-
Planning and Design
- Requirement 01 Risk management plan for AI system and execution of the plan
-
Requirement 02 Organization of an AI governance system
- 02-1 Have you established guidelines and policies on AI governance?
- 02-2 Have you formed an AI governance group and reviewed the composition of the group?
- 02-3 Is the AI governance being supervised to ensure proper implementation?
- 02-4 Have you analyzed risk factors that may arise throughout the life cycle of the AI system?
- Requirement 03 Development of a plan to test trustworthiness in the AI system
-
Data Collection and Processing
-
Requirement 04 Provision of detailed information for data utilization
-
04-1 Is there detailed information to support the accurate comprehension and utilization of data?
- 04-1a Have you explained the data attributes before and after cleansing?
- 04-1b Have you sorted data into training data and metadata and is there a specification document for each of them?
- 04-1c Have you explained the reason for selecting the protected attributes and whether they were reflected?
- 04-1d Were data labelers trained and have you provided them with work instructions?
- 04-2 Is data provenance documented and managed?
-
04-1 Is there detailed information to support the accurate comprehension and utilization of data?
- Requirement 05 Inspection of abnormal data to ensure data robustness
-
Requirement 06 Removal of bias in the collected and processed training data
- 06-1 Have you prepared measures to mitigate bias due to human and physical elements in data collection?
- 06-2 Have you analyzed features used in training and prepared selection criteria?
- 06-3 Have you checked and prevented potential biases in data labeling?
- 06-4 Have you conducted sampling to prevent bias in data collection?
-
Requirement 04 Provision of detailed information for data utilization
-
AI Model Development
- Requirement 07 Ensuring security and compatibility of the open-source library
- Requirement 08 Removal of bias in the AI model
- Requirement 09 Establishment of defensive measures for AI model attacks
-
Requirement 10 Explanation of AI model specifications and the inference results
- 10-1 Do you provide evidence for users to accept the generation process of the model’s inference results?
- 10-2 Have you transparently provided the specification of the model on the AI model specification document?
- 10-3 When needed, do you provide an explanation about the inference results of the AI model?
-
System Development
- Requirement 11 Removal of potential bias in the implementation of the AI system
-
Requirement 12 Safe mode of AI system and establishment of a process for notification of problems
-
12-1 Have you implemented a safe mode that can respond to problems such as attacks, low performance, and social issues?
- 12-1a Have you prepared an exception handling policy for such problems?
- 12-1b Have you implemented a security technique to reinforce the security of the AI system?
- 12-1c Have you considered human intervention if there is a significant ripple effect and high uncertainty due to the AI system’s decision-making?
- 12-1d Are guidance and action on handling expected user error provided?
- 12-2 Does the system perform the function of alerting the operator if a problem occurs in the AI system?
-
12-1 Have you implemented a safe mode that can respond to problems such as attacks, low performance, and social issues?
-
Requirement 13 Improvement of users’ comprehension of the explanation of the AI system
- 13-1 Have you analyzed user characteristics and constraints in the AI system?
-
13-2 Have you provided a thorough explanation based on user characteristics?
- 13-2a Have you established criteria for the evaluation of explanation according to user characteristics?
- 13-2b Have you refrained from using technical terms that are difficult for users to understand?
- 13-2c Have you used accurate expressions to lead users to specific behaviors and comprehension?
- 13-2d Are the location and timing where an explanation is needed appropriate?
- 13-2e Have you utilized various user survey techniques to evaluate user experience?
-
Operation and Monitoring
-
Requirement 14 Development of a plan to test trustworthiness in the AI system
- 14-1 Have you established measures to track the AI system’s decision-making?
-
14-2 Have you obtained the modification history of training data and managed the impact of data modifications?
- 14-2a Have you prepared measures to track the data flow and lineage?
- 14-2b Have you developed measures to monitor modifications in the data source?
- 14-2c Have you managed the versions during data change?
- 14-2d Do you provide information to stakeholders when data change?
- 14-2e When new data have been collected, do you reevaluate the performance of the AI model?
- Requirement 15 Explanation about the scope of services provided and the subject of interactions
-
Requirement 14 Development of a plan to test trustworthiness in the AI system
-
10-1Do you provide evidence for users to accept the generation process of the model’s inference results?Determine applicability: Consider this question if you intend to provide an explanation of the result or cause of the system operation with an AI algorithm or model to ensure the trustworthiness of the public service system, and determine if the requirement has been satisfied.
• System users must be able to understand how the AI model made such an inference in order for them to trust the AI model’s inference result and the AI system’s operation. It is ideal to provide users with an explanation and evidence.
• Explanations about the outputs of the model should be taken into account, as the public service can lead to results of public benefits, convenience, and beneficiary criteria. For instance, in terms of the eligibility of an user for welfare, you must consider giving an explanation of which user’s information greatly affected the result.
• Also consider providing an explanation about the model’s inference result if the system does not operate properly or needs information for an upgrade. Below are examples of model output and utilization you can consider to improve the model’s performance internally.✔ Image and speech processing in an AI interview system: Input data and other information (e.g. background of the video, location of the interviewer in the video) to be used in the analysis for performance improvement when the predictive value of the processing result is a low confidence value
✔ Image processing in an illegal dumping detection model: Input data and other information (e.g. complexity of buildings, undetected, misdetection time, weather) to be used in the analysis for performance improvement when the dumping detection result and vice versa is a low confidence value
• Review diverse studies, trials, and implementations of explainable AI (XAI) for AI systems that can be utilized in public services.
• You may also review classical decision tree methods or causal learning technologies currently being researched to implement them in areas unexplainable with XAI technologies.
• After reviewing the applicability of XAI, stick to "10-1a" if the application is feasible, and if application is challenging, refer to "10-1b."