI(NE2) to evaluate the AI capability’s uncertainty on in-distribution inputs to help measure and demonstrate reliability (effectiveness of AI capabilities) and governability (fulfill intended functions).
1
Apply IBM Adversarial Robustness 360 Estimators to evaluate the AI capability’s uncertainty on in-distribution inputs to help measure and demonstrate reliability (effectiveness of AI capabilities) and governability (fulfill intended functions).
1
Apply IBM Uncertainty Quantification 360 to evaluate the AI capability’s uncertainty on in-distribution inputs to help measure and demonstrate reliability (effectiveness of AI capabilities) and governability (fulfill intended functions).
1
Apply the Carbon Emissions Calculator to the AI capability to inform stakeholders of the carbon cost of using the capability to help achieve traceability (appropriate understanding of the technology).
1
Ask users to complete the Trust in Autonomous Systems Test after they have tried out the AI capability to help measure and demonstrate traceability (appropriate understanding of the technology and operational methods).
1
Compose a model card, patterned after the Model Card Examples, for each model, describing: its technique and how it was designed, to help achieve traceability (transparent and auditable documentation). its technique, to help measure and demonstrate traceability (appropriate understanding of the technology). how it was designed, to help measure and demonstrate traceability (transparent and auditable design procedures).
1
Conduct activities using the Design Kit Inspiration Methods to hone how the AI capability will be used to help achieve reliability (well-defined uses).
1
Conduct activities using the Impact Goals Tool to hone how the AI capability will be used to help achieve reliability (well-defined uses).
1
Conduct activities using the Stakeholder Engagement Toolkit to hone how the AI capability will be used to help achieve reliability (well-defined uses).
1
Conduct an activity using the Stakeholder Mapping Template to hone how the AI capability will be used to help achieve reliability (well-defined uses).
1
Conduct an impact assessment using the DIU Worksheets to hone how the AI capability will be used to help achieve reliability (well-defined uses).
1
Conduct an impact assessment using the Impact Assessment Tools to hone how the AI capability will be used to help achieve reliability (well-defined uses).
1
Conduct Use Case Analysis to hone how the AI capability will be used to help achieve reliability (well-defined uses).
1
Consult the Framework for Ethical Decision Making to iterate on the use case, optimizing it for the relevant ethical lenses, to help achieve reliability (well-defined uses).
1
Delegate authority using the Responsibility Flows Tool to help achieve responsibility (for the development, deployment, and use of AI capabilities) and traceability (appropriate understanding of the operational methods). Capture, in the Responsibility Flows Tool, which commands are issued by which authorized individuals to help measure and demonstrate responsibility (for the development, deployment, and use of AI capabilities).
1
Employ CounterFit to: generate adversarial attacks and train the AI capability to be robust to them to help achieve reliability (security of AI capabilities). Generate adversarial attacks and compute attack success rate to help measure and demonstrate reliability (security of AI capabilities).
1
Employ Diverse Counterfactual Explanations (DiCE) to: generate counterfactual input data points that can reveal susceptibility of the AI capability to adversarial attacks to help measure and demonstrate reliability (security of AI capabilities). generate counterfactual input data points that can reveal logic within the AI capability that is inconsistent with policy governing the capability, hence an unintended consequence, to help measure and demonstrate governability (ability to avoid unintended consequences).
1
Employ FoolBox to: generate adversarial attacks and train the AI capability to be robust to them to help achieve reliability (security of AI capabilities). generate adversarial attacks and compute attack success rate to help measure and demonstrate reliability (security of AI capabilities).
1
Employ IBM Adversarial Robustness 360 Attacks to: generate adversarial attacks and train the AI capability to be robust to them to help achieve reliability (security of AI capabilities). generate adversarial attacks and compute attack success rate to help measure and demonstrate reliability (security of AI capabilities).
1
Employ the Cognitive Biases Resource to minimize the influence of team members’ biases on the AI capability to help achieve equitability (deliberate steps to minimize unintended bias).
1
Employ the Human Bias Red-Teaming Toolkit to minimize the influence of team members’ biases on the AI capability to help achieve equitability (deliberate steps to minimize unintended bias).
