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

Practice holism and do not reduce our ethical focus to components

We provide integrated technologies to defend and support democracy. We do not fixate only on algorithms and data in a silo, but rather take a holistic view of the potential impact of AI on outcomes to avoid unintended consequences in the real world. We aim to ensure that the entire systems we develop have the capability to manage data quality while upholding governance around software and models. We routinely employ statistical analyses to search for unwarranted data, model, and outcome bias.

Principle: AI Ethical Principles, January 2023

Published by Rebelliondefense

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)

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)

6. We set the framework.

Our AI solutions are developed and enhanced on grounds of deep analysis and evaluation. They are transparent, auditable, fair, and fully documented. We consciously initiate the AI’s development for the best possible outcome. The essential paradigm for our AI systems’ impact analysis is “privacy und security by design”. This is accompanied e.g. by risks and chances scenarios or reliable disaster scenarios. We take great care in the initial algorithm of our own AI solutions to prevent so called “Black Boxes” and to make sure that our systems shall not unintentionally harm the users

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

Design for human control, accountability, and intended use

Humans should have ultimate control of our technology, and we strive to prevent unintended use of our products. Our user experience enforces accountability, responsible use, and transparency of consequences. We build protections into our products to detect and avoid unintended system behaviors. We achieve this through modern software engineering and rigorous testing on our entire systems including their constituent data and AI products, in isolation and in concert. Additionally, we rely on ongoing user research to help ensure that our products function as expected and can be appropriately disabled when necessary. Accountability is enforced by providing customers with insight into the provenance of data sources, methodologies, and design processes in easily understood and transparent language. Effective governance — of data, models, and software — is foundational to the ethical and accountable deployment of AI.

Published by Rebelliondefense in AI Ethical Principles, January 2023