AI Governance: Managing the Power of AI Effectively and Responsibly

In today’s environment, Artificial Intelligence (AI) is key to automating, accelerating and enhancing key business processes to help transform at scale and drive value. The AI revolution has already arrived. Now question is no longer whether AI will fit into your business, but rather how you can realign your organization to fully capitalize potential of artificial intelligence and become an AI-first enterprise. Transforming into an AI-first enterprise requires organizations to think like leaders. It means thinking bigger and embedding data-driven technology broadly throughout the entire enterprise. In this article we will talk about the real-world implications of AI including challenges and opportunities related to AI Governance.

What is AI Governance?

• In recent years, we have seen revitalized focus on the role technology and AI plays across the environmental, social, and governance landscape. This includes AI use cases and applications in healthcare, education, law enforcement, and financial services among others.
• The process of setting policies and establishing accountability to drive the development and deployment of AI systems in an organization is known as AI governance.
• It is a broad framework that oversees an organization’s use of artificial intelligence using a variety of processes, approaches, and tools.

Difference between AI Regulation and AI Governance:

• AI regulation refers to the laws and rules governing AI that are enacted by a government or regulator and apply to all organizations that fall under its jurisdiction.
• AI governance relates to how AI is controlled within a company.

The Need for AI Governance:

• When AI is used incorrectly, it can expose a company to operational, financial, regulatory, and reputational hazards. Because of the unique nature of AI, safeguards must be put in place to ensure that it functions as intended.
• Software engineers and ML/ DL professionals aren’t the only ones responsible for AI governance. It is multidisciplinary, with both technical and non-technical stakeholders participating.
• It comprises how an organization should adopt AI ethics principles and ensures responsible use of AI. AI governance frameworks help organizations learn, govern, monitor, and mature AI adoption.

Five Principles of AI Governance:

• Explainability Standards: Having an explanation for why an AI system behaves in a certain way can be a big help in boosting people’s confidence and trust in the accuracy and appropriateness of its predictions.
• Fairness Appraisal: Unfair stereotypes and negative associations embedded in algorithmic systems (deliberately or otherwise) can cause or amplify serious and lasting harm. Deciding which to use requires ethical reasoning and is very context specific.
• Safety Considerations: It is essential to take precautions against both accidental and deliberate misuse of AI with risks to safety. Companies must think carefully upfront about the kind of problems and attacks that their AI system is likely to face and their consequences and continue to monitor the threat and update systems accordingly.
• Human-AI Collaboration: It advocates to include people at one or more points in the decision-making process of an otherwise automated system. Generally, guidance would be useful about the extent to which people should be able to switch off an AI system to which they have previously chosen to delegate a task.
• Liability Frameworks: No matter how complex the AI system, it must be persons or organizations who are ultimately responsible for the actions of AI systems within their design or control. It is not appropriate for moral or legal responsibility to be shifted to a machine.

Strategic Importance of a good AI Governance policy:

• Organizations that effectively implement all components of AI/ML model governance can achieve a fine-grained level of control and visibility into how models operate in production while unlocking operational efficiencies that help them achieve more with their AI investments.
• By tracking, documenting, monitoring, versioning, and controlling access to all models, these organizations can closely control model inputs and understand all the variables that might affect their results.

Questions related to AI Governance: The idea of governing algorithms involves guidelines or laws, but it is not strictly legal. The word “governance” to refer to any means of controlling how the algorithms work with people and others. Some frequently asked questions related to Artificial Intelligence Governance are:

• Who can train the model?
• Who decides which data is included in the training set?
• Are there any rules on which data can be included?
• Who can examine the model after training?
• When can the model be adjusted and retrained?
• How can the model be tested for bias?
• Are there any biases that must be defended against?
• How is the model performing?
• How does performance compare to any ground truth?
• Do the data sources in the model comply with privacy regulations?
• Are the data sources used for training a good representation of the general domain in which the algorithm will operate?

What you should do now? In absence of comprehensive and enforceable AI regulation at present, organizations should be proactive and prepared to consider the unique governance and risk implications as they embark on their AI journey. Here are few tactical steps which help business to minimize risk while developing a responsible AI solution and contribute in creating a standardized AI governance frameworks.

• Develop AI principles, policies and design criteria that fosters innovation, flexibility, and trust while identifying the unique risks and complexities associated with AI and data.
• Design, implement, and operationalize an end-to-end AI governance and operating framework across the entire AI development life cycle, including strategy, data sourcing, model building, training, evaluating, deploying, operating and monitoring AI model.
• Assess the current governance and risk framework and perform a gap analysis to identify opportunities and areas that need to be addressed.
• Integrate a risk management framework to identify and prioritize business-critical algorithms and incorporate an agile risk mitigation strategy to address five principles of AI governance during design and operation.
• Design and develop criteria to monitor and maintain continuous control over AIML algorithms without stifling innovation and flexibility.

In summary, as artificial intelligence becomes increasingly prevalent in our society, it is essential that we establish appropriate guidelines and regulations to ensure it’s safe and responsible development and use. The challenges associated with AI governance are complex, but with the right approach and collaboration among policymakers, technologists, and stakeholders, we can develop a governance framework that fosters innovation and protects society from the potential risks of AI.

I hope that you found this article informative, engaging, and thought-provoking. It was a pleasure to share my knowledge and insights with you. Thank you for taking the time to read this article, and I look forward to sharing more with you in the future.

This article was originally published by author on LinkedIn. Please visit this link AI Governance to read original article.