1
Equip senior leaders to decide whether AI is an appropriate solution by completing the Checklist for ML Suitability to help achieve responsibility (appropriate levels of judgment).
1
Equip senior leaders to decide whether to continue funding an AI capability by completing RAI Program Manager Reviews to help achieve responsibility (appropriate levels of judgment).
1
Fill in a Hugging Face Data Card Template for each data source, describing: what it contains and how it was collected, to help achieve traceability (transparent and auditable documentation). how it was collected, to help measure and demonstrate traceability (appropriate understanding of the development processes). what it contains, to help measure and demonstrate traceability (transparent and auditable data sources).
1
Fill in a Hugging Face Model Card Template for each model, describing: its technique and how it was designed, to help achieve traceability (transparent and auditable documentation). its technique, to help measure and demonstrate traceability (appropriate understanding of the technology). how it was designed, to help measure and demonstrate traceability (transparent and auditable design procedures).
1
Fill in an Aether Datasheet Template for each data source, describing what it contains and how it was collected, to help achieve traceability (transparent and auditable documentation). how it was collected, to help measure and demonstrate traceability (appropriate understanding of the development processes). what it contains, to help measure and demonstrate traceability (transparent and auditable data sources).
1
Heed the Incident Response Guidance when issues arise to help achieve responsibility (appropriate levels of care).
1
If input data correspond to individual people, apply masking techniques in the IBM Data Privacy Toolkit to help the AI capability avoid the unintended consequence of re-identifying individuals to help achieve reliability (safety of AI capabilities) and governability (ability to avoid unintended consequences). apply tests in the IBM Data Privacy Toolkit to see how well the AI capability avoids the unintended consequence of re-identifying individuals to help measure and demonstrate reliability (safety of AI capabilities) and governability (ability to avoid unintended consequences).
1
If input data correspond to individual people, apply masking techniques in the IBM Data Privacy Toolkit to help the AI capability avoid the unintended consequence of re-identifying individuals to help achieve reliability (safety of AI capabilities) and governability (ability to avoid unintended consequences). If input data correspond to individual people, apply tests in the IBM Data Privacy Toolkit to see how well the AI capability avoids the unintended consequence of re-identifying individuals to help measure and demonstrate reliability (safety of AI capabilities) and governability (ability to avoid unintended consequences).
1
If labeled training data are used and group affiliations are recorded, use group parity metrics in Auditing tools to calculate group parity among labels to help achieve traceability (transparent and auditable data sources). use group parity optimization methods in Auditing tools to increase group parity by changing feature values in the data to help achieve equitability (deliberate steps to minimize unintended bias). If group affiliations are recorded for data points, use group parity optimization methods in Auditing tools to increase group parity in the model’s predicted labels by modifying the model to help achieve equitability (deliberate steps to minimize unintended bias). If labeled training data are used and group affiliations are recorded, use group parity metrics in Auditing tools before and after modifying the training data to calculate the change in group parity among labels to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If group affiliations are recorded for data points, use group parity metrics in Auditing tools before and after modifying the model to calculate the change in group parity among predicted labels to help measure and demonstrate equitability (deliberate steps to minimize unintended bias).
1
If labeled training data are used and group affiliations are recorded, use the Bias and Fairness Audit Tool to calculate group parity among labels to help achieve traceability (transparent and auditable data sources). use the Bias and Fairness Audit Tool before and after modifying the training data to calculate the change in group parity among labels to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If group affiliations are recorded for data points, use the Bias and Fairness Audit Tool before and after modifying the model to calculate the change in group parity among predicted labels to help measure and demonstrate equitability (deliberate steps to minimize unintended bias).
1
If labeled training data are used and group affiliations are recorded, apply TensorFlow Fairness Indicators to calculate group parity among labels to help achieve traceability (transparent and auditable data sources). If labeled training data are used and group affiliations are recorded, apply TensorFlow Fairness Indicators before and after modifying the training data to calculate the change in group parity among labels to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If group affiliations are recorded for data points, apply TensorFlow Fairness Indicators before and after modifying the model to calculate the change in group parity among predicted labels to help measure and demonstrate equitability (deliberate steps to minimize unintended bias).
