Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

5. We are secure.

Data security is a prime quality of Deutsche Telekom. In order to maintain this asset, we ensure that our security measures are up to date while having a full overview of how customer related data is used and who has access to which kind of data. We never process privacy relevant data without legal permission. This policy applies to our AI systems just as much as it does to all of our activities. Additionally, we limit the usage to appropriate use cases and thoroughly secure our systems to obstruct external access and ensure data privacy.

Principle: Deutsche Telekom’s guidelines for artificial intelligence, May 11, 2018

Published by Deutsche Telekom

Related Principles

2. Privacy Principles Privacy by Design

o We have implemented an enterprise wide Privacy by Design approach that incorporates privacy and data security into our ML and associated data processing systems. Our ML models seek to minimize access to identifiable information to ensure we are using only the personal data we need to generate insights. ADP is committed to providing individuals with a reasonable opportunity to examine their own personal data and to update it if it is incorrect.

Published by ADP in ADP: Ethics in Artificial Intelligence, 2018 (unconfirmed)

4. Data Governance

o Understanding how we use data, and the sources from which we obtain it, are key to our AI and ML principles. We maintain processes and systems to track and manage our data usage and retention from across ADP systems or processes. If we use external information in our models, such as government reports or industry terminologies, we understand the processes and impact of that information in our models. All data included in our ML models is monitored for changes that could alter the desired outcomes.

Published by ADP in ADP: Ethics in Artificial Intelligence, 2018 (unconfirmed)

5. Privacy and Data Governance

AI systems should have proper mechanisms in place to ensure data privacy and protection and maintain and protect the quality and integrity of data throughout their entire lifecycle. Data protocols need to be set up to govern who can access data and when data can be accessed. Data privacy and protection should be respected and upheld during the design, development, and deployment of AI systems. The way data is collected, stored, generated, and deleted throughout the AI system lifecycle must comply with applicable data protection laws, data governance legislation, and ethical principles. Some data protection and privacy laws in ASEAN include Malaysia’s Personal Data Protection Act 2010, the Philippines’ Data Privacy Act of 2012, Singapore’s Personal Data Protection Act 2012, Thailand’s Personal Data Protection Act 2019, Indonesia’s Personal Data Protection Law 2022, and Vietnam’s Personal Data Protection Decree 2023. Organisations should be transparent about their data collection practices, including the types of data collected, how it is used, and who has access to it. Organisations should ensure that necessary consent is obtained from individuals before collecting, using, or disclosing personal data for AI development and deployment, or otherwise have appropriate legal basis to collect, use or disclose personal data without consent. Unnecessary or irrelevant data should not be gathered to prevent potential misuse. Data protection and governance frameworks should be set up and adhered to by developers and deployers of AI systems. These frameworks should also be periodically reviewed and updated in accordance with applicable privacy and data protection laws. For example, data protection impact assessments (DPIA) help organisations determine how data processing systems, procedures, or technologies affect individuals’ privacy and eliminate risks that might violate compliance7. However, it is important to note that DPIAs are much narrower in scope than an overall impact assessment for use of AI systems and are not sufficient as an AI risk assessment. Other components will need to be considered for a full assessment of risks associated with AI systems. Developers and deployers of AI systems should also incorporate a privacy by design principle when developing and deploying AI systems. Privacy by design is an approach that embeds privacy in every stage of the system development lifecycle. Data privacy is essential in gaining the public’s trust in technological advances. Another consideration is investing in privacy enhancing technologies to preserve privacy while allowing personal data to be used for innovation. Privacy enhancing technologies include, but are not limited to, differential privacy, where small changes are made to raw data to securely de identify inputs without having a significant impact on the results of the AI system, and zero knowledge proofs (ZKP), where ZKP hide the underlying data and answer simple questions about whether something is true or false without revealing additional information

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2024

Encode privacy into technology

We encode privacy protections and adhere to the principle of least privilege in our products, so that users only have access to data that they absolutely need to complete their specific task. We treat misuse and violations as product failure. Compliance with the applicable legal frameworks governing privacy is a basic tenet that guides our product development.

Published by Rebelliondefense in AI Ethical Principles, January 2023

· 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