Responsible AI

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.

Principles of responsible AI

AI and ML models that support it should be comprehensive, explainable, ethical, and efficient.

• Comprehensive AI has extensively defined testing and governance criteria to prevent ML from getting hacked easily.
•  Explainable AI is programmed to ‘explain’ its purpose and rationale behind the decision-making process so that the average end-user can understand.
•  Ethical AI is a set of strategies that seek out and eliminate biases in ML models.
•  Efficient AI is the ability to respond quickly to changes within the operational environment.

Importance of responsible AI 

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:

•  Every step of the AI model development process should be recorded and not be altered by humans or any external factors.
•  The data used to train ML models should not be biased.
•  The organization deploying AI programming should be sensitive to AI’s potential impact.

Designing responsible AI

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:

• Shared code repositories
• Approved model architectures
• Sanctioned variables
• Established bias testing methodologies

Implementation and how it works

Today, organizations have different ways to implement responsible AI and demonstrate that they have eliminated black box AI models. Current strategies include the following:

• Make sure data is explainable in a way that a human can interpret
• Make sure design and decision-making processes are documented so that if a mistake occurs, it can be reverse-engineered to identify what happened
• Build a diverse work culture
• Promote constructive discussions to help mitigate bias
• Use interpretable latent features
• Create a rigorous development process

Best practices for responsible AI

Governance processes should be systematic and repeatable. Methods for best practices include:

• Implement machine learning best practices.
• Creating a diverse culture of support by creating gender and racially diverse teams that create responsible AI standards. 
• Organizational structures should make sure review committees are cross-functional within the organization. Encourage a culture that allows employees to speak freely on ethical concepts around AI and bias.
• Be transparent so that any decisions made by AI are explainable.
• Make work measurable because what cannot be measured cannot be monitored. Dealing with responsibility can be subjective. Measurable processes such as visibility, explainability, an auditable technical framework, or an ethical framework are fundamental.
• Use responsible AI tools to inspect AI models. Options such as explainable AI and the TensorFlow toolkit are available. Besides, perform tests such as bias testing or predictive maintenance.
• Stay mindful and learn from the process. An organization will learn more about responsible AI in implementation as they go, from fairness practices to technical references and materials surrounding technical ethics.