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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

Well-being

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

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

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

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

Contact

If you have questions regarding our AI principles, please contact us via the contact form.

[1] We embrace the following formal definition of AI (source: HLEG definition AI)
Artificial intelligence (AI) systems are software (and possibly also hardware) systems designed by humans that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data, reasoning on the knowledge, or processing the information, derived from this data and deciding the best action(s) to take to achieve the given goal. 


AI systems can either use symbolic rules or learn a numeric model, and they can also adapt their behavior by analyzing how the environment is affected by their previous actions. 
 

As a scientific discipline, AI includes several approaches and techniques, such as machine learning (of which deep learning and reinforcement learning are specific examples), machine reasoning (which includes planning, scheduling, knowledge representation and reasoning, search, and optimization), and robotics (which includes control, perception, sensors and actuators, as well as the integration of all other techniques into cyber-physical systems).

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