Understanding AI Governance

Who hasn’t heard the buzzword, Artificial Intelligence (AI)? It is a transformative technology pervading in all walks of our lives, professional and personal.

With rapid technological advancements, AI is becoming exponentially powerful and replacing many functions that are done manually. It has the potential to perform many tasks faster and possibly better than humans, however, how an AI model is developed can also pose a huge risk to the outcome. ?

What is AI Governance ?and Why it's Needed

AI governance is the guardrail that is being put in place to ensure that AI models are built in such a way that is ethical, fair, and causes no harm to people. AI Governance provides a structured framework uniting the diverse vantage points of all stakeholders such as AI developers, data scientists, users, regulatory, legal, policymakers, and business leaders. The goal is to achieve responsible development, deployment, and application of AI benefiting all.

It is important to recognize that AI is still a machine doing tasks based on the data and models created by humans. Its performance greatly depends on the data quality and modeling parameters selected by the AI developers who are technical professionals. They have the expertise to create powerful models to accomplish a given task, however, its overall usability highly depends on the inclusiveness of the data used.

For example, suppose a hospital develops an AI model to triage their patients based on certain data. In that case, the hospital must use robust datasets that represent its patients’ diversity and demographics and not just limited data representing only a part of its demographics. To use such a model in decision-making will be inherently biased and might provide incorrect output for people whose data were not used in the model development. ??

As humans, we all have biases and if not monitored, the model developed with biased data will be flawed. Such models will create misrepresentation and can lead to unfairness and risks for some individuals. To mitigate such risks, we must implement AI governance. ???

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Key principles of AI Governance

The AI governance landscape is relatively a new concept and is continually evolving. Some of the most significant principles of AI governance are listed below.

Transparency and Explainability ?

AI models can become extremely complex to the extent that it is often termed black boxes as the model creators themselves don’t fully understand how they are making decisions. A robust and ethical model must be able to provide the rationale for the decision-making.

?Privacy and Compliance

Data is the lifeline of AI models. It is critical to understand the quality of data. Some important considerations to take into account are listed below.

• Are the data privacy laws followed to acquire and store the data??
• Are the data correctly annotated and anonymized?
• Are the data used to represent all key stakeholders the model is supposed to serve?
• What is the process of validating data quality and privacy?
• Are the technical standards and best practices procedures in place for safe AI development?

Proper oversight is a must to ensure that these issues are handled objectively. ????

?Risk Mitigation Strategies

A model developed with biased data has the potential to harm some individuals by providing incorrect results. Developers must ensure that the model is trained on representative and comprehensive data sets and then validated with diverse data sets. The testing is an ongoing process as the models can drift over time impacting the quality of the outcome. The risk mitigation processes and cadence should be in place to ensure long-term safety. ?

?Stakeholders’ Engagement ?

The AI model is not simply a technical exercise. A robust AI model is a result of strong collaboration among developers, data scientists, regulatory and legal, privacy professionals, and business leaders. The role of a cross-functional team is to remove biases in the data selection and model building. For example, while the developer team can create an efficient model for house loan approval, and the business leaders may be eager to use this model to replace the human loan officer, the role of the compliance and legal team must be to ascertain if the model is built to be inclusive and fair to all and not just favoring one prominent segment of the society whose data was easily available. ???

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Who Owns AI Governance?

Sensing the importance of AI governance, organizations are appointing AI leaders who are establishing AI boards and developing AI policies, however, given the omnipresence of AI means that AI governance is everyone’s responsibility. AI leaders should create an AI-aware culture with the help of regular education and training for their employees. ??

As AI technical innovations are evolving so is the need to put the effective safeguards. In 2023, the US government issued an executive order to develop an AI safety and security framework. European Union has enacted an 'AI act' which is effective from August 2024. Based on the specific use of AI, this act outlines clear requirements and obligations for AI creators.

Many organizations are also setting up AI boards and AI oversight panels to help them stay compliant with the fast-changing AI landscape. ???

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Challenges in AI Governance

AI technical advancements are growing fast. Generative AI and large language models are transforming the field with new applications. AI governance and regulation are moving rather slowly and often not fully developed to effectively manage this novel space.

Some like Elon Musk think AI is dangerous and will have catastrophic effects on humanity without stringent control. Others see AI as a game changer that will bring greater good to humanity. Regardless, AI governance is the backbone of effective AI model development and implementation.

As AI governance and regulations are taking shape, it is equally important to have a balance between providing checks and balances for AI development and deployment and AI innovation. When properly done, it is possible that a good AI model can help humans get rid of our subconscious bias and achieve more objective decision-making.