Responsible AI is a framework that registers how an organization is addressing the challenges around artificial intelligence (AI) from an ethical and legal perspective. An essential driver for responsible AI initiatives is to resolve uncertainty for where responsibility lies if anything goes wrong.
Until now, the development of acceptable AI standards is left to the wisdom of the data scientists and software developers who write AI algorithms and deploy AI models. It means, steps needed to prevent discrimination and promote transparency differs from one company to another.
Advocates of responsible AI hope that a widely adopted framework of AI governance and best practices will make it easier for companies to make sure their AI is human-centered, interpretable, and explainable.
In large companies, the chief analytics officer (CAO) is responsible for developing, implementing, and monitoring the organization’s Responsible AI governance framework. The information is usually documented on the company’s website, explaining in simple language how the company is addressing accountability to make sure that the use of AI is anti-discriminatory.
AI and ML models that support it should be comprehensive, explainable, ethical, and efficient.
The heads of Microsoft and Google have publicly called for AI regulations. However, as of today, there are no standards for accountability in AI or its unintended consequences. Most often, bias can be introduced into AI through the data used to train the machine learning (ML) models. If the training data is biased, the programming decisions will also tend to be biased.
An essential goal of responsible AI is to reduce the risk that a minor change in an input’s weight will drastically change the ML’s output.
Therefore, responsible AI should be:
Designing a responsible AI governance framework is a lot of work that requires ongoing scrutiny to make sure the organization is committed to providing an unbiased, trustworthy AI. Hence, an organization should have a maturity model to adhere to when creating and implementing an AI system.
Build AI with resources and technology according to a company-wide development standard that requires the use of:
Today, organizations have different ways to implement responsible AI and demonstrate that they have eliminated black box AI models. Current strategies include the following:
Governance processes should be systematic and repeatable. Methods for best practices include:
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