Most of you might be wondering what responsible AI is, right? So, Responsible AI or Ethical AI is a governance framework that tracks and keeps documents on how a specific organization is addressing the issues around artificial intelligence from both the aspects, ethical and legal. It resolves ambiguity for where responsibility lies if something goes off track is a vital driver for responsible Artificial Intelligence initiatives. Now, as we know about what responsible Artificial Intelligence or ethical AI is. Let’s know why it is important?
Responsible AI is an upcoming area of Artificial Intelligence governance and the use of responsibility is a wide umbrella term that covers both ethics as well as democratization. So when bias is introduced into Artificial Intelligence, it makes similar decisions so to condemn such events, ethical AI plays a role here.
Principles of Responsible AI for Corporates
These principles are taken from the definitions of responsible AI or ethical Artificial Intelligence towards specific stakeholders such as corporates who are deploying, building, and buying Artificial Intelligence systems.
Line up with AI principles: As Artificial Intelligence systems have a great ability to monitor, assist, augment and perform that has an impact on its customers and society. The company has to line up with its different business units concerning principles of responsible AI, policies, and practices that it wants to adopt.
Should stick to top-down and end-to-end governance: So the human oversight needs to be applied from an end-to-end Artificial Intelligence systems lifecycle way at the same time also from the end-user and regulators to the senior executives and Board of a company.
Opt for robustness and safety: The safety of the AI systems needs to be designed taking into account the risks that are associated with an impact of the Artificial Intelligence systems. Risk tiering is a way the corporates can achieve it.
Hold control: The value here is associated with corporations and does not cater to a broad issue of human values. Similarly, the principle of control aims at ensuring when an Artificial Intelligence system gets deviated from its performance and obtaining control over it.
Adhere to privacy: Privacy is not only about the original data, it is also about decisions, insights, actions, and outcomes of the Artificial Intelligence system. Many regulations focus on this principle however organizations should consider more broadening what they “can” vs “should” do with data.
Be clear: This principle embodies the explainability, traceability, and communication of decisions, information, actions of the AI systems as well as the data that feeds the AI system, and visibility into broader systems leverage Artificial Intelligence.
Implant security: The principle of security is developed to protect users against both malicious harm and unintentional harm that can lead to poor decision-making of Artificial Intelligence.
No biases: This principle aims to combat biases, universal design, and accessibilities that may affect the users. And then facilitates the broader societal benefits of the Artificial Intelligence systems.
Maintain accountability: It addresses the key elements of responsibility as accountability, liability, and blameworthiness.
Work for well-being: It applies to the company’s focuses on social, environmental, and governance factors. It also addresses the broader governance aspects.