Ethical Principles for Artificial Intelligence from Microsoft
To realise the full benefits of AI, we’ll need to work together to find answers to these questions and create systems that people trust. Ultimately, for AI to be trustworthy, we believe that it must be “human-centred” – designed in a way that augments human ingenuity and capabilities – and that its development and deployment must be guided by ethical principles that are deeply rooted in timeless values.
At Microsoft, we believe that six principles should provide the foundation for the development and deployment of AI-powered solutions that will put humans at the centre:
Fairness: When AI systems make decisions about medical treatment or employment, for example, they should make the same recommendations for everyone with similar symptoms or qualifications. To ensure fairness, we must understand how bias can affect AI systems.
Reliability: AI systems must be designed to operate within clear parameters and undergo rigorous testing to ensure that they respond safely to unanticipated situations and do not evolve in ways that are inconsistent with original expectations. People should play a critical role in making decisions about how and when AI systems are deployed.
Privacy and security: Like other cloud technologies, AI systems must comply with privacy laws that regulate data collection, use and storage, and ensure that personal information is used in accordance with privacy standards and protected from theft.
Inclusiveness: AI solutions must address a broad range of human needs and experiences through inclusive design practices that anticipate potential barriers in products or environments that can unintentionally exclude people.
Transparency: As AI increasingly impacts people’s lives, we must provide contextual information about how AI systems operate so that people understand how decisions are made and can more easily identify potential bias, errors and unintended outcomes.
Accountability: People who design and deploy AI systems must be accountable for how their systems operate. Accountability norms for AI should draw on the experience and practices of other areas, such as healthcare and privacy, and be observed both during system design and in an ongoing manner as systems operate in the world.
