Philips AI Principles

At Philips, we are committed to ethical use of Artificial Intelligence (AI) [1]. When we design AI-enabled solutions, we strive to complement and benefit our customers, patients, and society as a whole. In addition to the Philips Data Principles on privacy, security, and beneficial use of data, we therefore embrace the following AI Principles.

Well-being

We design our solutions to benefit the health and well-being of individuals and to contribute to the sustainable development of society.

Oversight

We design AI-enabled solutions to augment and empower people, with appropriate human supervision.

Robustness

We develop AI-enabled solutions that are intended to do no harm, with appropriate protection against deliberate or inadvertent misuse.

Fairness

We develop and validate solutions using data that is representative of the target group for the intended use, and we aim to avoid bias or discrimination.

Transparency

We are transparent about which functions and features of our offerings are AI-enabled, the validation process, and the responsibility for ultimate decision-taking.

View our AI publications in scientific journals

[1] At Philips, we embrace the following, internationally recognized definition of artificial intelligence (AI) from the Organization for Economic Co-operation and Development (OECD):
“An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.”
Source: Organization for Economic Co-operation and Development (OECD), March 2024

This definition encompasses a wide range of tools, methods and subfields – including machine learning, deep learning and generative AI:

Machine learning is a subfield of AI that is focused on learning patterns from data without explicit instructions and improving over time by applying those learnings to predict patterns in new data.
Deep learning is a specific machine learning approach that is loosely modelled after the neuronal structure of the human brain. Deep learning algorithms are suitable for tasks such as speech, text, and image interpretation, and particularly (visual) pattern recognition.
Generative AI refers to a type of AI designed to create new content by leveraging patterns learned from extensive datasets. When given a prompt, generative AI is able to predict and generate new, original content based on the information it has absorbed. This technology enables the creation of diverse types of content, such as text, images, and music, reflecting its adaptability and wide range of applications.

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