1
If labeled training data are used and group affiliations are recorded, use TensorFlow Model Remediation to increase group parity by changing feature values in the data to help achieve equitability (deliberate steps to minimize unintended bias). If group affiliations are recorded for data points, use TensorFlow Model Remediation to increase group parity in the model’s predicted labels by modifying the model to help achieve equitability (deliberate steps to minimize unintended bias).
1
If the AI capability is a service that is continually available and for which new features are periodically released, use the Feature Release Rollback Resource to plan and prepare for rollbacks when new releases trigger unexpected behavior to help achieve responsibility (appropriate levels of judgment) and governability (ability to avoid unintended consequences).
1
If the features in the input data include ones that are not intended to influence the output, incorporate algorithms in ExplainerDashboard to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ ExplainerDashboard before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply ExplainerDashboard to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities).
1
If the features in the input data include ones that are not intended to influence the output, incorporate algorithms in FairML to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ FairML before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply FairML to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities).
1
If the features in the input data include ones that are not intended to influence the output, incorporate algorithms in IBM Explainability 360 to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ IBM Explainability 360 before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply IBM Explainability 360 to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities).
1
If the features in the input data include ones that are not intended to influence the output, incorporate algorithms in InterpretML to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ InterpretML before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply InterpretML to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities). Employ InterpretML to: generate counterfactual input data points that can reveal susceptibility of the AI capability to adversarial attacks to help measure and demonstrate reliability (security of AI capabilities). generate counterfactual input data points that can reveal logic within the AI capability that is inconsistent with policy governing the capability, hence an unintended consequence, to help measure and demonstrate governability (ability to avoid unintended consequences).
1
If the features in the input data include ones that are not intended to influence the output, incorporate algorithms in Model Agnostic Language for Exploration and Explanation (DALEX) to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ Model Agnostic Language for Exploration and Explanation (DALEX) before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply Model Agnostic Language for Exploration and Explanation (DALEX) to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities).
1
If the features in the input data include ones that are not intended to influence the output, incorporate algorithms in the What-If Tool to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ the What-If Tool before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply the What-If Tool to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities). Employ the What-If Tool to generate counterfactual input data points that can reveal susceptibility of the AI capability to adversarial attacks to help measure and demonstrate reliability (security of AI capabilities). counterfactual input data points that can reveal logic within the AI capability that is inconsistent with policy governing the capability, hence an unintended consequence, to help measure and demonstrate governability (ability to avoid unintended consequences).
1
If the features in the input data include ones that are not intended to influence the output, incorporate algorithms in the XAI Toolkit - Saliency to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ the XAI Toolkit - Saliency before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply the XAI Toolkit - Saliency to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities).
1
If the features in the input data include ones that are not intended to influence the output, incorporate LIME to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ LIME before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply LIME to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities).
1
If the features in the input data include ones that are not intended to influence the output, incorporate SHAP to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ SHAP before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply SHAP to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities).
1
If the features in the input data include ones that are not intended to influence the output, incorporate ShapASH to flag if weight is attributed to those features to help achieve governability (ability to detect unintended consequences). If the features in the input data include ones along which model bias is unintended, employ ShapASH before and after modifying the model to calculate the change in the weight attributed to those features to help measure and demonstrate equitability (deliberate steps to minimize unintended bias). If the features in the input data include ones that are not intended to influence the output, apply ShapASH to reveal if weight was attributed to those features to help measure and demonstrate reliability (effectiveness of AI capabilities).
1
If there are potential biases that are unintended for the AI capability and are widely recognized in society, use crowdsourcing methods, such as the Crowdsourcing Model Bias Tools, to design tests to present to crowd workers before and after modifying the capability to calculate the change in bias perceived by the crowd to help measure and demonstrate equitability (deliberate steps to minimize unintended bias).
