When software is powered by AI, we must build it with care. AI can impact real lives. So, engineers and teams should follow clear principles to keep AI safe, fair, and trustworthy.

Here are 𝘀𝗶𝗺𝗽𝗹𝗲 𝗮𝗻𝗱 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀:

1. 𝗙𝗮𝗶𝗿𝗻𝗲𝘀𝘀 – AI should treat everyone equally.
   𝗘𝘅𝗮𝗺𝗽𝗹𝗲: A loan approval system must not be biased by gender or race.

2. 𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗦𝗮𝗳𝗲𝘁𝘆 – AI must work safely and give consistent results.
   𝗘𝘅𝗮𝗺𝗽𝗹𝗲: A medical diagnosis tool or self-driving car should never fail in critical situations.

3. 𝗣𝗿𝗶𝘃𝗮𝗰𝘆 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 – AI should protect users’ personal data.
   𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Data used to train or run models must be secure and handled properly.

4. 𝗜𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗲𝗻𝗲𝘀𝘀 – AI should benefit everyone, regardless of ability, background, or identity.
   𝗧𝗶𝗽: Involve diverse users and testers in the design and testing stages.

5. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 – People should understand how AI works and what it can or cannot do.
   𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Show users what data the AI uses, and how confident it is in its decisions.

6. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 – Humans are responsible for AI outcomes.
   Engineers and organizations must own the design, logic, and impact of the AI systems they create.

These principles guide teams at Microsoft and many other companies to build 𝗔𝗜 𝘁𝗵𝗮𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗰𝗮𝗻 𝘁𝗿𝘂𝘀𝘁.

𝗪𝗵𝗶𝗰𝗵 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲 𝗱𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝘀 𝗺𝗼𝘀𝘁 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜—𝗮𝗻𝗱 𝘄𝗵𝘆?