Machine Learning Quality Management Guideline
Japan · Guideline · Digital Architecture Research Center (DigiARC), Cyber Physical Security Research Center (CPSEC), and Artificial Intelligence Research Center (AIRC) of AIST
Our summary
The guideline establishes quality objectives for machine learning-based products and services, and offers procedural guidance for achieving quality through development process oversight and system assessments. It is a collaborative effort of three research centers within AIST and provides frameworks for managing quality in machine learning applications.
Written by the Index in our own words. It is not legal advice; the official text is the authority.
Inside this policy
Every section of this policy in order. Our notes summarise each one in plain language and do not quote the official text, which is the authority.
Drafted by the Index An automated summary of the official text, not yet reviewed by a person.Reviewed Checked by a person against the official text.
5 sections
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Publication
Machine Learning Quality Management Guideline
This guideline establishes quality objectives for machine learning-based products and services, and offers procedural guidance for achieving quality through development process management and system assessment. It was jointly developed by three research centers within AIST.
Machine Learning Quality Management Guideline
Feedback and Contacts
Acknowledgments
Controls
What this policy requires, forbids, grants or penalises, one control at a time and in our own words. Each control links to the official text, which is the authority. This is not legal advice.
Drafted by the Index Drafted automatically from the official text, not yet reviewed by a person.Reviewed Checked by a person against the official text.
Themes covered 1 of 35
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Risk and scope 0 of 4
- Risk classification, not covered
- Prohibited practices, not covered
- Risk management system, not covered
- Impact assessment, not covered
Data and privacy 0 of 4
- Data governance and quality, not covered
- Data subject rights and consent, not covered
- Cross-border data transfer, not covered
- Copyright and training data, not covered
Transparency and records 0 of 4
- Transparency and disclosure to users, not covered
- Labelling of synthetic content, not covered
- Technical documentation, not covered
- Record keeping and logging, not covered
Technical safeguards 0 of 4
- Human oversight, not covered
- Accuracy, robustness and cybersecurity, not covered
- Bias and non-discrimination, not covered
- Testing and evaluation, not covered
Assurance and lifecycle 0 of 4
- Conformity assessment and certification, not covered
- Registration and notification, not covered
- Incident and breach reporting, not covered
- Post-market monitoring and corrective action, not covered
Rights and protected groups 1 of 4
- Automated decision-making rights, not covered
- Children and vulnerable groups, not covered
- Biometric identification and surveillance, not covered
Roles and organisation 0 of 6
- Supply chain and provider-deployer duties, not covered
- General purpose and foundation models, not covered
- Accountability and governance roles, not covered
- Training and AI literacy, not covered
- Public sector use and procurement, not covered
- Innovation support and sandboxes, not covered
Enforcement and sectors 0 of 5
- Enforcement, penalties and regulators, not covered
- Sector rules: health, not covered
- Sector rules: finance and insurance, not covered
- Sector rules: employment, not covered
- Sector rules: education, not covered
The full list of controls could not be loaded, so only the first 1 are shown.
Feedback and Contacts 1 control
- Applies to: AnyoneDrafted by the Index
Users should provide feedback on the guideline contents through the designated feedback form.
Change history
No changes recorded since we began monitoring this policy.