1
Incorporate algorithms from ALICE into the AI capability to detect out-of-distribution inputs, on which the capability’s performance is not assured, to help achieve reliability (effectiveness of AI capabilities) and governability (ability to detect unintended consequences). If the AI capability preempts out-of-distribution inputs, on which its performance may exhibit unintended bias, employ ALICE to evaluate how well the capability detects out-of-distribution inputs to help measure and demonstrate equitability (deliberate steps to minimize unintended bias), reliability (security and effectiveness of AI capabilities), and governability (ability to detect unintended consequences). Apply ALICE to evaluate the AI capability’s uncertainty on in-distribution inputs to help measure and demonstrate reliability (effectiveness of AI capabilities) and governability (fulfill intended functions).
1
Incorporate algorithms from Drift Tools into the AI capability to detect out-of-distribution inputs, on which the capability’s performance is not assured, to help achieve reliability (effectiveness of AI capabilities) and governability (ability to detect unintended consequences). If the AI capability preempts out-of-distribution inputs, on which its performance may exhibit unintended bias, employ Drift Tools to evaluate how well the capability detects out-of-distribution inputs to help measure and demonstrate equitability (deliberate steps to minimize unintended bias), reliability (security and effectiveness of AI capabilities), and governability (ability to detect unintended consequences).
1
Incorporate algorithms from IBM Adversarial Robustness 360 Defenses into the AI capability to: regularize inputs to help achieve equitability (deliberate steps to minimize unintended bias). detect out-of-distribution inputs, on which the capability’s performance is not assured, to help achieve reliability (effectiveness of AI capabilities) and governability (ability to detect unintended consequences). If the AI capability preempts out-of-distribution inputs, on which its performance may exhibit unintended bias, employ IBM Adversarial Robustness 360 Defenses to evaluate how well the capability detects out-of-distribution inputs to help measure and demonstrate equitability (deliberate steps to minimize unintended bias), reliability (security and effectiveness of AI capabilities), and governability (ability to detect unintended consequences).
1
Incorporate algorithms from Python Outlier Detection (PyOD) into the AI capability to detect out-of-distribution inputs, on which the capability’s performance is not assured, to help achieve reliability (effectiveness of AI capabilities) and governability (ability to detect unintended consequences). If the AI capability preempts out-of-distribution inputs, on which its performance may exhibit unintended bias, employ Python Outlier Detection (PyOD) to evaluate how well the capability detects out-of-distribution inputs to help measure and demonstrate equitability (deliberate steps to minimize unintended bias), reliability (security and effectiveness of AI capabilities), and governability (ability to detect unintended consequences).
1
Incorporate risk mitigations recommended by the Threat Modeling Resource to help achieve reliability (security of AI capabilities). Conduct a security review, guided by the Threat Modeling Resource, to help measure and demonstrate reliability (security of AI capabilities).
1
Keep senior leaders informed with DAGR to help achieve responsibility (for the development, deployment, and use of AI capabilities) and traceability (appropriate understanding of the development processes).
1
Keep senior leaders informed with the Executive Dashboard to help achieve responsibility (for the development, deployment, and use of AI capabilities) and traceability (appropriate understanding of the development processes).
1
Learn from past experiences in the AI Incidents Database to help achieve responsibility (appropriate care), traceability (appropriate understanding of the technology and development processes), and governability (ability to avoid unintended consequences).
1
Learn from past experiences in the Failure Modes Resources to help achieve responsibility (appropriate care), traceability (appropriate understanding of the technology and development processes), and governability (ability to avoid unintended consequences).
1
Learn from past experiences in the RAI Use Case Repository to help achieve responsibility (appropriate care), traceability (appropriate understanding of the technology and development processes), and governability (ability to avoid unintended consequences).
1
Leverage the Bias Bounty Guidebook to design and perform security tests of the AI capability to help measure and demonstrate reliability (security of AI capabilities).
1
Leverage the HSI Guidebook to tailor the user interface to the intended operators to help achieve responsibility (appropriate levels of judgment) and traceability (appropriate understanding of the operational methods). tailor the user interface to the intended operators, who would disengage or deactivate if needed, to help achieve governability (ability to disengage or deactivate deployed systems).
1
Leverage the Human-Machine Teaming Systems Engineering Guide to: tailor the user interface to the intended operators to help achieve responsibility (appropriate levels of judgment) and traceability (appropriate understanding of the operational methods). tailor the user interface to the intended operators, who would disengage or deactivate if needed, to help achieve governability (ability to disengage or deactivate deployed systems).
