Be a trusted steward of the data and insights

Principle: GE Healthcare AI principles, Oct 1, 2018 (unconfirmed)

Published by GE Healthcare

Related Principles

2. Data and insights belong to their creator

IBM clients’ data is their data, and their insights are their insights. Client data and the insights produced on IBM’s cloud or from IBM’s AI are owned by IBM’s clients. We believe that government data policies should be fair and equitable and prioritize openness.

Published by IBM in Principles for Trust and Transparency, May 30, 2018

· Prepare Input Data:

1 The exercise of data procurement, management, and organization should uphold the legal frameworks and standards of data privacy. Data privacy and security protect information from a wide range of threats. 2 The confidentiality of data ensures that information is accessible only to those who are authorized to access the information and that there are specific controls that manage the delegation of authority. 3 Designers and engineers of the AI system must exhibit the appropriate levels of integrity to safeguard the accuracy and completeness of information and processing methods to ensure that the privacy and security legal framework and standards are followed. They should also ensure that the availability and storage of data are protected through suitable security database systems. 4 All processed data should be classified to ensure that it receives the appropriate level of protection in accordance with its sensitivity or security classification and that AI system developers and owners are aware of the classification or sensitivity of the information they are handling and the associated requirements to keep it secure. All data shall be classified in terms of business requirements, criticality, and sensitivity in order to prevent unauthorized disclosure or modification. Data classification should be conducted in a contextual manner that does not result in the inference of personal information. Furthermore, de identification mechanisms should be employed based on data classification as well as requirements relating to data protection laws. 5 Data backups and archiving actions should be taken in this stage to align with business continuity, disaster recovery and risk mitigation policies.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· Prepare Input Data:

1 The processes and policies that govern data management should be followed when preparing the categorization and structuring of data that will feed into the AI system. 2 The data pertaining to the social and environmental topics should be accessible to the public data infrastructure and must clearly articulate the social benefit of the data presented.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· Prepare Input Data:

1 An important aspect of the Accountability and Responsibility principle during Prepare Input Data step in the AI System Lifecycle is data quality as it affects the outcome of the AI model and decisions accordingly. It is, therefore, important to do necessary data quality checks, clean data and ensure the integrity of the data in order to get accurate results and capture intended behavior in supervised and unsupervised models. 2 Data sets should be approved and signed off before commencing with developing the AI model. Furthermore, the data should be cleansed from societal biases. In parallel with the fairness principle, the sensitive features should not be included in the model data. In the event that sensitive features need to be included, the rationale or trade off behind the decision for such inclusion should be clearly explained. The data preparation process and data quality checks should be documented and validated by responsible parties. 3 The documentation of the process is necessary for auditing and risk mitigation. Data must be properly acquired, classified, processed, and accessible to ease human intervention and control at later stages when needed.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· ⑦ Data Management

Data, such as personal information, should not be used for purposes other than its intended use. Throughout the entire process of data collection and utilization, data quality and risks should be carefully managed so as to minimize data bias.

Published by The Ministry of Science and ICT (MSIT) and the Korea Information Society Development Institute (KISDI) in National AI Ethical Guidelines, Dec 23, 2020