AI governance is crucial for the ethical development, deployment, and monitoring of AI systems. AI has the potential to transform operational efficiency across a broad range of industries, but without a proper framework in place to facilitate responsible AI usage, there is also the potential for significant harm.
We will explain the basics of AI governance and its importance, and outline best practices for organizations to promote the fair, equitable, and safe application of AI tools and models.
As AI technology is adopted on a widening scale across various industries, proper AI governance is becoming increasingly crucial. AI governance involves developing a comprehensive framework to detect, prevent, correct and mitigate AI risks to protect your data and address security threats as they arise. To build an AI governance framework, you'll need to understand your risks and the potential impact of your AI models and applications. You must manage these risks throughout the AI lifecycle and prioritise mitigation based on likelihood and impact. Only with strong AI governance can you ensure your organisation is using AI tools in a manner that fulfils all legal and ethical requirements.
What is AI governance? AI governance practices are the guardrails you put in place to ensure your AI tools and systems are safe, ethical and legally compliant. Your AI governance framework will outline your policies, procedures and commitment to ensure safety, fairness, and respect for human rights, serving as a foundation for policy-making and responsible AI.
AI governance goes beyond general IT governance, which focuses on an organisation's people, processes and technology. Responsible AI governance adds ethical components and considerations such as data privacy and the impact of AI on society, emphasising the importance of removing bias.
A strong AI governance framework will incorporate four key components:
AI governance is important from a compliance standpoint but it goes much further than that. With AI’s increasing integration into operations — and everyday life — creators and users of AI systems must ensure AI is used responsibly. Ethical considerations when using AI tools can be as important as legal ones.
AI governance promotes responsible AI usage, establishing and maintaining safeguards and methods for monitoring AI systems.
AI governance provides practical guidance for AI deployment, use and behaviour with a focus on:
Regulatory compliance with AI is a challenge. There is a patchwork of laws and regulations globally. AI governance helps organisations stay on top of emerging regulations and remain compliant.
Addressing AI risks and ethical concerns can also help organizations minimize the risk of potential fines, legal action, or liability.
AI governance is a mechanism to identify, assess and mitigate risks — crucial for ethical reasons and complying with regulations such as the EU’s AI Act which imposes strict controls on AI systems deemed to be higher risk.
Nearly half of companies say they have already taken action to manage risk in light of the emergence of generative AI. This includes:
A lack of an AI governance framework can lead to significant and unprecedented issues. For example, in 2016 Microsoft launched an AI chatbot named Tay that had to be taken offline in just 24 hours. Malicious user input taught the chatbot how to respond inappropriately, creating racist comments. As another real-world example, iTutor Group faced a lawsuit from the U.S. Equal Employment Opportunity Commission (EEOC) over its AI-powered recruiting software automatically rejecting any female job applicants over 55 or older as well as male applicants who were 60 or older.
Poor governance can also have significant financial implications. Zillow Offers used AI and machine learning algorithms to buy properties to renovate and flip. The AI tool had a high error rate causing the purchase of homes at prices higher than future selling values. Zillow was forced to take a half-billion-dollar write-down as a result and close down the project.
There are also privacy concerns. In 2023, ChatGPT exposed the title and active user chat history to other users. Some private data from a small number of users was also exposed, including name, email and payment address, credit card expiration dates, and the last four digits of credit card information.
An AI governance framework is not a one-and-done setup, but rather something that requires continuous monitoring and updating. Even if an AI model has been trained on data with robust governance, it's essential to maintain an AI governance framework because models evolve. They can drift from their original training, leading to degradation in output quality or reliability. They can learn bad behaviours that have a ripple effect across future interactions. This can cause reputational, financial and legal damage.
Deploying best practices for AI governance requires a comprehensive effort. Your approach to AI will impact how your business operates, whether you build your models in-house or use third-party solutions.
Key best practices include:
Human-centred AI ensures that systems are designed to enhance human capabilities and align with human values, respecting rights and privacy. In this development model, humans and AI work together to power decisions and drive outcomes. This ensures that AI outcomes are weighed with human perceptions to ensure fairness and equity and mitigate bias.
Prioritising people when making AI decisions sets the table for more responsible AI.
Any AI framework you choose is only as good as the policies and guidelines you set in place and the safeguards you create to ensure fair implementation. Your policies should provide both broad and specific definitions for acceptable uses, ethical standards and compliance requirements.
Clear policies act as the foundation for ethical AI and help facilitate alignment across your organization.
Transparency and accountability are key to building trust and ensuring acceptance of AI systems among consumers. While AI adoption is soaring, more than half of those surveyed said they felt nervous about AI products and services and the dramatic impact they expect AI to have on their lives.
Trust is essential. Documentation, public reporting and open communication can help create this trust and hold stakeholders accountable.