1
Leverage the MIT-LL Red-Teaming Guidebook to tailor the user interface to the intended operators to help achieve responsibility (appropriate levels of judgment) and traceability (appropriate understanding of the operational methods). Tailor the user interface to the intended operators, who would disengage or deactivate if needed, to help achieve governability (ability to disengage or deactivate deployed systems). $[In Development for the DoD]$
1
No data
70
Put together a concept of operations, patterned after the CONOPS Examples/Templates, for how the AI capability will be used, both when it is exhibiting expected behavior and when it is not, to help achieve governability (fulfill intended functions). to help measure and demonstrate reliability (well-defined uses); if a detailed one can be constructed, then the use case is likely to be well-defined. that includes user methods for detecting unintended AI capability behavior and deactivating or disengaging the capability to help measure and demonstrate governability (ability to disengage or deactivate deployed systems).
1
Submit the use case to the Use Case Repository to help achieve traceability (transparent and auditable documentation) and reliability (explicit uses). Compare the use case to those in the Use Case repository to help measure and demonstrate reliability (well-defined uses).
1
Train the AI capability on datasets in the Domain Generalization Dataset List to: perform better on a wider array of inputs to help achieve equitability (deliberate steps to minimize unintended bias) and reliability (effectiveness of AI capabilities). better detect out-of-distribution inputs, on which the capability’s performance may exhibit unintended bias, to help achieve equitability (deliberate steps to minimize unintended bias) and governability (ability to detect unintended consequences).
1
Train the AI capability on Robustness Tools to perform better on a wider array of inputs to help achieve equitability (deliberate steps to minimize unintended bias) and reliability (effectiveness of AI capabilities). Train the AI capability on Robustness Tools to better detect out-of-distribution inputs, on which the capability’s performance may exhibit unintended bias, to help achieve equitability (deliberate steps to minimize unintended bias) and governability (ability to detect unintended consequences).
1
Train the AI capability on the Baseline Data Sets to perform better on a wider array of inputs to help achieve equitability (deliberate steps to minimize unintended bias) and reliability (effectiveness of AI capabilities). Train the AI capability on the Baseline Data Sets to better detect out-of-distribution inputs, on which the capability’s performance may exhibit unintended bias, to help achieve equitability (deliberate steps to minimize unintended bias) and governability (ability to detect unintended consequences).
1
Use Root Cause Analysis to investigate unexpected behavior to help measure and demonstrate traceability (auditable methodologies, data sources, and design procedures and documentation); if a root cause analysis can be successfully conducted, the AI capability is likely to be auditable.
1
Use the Public Affairs and Communications Toolkit to engage and maintain transparency with stakeholders to help achieve traceability (transparent and auditable methodologies).
1
Use the System Auditing Tools to explore unexpected behavior to help measure and demonstrate traceability (auditable methodologies, data sources, and design procedures and documentation); if an audit can be performed, the AI capability is likely to be auditable.
1
Status

×^
Development
10
Limited support
5
No data
14
Production
30
Static
12
Supported
1
Coding Level

×^
High
32
Low
9
None
29
Name Description
Aether Datasheet Template
Tool
Template that includes questions that dataset creators should think through and document the answers to. Enables data provenance for multiple data sources. (Additional Examples Below)
[Data/Model/System Card Templates are currently in Development for the DoD; MVP Release FY24; LOE 3.2.3 under the RAI Strategy & Implementation Pathway]
AI Incident Repository
Collection of AI incidents and failures for review and to improve future development.
[In Development; LOE 1.2.1 under the RAI Strategy & Implementation Pathway]
AI Incidents Database
Tool
-
Docs
The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems. Like similar databases in aviation and computer security, the AI Incident Database aims to learn from experience so we can prevent or mitigate bad outcomes.
ALICE
Tool
Library for out-of-distribution detection.
Armory Testbed
Tool
Testbed for running scalable evaluations of adversarial defenses. Configuration files are used to launch local or cloud instances of the Armory docker containers. Models, datasets, and evaluation scripts can be pulled from external repositories or from the baselines within this project.
