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Planning and Design
- Requirement 01 Risk management plan for AI system and execution of the plan
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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
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Data Collection and Processing
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Requirement 04 Provision of detailed information for data utilization
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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?
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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
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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?
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Requirement 04 Provision of detailed information for data utilization
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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
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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?
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System Development
- Requirement 11 Removal of potential bias in the implementation of the AI system
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Requirement 12 Safe mode of AI system and establishment of a process for notification of problems
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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?
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12-1 Have you implemented a safe mode that can respond to problems such as attacks, low performance, and social issues?
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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?
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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?
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Operation and Monitoring
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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?
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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
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Requirement 14 Development of a plan to test trustworthiness in the AI system
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04-1aHave you explained the data attributes before and after cleansing?• Data cleansing is a stage where data are selected and processed to create training data before labeling. The attributes of raw data cannot be identified clearly if a user uses cleansed data only. Therefore, explaining information related to cleansing and the attributes before and after cleansing will enhance the understanding on raw and cleansed data and contribute to developing a more trustworthy AI model.
• Check if an open dataset provides information that can identify the attributes of raw data. Identify the attributes of raw data by referring to the information provided with the dataset and document materials that describe the data attributes before and after cleansing in case additional data needs to be collected in the future.
• If a company has collected raw data by itself, prepare related documents and provide information on the purpose of data collection, data to be collected, data collection environment, and subject based on the institution’s purpose of use to assist in understanding raw data. The following are examples of explanations about raw data attributes before cleansing:✓ The purpose of data collection: Use of civil petitions for policies through Korean conversation modeling;
✓ Subject: Individuals and relationships, healthcare and beauty, eating habits, residence and lifestyles, work, current affairs, economics, education, childbirth and parenting, leisure, shopping, events, etc.;
✓ Data to be collected: Nationality, gender, age group, area of residence, etc.; and
✓ Data collection environment: Collection method/date/time, number of participants, etc.
• Identify and remove any unnecessary information and personal data from the raw data to build training datasets using the collected raw data. The following are examples of explanations about raw data attributes after cleansing:✓ Selection and exclusion of data: Exclude inappropriate, factitious, and unnecessary information (e.g. emojis, emotions);
✓ Data processing: Explain about processing the data provided by public institutions, de-identifying personal data, processing emojis, etc.;
✓ Statistical explanation: Includes the number of data per subject and the number of data per the collected data; and
✓ Cleansed information: Includes update and cleansing cycles and rollback information.
• The “unnecessary” information in the above examples, however, can also be “necessary” attributes when examining the positive or negative responses to a specific subject. Considering this matter, it is important to establish cleansing standards appropriate to the objectives.