One way to help ensure a diverse and inclusive AI framework is to involve a range of stakeholders, including:
This consortium helps look at AI from different perspectives, reducing the risk of errors or unintended consequences.
Setting up systems for ongoing monitoring and regular audits of AI systems to ensure they operate as intended and comply with ethical standards helps ensure compliance with ethical frameworks.
AI tools evolve as they're used; after their initial implementation, they continue to collect data to learn and optimise their performance. Since AI systems are not static, this can introduce unwanted bias or behaviour into models.
Internal monitoring and third-party validation can identify and mitigate potential risks over time — but only if it happens continuously.
AI governance mitigates risk and ethical concerns, but it should not prevent innovation. Organisations need to strike a balance between responsible AI practices and encouraging experimentation. Ongoing support for new initiatives and the use of test environments can help foster innovation while rigorous testing and auditing can encourage ethical AI practices to be built in before public launch.
Awareness and training across your organisation will also play a vital role in enhancing AI literacy among employees and decision-makers. When they better understand AI technologies and the implications of their actions, they are better positioned to make good decisions.
Organisations should invest in — and advocate for — AI literacy to help employees and users navigate the ethical challenges and the potential misuse of artificial intelligence.
Best practices should be tailored to the risks and implications of different use cases. For example, errors or biases in AI systems used in healthcare could have life-changing impacts on patients and require additional safeguards. However, the protocols for testing and approving the use of healthcare AI systems will be far different than the testing required for other types of AI systems, such as generative AI tools or financial models.
There are considerable challenges in implementing and managing AI governance. Applying AI frameworks can require a nuanced approach with technical, ethical, legal and societal implications that are constantly evolving.
The AI market is revolutionising entire industries. Both public and private development and usage continue to grow at an unprecedented rate. Forecasts for continued adoption hover at nearly a 30% growth rate through 2030 with investments rivalling the GDP of all but a handful of nations.
Fortunes will be made and lost, creating immense pressure on developers and organisations to move quickly. Yet, models are still developing and evolving. It will take a concerted effort and commitment to AI governance principles to ensure innovation doesn’t further outpace ethical and responsible use.
The frenzied pace of the development and adoption of AI tools is far outstripping the ability of regulatory frameworks and governmental bodies to keep up. Key legislation takes a measured approach and legal concerns are still working their way through the courts. AI often works in a regulatory vacuum with varying degrees of oversight.
In some cases, though, regulations can change quickly. In the U.S., more than 40 states introduced new resolutions or adopted regulations concerning AI in just the first quarter of 2024. More than a quarter of states are considering bills to regulate private sector use of artificial intelligence.
Globally, dozens of countries have recent laws or new resolutions under consideration. In some cases, there are significant conflicts between regulations and gaps that fail to address the dynamic AI landscape.
Organisations need to stay on top of emerging regulations to ensure compliance. In most cases, this will require adhering to the highest levels of ethical behaviour to avoid building or deploying AI in ways that may go against legal frameworks in the future.
As cloud computing and AI cross borders, there can also be challenges with the coordination of AI frameworks. What may be legal in one country may be illegal in another and societal norms can differ greatly.
There are also competing AI governance frameworks, including:
In addition, more than 60 countries in the Americas, Africa, Asia and Europe have published national AI strategies, according to Stanford University’s AI Index report.
While widespread responsible AI requires global coordination, it is exceptionally difficult to achieve. As such, organizations must adhere to their own AI governance and vet any products, tools, or use cases carefully to ensure they conform with their comfort level.
AI systems can seem like magic, but they are built on complex and interlocking systems powered by vast data sets. Effective AI governance requires transparency and an understanding of this complexity which, in itself, can be difficult. Organisations may not have the level of knowledge or expertise to decipher the nuance of data collection, processing and use to make informed decisions.
Organisations may have to rely on third-party audits and attestations from suppliers to ensure that ethical frameworks are followed and comply with applicable laws.
The significance of AI governance in ensuring the ethical, transparent and responsible use of AI technologies cannot be overstated. There are far-reaching implications that can impact nearly every facet of life for billions of people around the globe.
Now is the time to be proactive, establishing and refining AI governance to implement fair, equitable and ethical AI practices and policies, including maintaining privacy.
Data privacy and AI governance are inextricably linked. To have good AI governance, you need strong data privacy practices. One way you can ensure privacy is maintained in your AI governance framework is by actively redacting sensitive information or producing synthetic data, as data security and privacy compliance platforms like Zendata do. This method allows you to collect and process data while protecting privacy and minimising risk.
To avoid ethical or legal consequences and get ahead of the curve with AI compliance, it's best to establish an AI governance framework now and maintain it as you go along. This way, you'll protect your organisation from potential repercussions down the line and establish trust with consumers and stakeholders.