Auditing tools
Tool
-
Docs
Extensible open-source toolkit to examine, report, and mitigate discrimination and bias in machine learning models alongside explanations for metrics throughout the AI application lifecycle. (See below for additional tools)
Baseline Data Sets
Tool
Curated collection of benchmark datasets that represent distribution shifts faced in the wild. In each dataset, each data point is drawn from a domain, which represents a distribution over data that is similar in some way, e.g., molecules with the same scaffold structure, or satellite images from the same region. Includes two types of distribution shifts over domains. In domain generalization, the training and test distributions comprise disjoint sets of domains, and the goal is to generalize to domains unseen during training, e.g., molecules with a new scaffold structure. In subpopulation shift, the training and test domains overlap, but their relative proportions differ. We typically assess models by their worst performance over test domains, each of which correspond to a subpopulation of interest, e.g., different geographical regions. (See below for additional resources)
Bias and Fairness Audit Tool
Tool
-
Docs
Open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers to audit machine learning models for discrimination and bias, and to make informed and equitable decisions around developing and deploying predictive tools.
Bias Bounty Guidebook
Document establishing guidance on bias bounty programs.
[In Development for the DoD]
Carbon Emissions Calculator
Tool
-
Docs
Tool to estimate model carbon emissions
Cognitive Biases Resource
Tool
Infographic with 50 cognitive bases and explanations.
CONOPS Examples/Templates
Tool
1 page PDF showing an example of Concepts of Operations in the context of a helicopter.
CounterFit
Tool
CLI that provides a generic automation layer for assessing the security of ML models
Crowdsourcing Model Bias Tools
Tool
Paper with algorithm for enumerating biases in word embeddings.
DAGR
Tool
Document about risks related to AI and how to calculate and mitigate risks.
[In Development for the DoD; MVP Available in Appendix 1; LOE 2.1.7 under the RAI Strategy & Implementation Pathway]
Design Kit Inspiration Methods
Tool
Step-by-step guide to help the design process.
DIU Worksheets
Tool
Worksheet to help guide thinking and surface potential issues sooner, rather than later, to avoid unintended consequences in creating AI systems.
Diverse Counterfactual Explanations (DiCE)
Tool
-
Docs
Implements counterfactual (CF) explanations that provide information by showing feature-perturbed versions of the same scenario. Provides "what-if"explanations for model output and can be a useful complement to other explanation methods, both for end-users and model developers.
Domain Generalization Dataset List
Tool
Paper on domain generalization (DG). Contains a comprehensive literature review and formally defining DG, a review into existing methods and theories, and additional insights/discussions.
Drift Tools
Tool
-
Docs
Open-source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection.
EQUI(NE2)
Tool
Library for uncertainty quantification.
Executive Dashboard
Senior-level dashboards for clear understanding of RAI goals of a project.
[In Development for the DoD]
ExplainerDashboard
Tool
-
Docs
Used to deploy a dashboard web app that explains the workings of a (scikit-learn compatible) machine learning model. The dashboard provides interactive plots on model performance, feature importances, feature contributions to individual predictions, "what if"analysis, partial dependence plots, SHAP (interaction) values, visualization of individual decision trees, etc.
Failure Modes Resources
Tool
-
Docs
Document to identify threats, attacks, vulnerabilities and use the framework to plan for countermeasures. Also organizes ML failure modes and presents a framework to analyze key issues.
FairML
Tool
Tool for auditing machine learning (ML) for bias.
Feature Release Rollback Resource
Tool
Article on reliability of releases and deploying rollbacks.
FoolBox
Tool
-
Docs
Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX
Framework for Ethical Decision Making
Tool
-
Docs
Document designed as an introduction to thinking ethically and framework to aid in decision making.
GameChanger Policy Database
Tool
-
Docs
Searchable database of over 50,000 DOD policy documents.
