Philips AI Principles

At Philips, we are committed to responsible use of Artificial Intelligence (AI) [1]. When we design, develop, deploy and monitor AI-enabled solutions, we strive to complement andbenefit our customers, patients, and society. This also includes responsible data use. We therefore embrace the following AI Principles:

Well-being

We design our AI-enabled solutions to benefit the health and well-being of individuals.

Human oversight

We design AI-enabled solutions to augment and empower people, with appropriate human supervision ensuring that critical decisions affecting people remain reviewable, overridable, and accountable to qualified professionals.

Safety

We develop AI-enabled solutions that are robust with protection against potential harm.

Fairness

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

Transparency

We are transparent about which functions and features of our offerings are AI-enabled, their capabilities and limitations.

Security

We protect our AI-enabled solutions against vulnerabilities and mitigate risks.

Privacy

We handle all personal data with integrity and respect the rights of individuals.

Sustainability

With our AI-enabled solutions we pursue long-term value and sustainable development for people and planet.

[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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