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

Be Responsible.

Develop products responsibly and do not take advantage of your products’ users by manipulating them through AI’s vastly more predictive capabilities derived from user data.

Principle: Unity’s Guiding Principles for Ethical AI, Nov 28, 2018

Published by Unity Technologies

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)

· 7. Be made available for uses that accord with these principles.

Many technologies have multiple uses. We will work to limit potentially harmful or abusive applications. As we develop and deploy AI technologies, we will evaluate likely uses in light of the following factors: Primary purpose and use: the primary purpose and likely use of a technology and application, including how closely the solution is related to or adaptable to a harmful use Nature and uniqueness: whether we are making available technology that is unique or more generally available Scale: whether the use of this technology will have significant impact Nature of Google’s involvement: whether we are providing general purpose tools, integrating tools for customers, or developing custom solutions

Published by Google in Artificial Intelligence at Google: Our Principles, Jun 7, 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

· 2.4 Cybersecurity and Privacy

Just like technologies that have come before it, AI depends on strong cybersecurity and privacy provisions. We encourage governments to use strong, globally accepted and deployed cryptography and other security standards that enable trust and interoperability. We also promote voluntary information sharing on cyberattacks or hacks to better enable consumer protection. The tech sector incorporates strong security features into our products and services to advance trust, including using published algorithms as our default cryptography approach as they have the greatest trust among global stakeholders, and limiting access to encryption keys. Data and cybersecurity are integral to the success of AI. We believe for AI to flourish, users must trust that their personal and sensitive data is protected and handled appropriately. AI systems should use tools, including anonymized data, de identification, or aggregation to protect personally identifiable information whenever possible.

Published by Information Technology Industry Council (ITI) in AI Policy Principles, Oct 24, 2017