[Developed for the DoD]
HSI Guidebook
Docs
Paper with framework evaluating suitability and effectiveness, identifies teaming concepts, emphasizes the importance of interaction, and provides a structure for identifying and selecting appropriate measures to evaluate team effectiveness. (See below for additional resources)
[In Development for the DoD]
Hugging Face Data Card Template
Tool
Instructions on creating a dataset card. A dataset card can promote responsible usage and inform users of any potential biases within the dataset. Dataset cards help users understand a dataset’s contents, the context for using the dataset, how it was created, and any other considerations a user should be aware of.
Hugging Face Model Card Template
Tool
Tool, annotated template and resources to aid in model card creation. Model cards are a documentation framework for understanding, sharing, and improving machine learning models.
Human Bias Red-Teaming Toolkit
Set of tools that helps reduce human bias in AI projects. (See below for additional resources)
[In Development for the DoD; LOE 2.1.5 under the RAI Strategy & Implementation Pathway]
Human-Machine Teaming Systems Engineering Guide
Tool
Guide to help system developers design autonomy and automation that works in partnership with the human operator.
IBM Adversarial Robustness 360 Attacks
Tool
-
Docs
Python library for Machine Learning Security. ART is hosted by the Linux Foundation AI & Data Foundation (LF AI & Data). ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition, generation, certification, etc.).
IBM Adversarial Robustness 360 Defenses
Tool
-
Docs
Python library for Machine Learning Security. ART is hosted by the Linux Foundation AI & Data Foundation (LF AI & Data). ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition, generation, certification, etc.).
IBM Adversarial Robustness 360 Estimators
Tool
-
Docs
Python library for Machine Learning Security. ART is hosted by the Linux Foundation AI & Data Foundation (LF AI & Data). ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference. ART supports all popular machine learning frameworks (TensorFlow, Keras, PyTorch, MXNet, scikit-learn, XGBoost, LightGBM, CatBoost, GPy, etc.), all data types (images, tables, audio, video, etc.) and machine learning tasks (classification, object detection, speech recognition, generation, certification, etc.).
IBM Data Privacy Toolkit
Tool
-
Docs
Toolkit for data type identification, privacy risk assessment, data masking and data anonymization that is exposed as a Java/Scala library and as a REST API. The toolkit consists of four main components: Type identification, Masking providers, Privacy risk assessment, Anonymization providers
IBM Explainability 360
Tool
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Docs
Extensible open source toolkit can help you comprehend how machine learning models predict labels by various means throughout the AI application lifecycle.
IBM Uncertainty Quantification 360
Tool
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Open-source toolkit to estimate, communicate and use uncertainty in machine learning model predictions.
Impact Assessment Tools
Tool
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Docs
Template for determining impact, including ethical considerations, impact identification and scenarios, and potential harm analysis. Originally created for the NHS.
Impact Goals Tool
Tool
Set of tools to help understand a problem and its impacts. Includes Cognitive walkthrough, Contextual inquiry, Design studio, Dot voting, Five whys, Heuristic evaluation, Hopes and fears, KJ method, Lean coffee, Stakeholder and user interviews, and Stakeholder influence mapping.
Incident Response Guidance
Interactive web application for end-user auditing
[In Development for the DoD; LOE 1.1.6 under the RAI Strategy & Implementation Pathway]
InterpretML
Tool
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Docs
Open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. Helps train interpretable glassbox models and explain blackbox systems. Helps users understand model's global behavior, or understand the reasons behind individual predictions.
LIME
Tool
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Local interpretable model-agnostic explanations. Used for explaining what machine learning classifiers (or models) are doing. Supports explaining individual predictions for text classifiers or classifiers that act on tables (NumPy arrays of numerical or categorical data) or images.
Machine Learning Suitability Checklist
Tool
Checklist of 10 questions for clinicians can ask about an algorithm, but which do not require users to have knowledge of complex statistical and computational concepts.
Model Agnostic Language for Exploration and Explanation (DALEX)
Tool
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Xrays any model and helps to explore and explain its behavior, helps to understand how complex models are working. The main function explain() creates a wrapper around a predictive model. Wrapped models may then be explored and compared with a collection of local and global explainers.
Model Card Examples
Tool
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Two model card examples for Face Detection and Object Detection.
Public Affairs and Communications Toolkit
Tools for public affairs and communications professionals.
