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

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.

Principle: ADP: Ethics in Artificial Intelligence, 2018 (unconfirmed)

Published by ADP

Related Principles

1. Accountability and Transparency

o ADP believes that human oversight is core to providing reliable ML results. We have implemented audit and risk assessments to test our models as the baseline of our oversight methodologies. We continue to actively monitor and improve our models and systems to ensure that changes in the underlying data or model conditions do not inappropriately affect the desired results. o ADP provides information as to how we handle personal data in the relevant privacy statement that is made available to our clients’ employees, consumers or job applicants.

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

3. We put our customers first.

We enrich and simplify our customers’ lives. If an AI system or the usage of customer related data helps us to benefit our customers, we embrace this opportunity to meet their demands and expectations. The aggregation and use of customer data – especially in AI systems – shall always be clear and serve a useful purpose towards our customers. Systems and processes that support in the background are as important as services that interact with our customers directly

Published by Deutsche Telekom in Deutsche Telekom’s guidelines for artificial intelligence, May 11, 2018

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.

Published by Deutsche Telekom in Deutsche Telekom’s guidelines for artificial intelligence, May 11, 2018

· 10. Transparency

Transparency concerns the reduction of information asymmetry. Explainability – as a form of transparency – entails the capability to describe, inspect and reproduce the mechanisms through which AI systems make decisions and learn to adapt to their environments, as well as the provenance and dynamics of the data that is used and created by the system. Being explicit and open about choices and decisions concerning data sources, development processes, and stakeholders should be required from all models that use human data or affect human beings or can have other morally signiﬁcant impact.

Published by The European Commission’s High-Level Expert Group on Artificial Intelligence in Draft Ethics Guidelines for Trustworthy AI, Dec 18, 2018

2. Transparency

For cognitive systems to fulfill their world changing potential, it is vital that people have confidence in their recommendations, judgments and uses. Therefore, the IBM company will make clear: When and for what purposes AI is being applied in the cognitive solutions we develop and deploy. The major sources of data and expertise that inform the insights of cognitive solutions, as well as the methods used to train those systems and solutions. The principle that clients own their own business models and intellectual property and that they can use AI and cognitive systems to enhance the advantages they have built, often through years of experience. We will work with our clients to protect their data and insights, and will encourage our clients, partners and industry colleagues to adopt similar practices.

Published by IBM in Principles for the Cognitive Era, Jan 17, 2017