If you have been following the news in artificial intelligence, you probably already know that this field is growing at an exponential pace. Each day we are hearing about new use cases in AI, use cases, and applications that we haven't even dreamt of in the past few years.
But in the same news, you probably are also hearing about AI systems that have been deployed to production that are not yielding the expected outcomes. We are hearing about chatbots that have been misdirecting customers and employees into making the wrong decisions.
We also heard about chatbots that are hallucinating responses to customers. And we also heard about models that have been generating biased outcomes.
There is no denying that there is huge potential in artificial intelligence, but a premature deployment and adoption of AI systems could put companies at a huge risk of reputational and financial loss. And this exactly is the reason why AI governance platforms have become one of the most relevant and important topics today.
What is an AI Governance Framework?
AI governance refers to a set of rules, standards, and processes that have been set in place in order to ensure the responsible and ethical development and deployment of artificial intelligence systems.
Think of it as a set of guardrails, that ensure the ethical use of these artificial intelligence systems so that we minimize the risk in the systems while maximizing the potential benefit.
Benefits of AI Governance
Now we all know what the benefits are of artificial intelligence. It leads to reduced costs, improved efficiency, and leads to automation of repetitive and manual tasks. While these benefits make artificial intelligence a highly important topic and the most sought-after technology today, there is still at risk in artificial intelligence that is making AI governance an even more important topic to be discussed.
What are the Components of an AI Governance Framework?
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Manage an AI Inventory
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Conduct Risk Assessments
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Establish Roles and Accountability
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Conduct Model Testing
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Track Laws and Regulations
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Generate Model Documentation
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Manage Vendor Risk
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Train and Educate Your Team
Challenges of AI Risk AssessmentLack of Transparency
No matter how much the AI models become smarter, there will always be the chance of errors with AI, and there are dozens of examples available on the internet. Even the developers who made these systems don't know how AI operates and assess risks. This lack of visibility makes it challenging to assess risks, and due to this organizations struggle to build trust with stakeholders
Rapid Technological Leaps
AI is growing at a rapid growth and the government is no doubt trying to adopt the new technologies but they can’t achieve the maximum. As a result, AI systems lack some crucial resources and fail to detect risks. Even organizations failed to establish stable risk assessment frameworks, as the tools and techniques used today may become obsolete within months.
Regulatory & Legal Hurdles
Governments worldwide are introducing new laws to ensure AI systems remain ethical, transparent, and fair, but they can do this to some extent. Because every region and industry is different so do the rules which may conflict with one another. Moreover, it's early to say that AI will become smart enough to handle legal issues as many AI regulations are still in their early stages.
Ethical Dilemmas
The purpose of using AI in risk assessment is to reduce human efforts. The problem we are seeing this AI today is unfair behavior of AI based on different factors like race, gender, or social background. Another ethical issue is deciding how much control AI should have. We all know that AI can analyze huge amounts of data fast, but should it always make important decisions without human supervision?
Best Practices for AI Risk MitigationAI Model Discovery
Every technology comes with challenges and limitations but it does not mean we should not adopt technology. Instead of fear from AI, organizations should have clear information about all AI models that they are using in their systems for transparency and accountability. If you do not know which AI platform works best for risk assessment associated with your business, you will face severe security risks.
AI Model Risk Assessment
If you have done with AI models, the next step is to identify potential threats and weaknesses in artificial intelligence systems before they cause harm. However, a detailed risk check-up will help AI models to work correctly and follow legal rules. To properly check AI risks, organizations should use a clear process that includes regular monitoring, and third-party audits.
Data & AI Mapping and Flows
AI needs excessive and accurate data to perform well and AI governance platforms have an abundance of data but without a clear mapping of its sources, organizations face the risk of data breaches.
However, if data is mapped properly then AI will surely help to find weak points. To map data and AI effectively, organizations should record all data sources and track how AI changes the data,
Data & AI Controls
Using AI for risk assessment if fine but its important to make sure that AI models operate securely and ethically. Without proper controls, AI system may process unauthorized data, or fail to meet industry standards. As a business owner, you can use AI controls by defining clear policies for data usage and implementing encryption techniques. Additionally, AI models should undergo continuous testing and validation to detect erorors and performance issues.
Conclusion
The rapid growth of artificial intelligence presents both opportunities and risks for businesses. Without proper AI governance frameworks, organizations are at risk of reputational and financial loss. To mitigate these risks, businesses should conduct thorough risk assessments and stay updated on laws and regulations.
Implementing best practices such as AI model discovery and risk assessment can help ensure the responsible use of AI. Ultimately, AI governance platforms play a pivotal role in maximizing the benefits of AI while minimizing the associated risks.
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