Why Responsible AI ?
Responsible AI refers to developing and deploying artificial intelligence (AI) systems that are ethical, trustworthy, and accountable. It involves designing AI systems that are fair, transparent, secure, and inclusive and that consider the potential impacts on society and the environment. Responsible AI also consists of adhering to legal and ethical standards and ensuring that individuals and organizations are accountable for developing and using AI systems. Responsible AI aims to promote the responsible and sustainable use of AI technology to benefit individuals, organizations, and society.
The principles of responsible AI are:
Fairness
Reliability and safety
Privacy and security
Inclusiveness
Transparency
Accountability
Fairness
Fairness is the idea that AI systems should treat all people fairly. The idea here is that data and people can have biases, and, as much as possible, we want to limit those in AI systems.
An example of how fairness should be applied is you have an application that uses AI and machine learning to approve or reject loan applications. You should ensure the application doesn’t discriminate based on gender, ethnicity, disabilities, or other factors.
Reliability and safety
With this principle, we need to ensure that AI systems perform reliably. This means they can handle errors or values it doesn’t expect, operate how they were initially designed, and resist manipulation. To ensure we have a reliable and safe application, perform rigorous testing. Now take self-driving cars, for example. We have to ensure they are reliable and safe. If they get a value or condition, they don’t understand that they don’t do something drastic to cause harm to human life. Another example of when reliability and safety are essential is an AI system that diagnoses patients or prescribes medication. Making sure the system operates how it was designed and being able to handle abnormal conditions or values is critical.
Privacy and security
This principle is all about keeping AI systems and data secure and private. We must secure data so that user data is not leaked or disclosed. Take a medical AI system, for example; it will access confidential patient records, so keeping data private and secure is paramount. In addition, another way to look at this principle is by providing customers with information and controls regarding how the system stores and uses their data.
Inclusiveness
And with this principle, we should build AI systems that empower all people, no matter their physical abilities or disabilities, gender, race, ethnicity, or anything else. The idea behind this principle is to make sure all users can use the AI system. An example of how this can be applied is by making an AI system that can be used with voice control to enable someone with a disability to use it or creating an AI system that can read off the results if the user is blind. Now, let’s look at transparency. As these AI systems make decisions, we should be fully aware of how it works, why it works, and their limitations. So, for example, if you have a program that detects water damage to a wall from a smartphone camera, you need to state and make users aware of any limitations, such as this has been tested in fully lighted situations against drywall and plywood, you should always consult a professional inspector if you suspect any damage. This will let your users know how you tested the AI system thoroughly. Another example would be providing documentation to your AI system, outlining how and why it works. If you do that, your users will be able to understand precisely how the system works and be aware of its limitations.
Accountability
Recognizes that people should be accountable for AI systems. In addition, AI systems should follow governance and frameworks to meet legal and ethical standards. An example of this principle would be in the area of facial recognition. It can be a fantastic tool and a great way to solve many issues, improve security, etc. But if facial recognition is misused without following legal or ethical standards, it could have many profound implications regarding privacy and human rights.
Summary
I began by prioritizing fairness, ensuring that our applications do not exhibit biases based on characteristics such as gender, race, or disabilities. We then focused on the principles of reliability and safety, ensuring that our systems and applications operate accurately and can handle errors or invalid information without compromising safety. These six principles are fundamental to Microsoft’s approach to responsible AI. Moving forward, we delved into the importance of privacy and security, safeguarding our systems and data to maintain privacy. Inclusiveness was also a key consideration. We strived to make our systems and software accessible to everyone, irrespective of gender, abilities, etc. We then emphasized the importance of transparency, ensuring that users understand precisely how an AI system functions and its limitations. Finally, we prioritized accountability, establishing a framework to ensure that individuals responsible for AI systems follow governance and ethical standards.