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AI governance is crucial for the ethical development, deployment, and monitoring of AI systems. AI has the potential to transform operational efficiency across a broad range of industries, but without a proper framework in place to facilitate responsible AI usage, there is also the potential for significant harm.
We will explain the basics of AI governance and its importance, and outline best practices for organizations to promote the fair, equitable, and safe application of AI tools and models.
As AI technology is adopted on a widening scale across various industries, proper AI governance is becoming increasingly crucial. AI governance involves developing a comprehensive framework to detect, prevent, correct and mitigate AI risks to protect your data and address security threats as they arise. To build an AI governance framework, you'll need to understand your risks and the potential impact of your AI models and applications. You must manage these risks throughout the AI lifecycle and prioritise mitigation based on likelihood and impact. Only with strong AI governance can you ensure your organisation is using AI tools in a manner that fulfils all legal and ethical requirements.
What is AI governance? AI governance practices are the guardrails you put in place to ensure your AI tools and systems are safe, ethical and legally compliant. Your AI governance framework will outline your policies, procedures and commitment to ensure safety, fairness, and respect for human rights, serving as a foundation for policy-making and responsible AI.
AI governance goes beyond general IT governance, which focuses on an organisation's people, processes and technology. Responsible AI governance adds ethical components and considerations such as data privacy and the impact of AI on society, emphasising the importance of removing bias.
A strong AI governance framework will incorporate four key components:
AI governance is important from a compliance standpoint but it goes much further than that. With AI’s increasing integration into operations — and everyday life — creators and users of AI systems must ensure AI is used responsibly. Ethical considerations when using AI tools can be as important as legal ones.
AI governance promotes responsible AI usage, establishing and maintaining safeguards and methods for monitoring AI systems.
AI governance provides practical guidance for AI deployment, use and behaviour with a focus on:
Regulatory compliance with AI is a challenge. There is a patchwork of laws and regulations globally. AI governance helps organisations stay on top of emerging regulations and remain compliant.
Addressing AI risks and ethical concerns can also help organizations minimize the risk of potential fines, legal action, or liability.
AI governance is a mechanism to identify, assess and mitigate risks — crucial for ethical reasons and complying with regulations such as the EU’s AI Act which imposes strict controls on AI systems deemed to be higher risk.
Nearly half of companies say they have already taken action to manage risk in light of the emergence of generative AI. This includes:
A lack of an AI governance framework can lead to significant and unprecedented issues. For example, in 2016 Microsoft launched an AI chatbot named Tay that had to be taken offline in just 24 hours. Malicious user input taught the chatbot how to respond inappropriately, creating racist comments. As another real-world example, iTutor Group faced a lawsuit from the U.S. Equal Employment Opportunity Commission (EEOC) over its AI-powered recruiting software automatically rejecting any female job applicants over 55 or older as well as male applicants who were 60 or older.
Poor governance can also have significant financial implications. Zillow Offers used AI and machine learning algorithms to buy properties to renovate and flip. The AI tool had a high error rate causing the purchase of homes at prices higher than future selling values. Zillow was forced to take a half-billion-dollar write-down as a result and close down the project.
There are also privacy concerns. In 2023, ChatGPT exposed the title and active user chat history to other users. Some private data from a small number of users was also exposed, including name, email and payment address, credit card expiration dates, and the last four digits of credit card information.
An AI governance framework is not a one-and-done setup, but rather something that requires continuous monitoring and updating. Even if an AI model has been trained on data with robust governance, it's essential to maintain an AI governance framework because models evolve. They can drift from their original training, leading to degradation in output quality or reliability. They can learn bad behaviours that have a ripple effect across future interactions. This can cause reputational, financial and legal damage.
Deploying best practices for AI governance requires a comprehensive effort. Your approach to AI will impact how your business operates, whether you build your models in-house or use third-party solutions.
Key best practices include:
Human-centred AI ensures that systems are designed to enhance human capabilities and align with human values, respecting rights and privacy. In this development model, humans and AI work together to power decisions and drive outcomes. This ensures that AI outcomes are weighed with human perceptions to ensure fairness and equity and mitigate bias.
Prioritising people when making AI decisions sets the table for more responsible AI.
Any AI framework you choose is only as good as the policies and guidelines you set in place and the safeguards you create to ensure fair implementation. Your policies should provide both broad and specific definitions for acceptable uses, ethical standards and compliance requirements.
Clear policies act as the foundation for ethical AI and help facilitate alignment across your organization.
Transparency and accountability are key to building trust and ensuring acceptance of AI systems among consumers. While AI adoption is soaring, more than half of those surveyed said they felt nervous about AI products and services and the dramatic impact they expect AI to have on their lives.
Trust is essential. Documentation, public reporting and open communication can help create this trust and hold stakeholders accountable.