[In Development for the DoD]
Python Outlier Detection (PyOD)
Tool
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Python library for detecting outlier objects in multivariate data. Includes more than 40 detection algorithms. (See below for additional tools)
RAI Acquisition Toolkit
Set of tools for guiding and enabling potential customers on acquiring RAI products.
[In Development for the DoD; LOE 3.1.1 under the RAI Strategy & Implementation Pathway]
RAI Program Manager Review
Document that establishes a guide for the review of program managers of AI projects for the use of senior leadership.
[In Development for the DoD; LOE 3.2.1 under the RAI Strategy and Implementation Pathway]
RAI Use Case Repository
Collection of AI use cases.
[In Development for the DoD; LOE 1.2.2 under the RAI Strategy & Implementation Pathway]
RAI UX/HMT Toolkit
Document establishing strategies for red-teaming HMT aspects of AI systems. [In Development for the DoD]
Responsibility Flows Tool
Tool
Tool assisting organizations define and establish roles and responsibilities for AI projects.
[In Development for the DoD; see Appendix 5 for MVP]
Robustness Tools
Tool
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Docs
Dataset that contains over 1000 images of printed adversarial patches in-the-wild. It is designed to be used in conjunction with the COCO dataset and COCO-trained object detection models. Created both to study the robustness of adversarial patch attacks in real-world conditions and to enable development of defensive mechanism for object detectors. Previous studies of physical adversarial objects typically tested their attacks in digital experiments or in constrained lab-like conditions. Developed to capture adversarial patches in more realistic conditions with wide variations in position, distance, lighting conditions, and viewing angles. (See below for additional tools)
Root Cause Analysis
Tool
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Template to conduct a Root Cause Analysis. A Root Cause Analysis assists in the development of a quality improvement plan for specific areas of service delivery.
SHAP
Tool
Game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions.
ShapASH
Tool
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Docs
Python library which aims to make machine learning interpretable and understandable by everyone. It provides several types of visualization that display explicit labels that everyone can understand. Helps data scientists understand their models easily and share their results. Allows end users to understand the decision proposed by a model using a summary of the most influential criteria. Contributes to data science auditing by displaying useful information about any model and data in a unique report.
SmartNoise
Tool
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Differential privacy toolkit for analytics and machine learning. Uses differential privacy (DP) techniques to inject noise into data, to prevent disclosure of sensitive information and manage exposure risk.
Stakeholder Engagement Toolkit
Tool
Activities that offer guidance to hone design thinking skills.
Stakeholder Mapping Template
Tool
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This template helps minimize confusion on who is who, clarifies responsibilities, and catalyzes a transition from strangers to collaborators. Use this template to identify stakeholders and strategize the level of involvement that each stakeholder will have.
System Auditing Tools
Tool
Interactive web application for end-user auditing
TensorFlow Fairness Indicators
Tool
Tool designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader TensorFlow toolkit.
TensorFlow Model Remediation
Tool
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Python library that provides solutions for machine learning practitioners working to create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.
Threat Modeling Resource
Tool
Document with framework for mitigating AI/ML threats
Trust in Autonomous Systems Test
Tool
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Rubric of 9 criteria to evaluate a system by various metrics of trust by a user.
[Additional tools currently in Development for the DoD; will become available FY25; LOE 2.1.2 under the RAI Strategy & Implementation Pathway]
Use case Analysis/Workflow Mapping Tool
Tool
Walkthrough on how to develop a use case from a web design perspective.
What-If Tool
Tool
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Docs
Provides an easy-to-use interface for expanding understanding of a black-box classification or regression ML model. With the plugin, you can perform inference on a large set of examples and immediately visualize the results in a variety of ways. Additionally, examples can be edited manually or programmatically and re-run through the model in order to see the results of the changes. It contains tooling for investigating model performance and fairness over subsets of a dataset.The purpose of the tool is that give people a simple, intuitive, and powerful way to play with a trained ML model on a set of data through a visual interface.
XAI Toolkit - Saliency
Tool
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Open source, Explainable AI (XAI) framework for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications. (See Below for Additional Tools)