One way to help ensure a diverse and inclusive AI framework is to involve a range of stakeholders, including:
This consortium helps look at AI from different perspectives, reducing the risk of errors or unintended consequences.
Setting up systems for ongoing monitoring and regular audits of AI systems to ensure they operate as intended and comply with ethical standards helps ensure compliance with ethical frameworks.
AI tools evolve as they're used; after their initial implementation, they continue to collect data to learn and optimise their performance. Since AI systems are not static, this can introduce unwanted bias or behaviour into models.
Internal monitoring and third-party validation can identify and mitigate potential risks over time — but only if it happens continuously.
AI governance mitigates risk and ethical concerns, but it should not prevent innovation. Organisations need to strike a balance between responsible AI practices and encouraging experimentation. Ongoing support for new initiatives and the use of test environments can help foster innovation while rigorous testing and auditing can encourage ethical AI practices to be built in before public launch.
Awareness and training across your organisation will also play a vital role in enhancing AI literacy among employees and decision-makers. When they better understand AI technologies and the implications of their actions, they are better positioned to make good decisions.
Organisations should invest in — and advocate for — AI literacy to help employees and users navigate the ethical challenges and the potential misuse of artificial intelligence.
Best practices should be tailored to the risks and implications of different use cases. For example, errors or biases in AI systems used in healthcare could have life-changing impacts on patients and require additional safeguards. However, the protocols for testing and approving the use of healthcare AI systems will be far different than the testing required for other types of AI systems, such as generative AI tools or financial models.
There are considerable challenges in implementing and managing AI governance. Applying AI frameworks can require a nuanced approach with technical, ethical, legal and societal implications that are constantly evolving.
The AI market is revolutionising entire industries. Both public and private development and usage continue to grow at an unprecedented rate. Forecasts for continued adoption hover at nearly a 30% growth rate through 2030 with investments rivalling the GDP of all but a handful of nations.
Fortunes will be made and lost, creating immense pressure on developers and organisations to move quickly. Yet, models are still developing and evolving. It will take a concerted effort and commitment to AI governance principles to ensure innovation doesn’t further outpace ethical and responsible use.
The frenzied pace of the development and adoption of AI tools is far outstripping the ability of regulatory frameworks and governmental bodies to keep up. Key legislation takes a measured approach and legal concerns are still working their way through the courts. AI often works in a regulatory vacuum with varying degrees of oversight.
In some cases, though, regulations can change quickly. In the U.S., more than 40 states introduced new resolutions or adopted regulations concerning AI in just the first quarter of 2024. More than a quarter of states are considering bills to regulate private sector use of artificial intelligence.
Globally, dozens of countries have recent laws or new resolutions under consideration. In some cases, there are significant conflicts between regulations and gaps that fail to address the dynamic AI landscape.
Organisations need to stay on top of emerging regulations to ensure compliance. In most cases, this will require adhering to the highest levels of ethical behaviour to avoid building or deploying AI in ways that may go against legal frameworks in the future.
As cloud computing and AI cross borders, there can also be challenges with the coordination of AI frameworks. What may be legal in one country may be illegal in another and societal norms can differ greatly.
There are also competing AI governance frameworks, including:
In addition, more than 60 countries in the Americas, Africa, Asia and Europe have published national AI strategies, according to Stanford University’s AI Index report.
While widespread responsible AI requires global coordination, it is exceptionally difficult to achieve. As such, organizations must adhere to their own AI governance and vet any products, tools, or use cases carefully to ensure they conform with their comfort level.
AI systems can seem like magic, but they are built on complex and interlocking systems powered by vast data sets. Effective AI governance requires transparency and an understanding of this complexity which, in itself, can be difficult. Organisations may not have the level of knowledge or expertise to decipher the nuance of data collection, processing and use to make informed decisions.
Organisations may have to rely on third-party audits and attestations from suppliers to ensure that ethical frameworks are followed and comply with applicable laws.
The significance of AI governance in ensuring the ethical, transparent and responsible use of AI technologies cannot be overstated. There are far-reaching implications that can impact nearly every facet of life for billions of people around the globe.
Now is the time to be proactive, establishing and refining AI governance to implement fair, equitable and ethical AI practices and policies, including maintaining privacy.
Data privacy and AI governance are inextricably linked. To have good AI governance, you need strong data privacy practices. One way you can ensure privacy is maintained in your AI governance framework is by actively redacting sensitive information or producing synthetic data, as data security and privacy compliance platforms like Zendata do. This method allows you to collect and process data while protecting privacy and minimising risk.
To avoid ethical or legal consequences and get ahead of the curve with AI compliance, it's best to establish an AI governance framework now and maintain it as you go along. This way, you'll protect your organisation from potential repercussions down the line and establish trust with consumers and stakeholders.
