Top 5 AI Governance software

Currently, we live in an era dominated by Artificial Intelligence (AI).

Every day we are surrounded by AI technologies: virtual assistants, personalized content recommendations on Netflix or Prime Video, fraud detection systems in the banking industry, navigation applications, task automation in manufacturing, generative AI (ChatGPT or Bard), and many more.

The list above is just the beginning when it comes to the possibilities of Artificial Intelligence. In fact, the number and versatility of AI applications will be growing as more and more businesses invest in AI.

According to Markets and Markets, the AI industry will grow to a $190 billion industry by 2025. What is more, according to IDC, 90% of commercial enterprise apps will use AI by 2025.

As a result, the role of AI is and will be growing at an accelerating pace. Nevertheless, the development of AI concerns not only employees, who fear losing their jobs, but also business leaders. They are concerned about:

• According to Gartner, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them,
• The EU AI Act will come into force by 2025, and they need to choose an appropriate AI governance tool to assist them in delivering responsible and ethical AI.

What is AI governance?

Artificial Intelligence governance is the legal framework that is needed to ensure that AI and Machine Learning (ML) technologies are researched and developed in a way that helps organizations worldwide adopt such systems in ethical and responsible ways. As a result, the AI governance framework’s goal is to close the gap between accountability and ethics in the development and use of AI.

Due to rising concerns about Artificial Intelligence (AI) development, the popularity of AI governance frameworks is growing as well.

Based on Google trends data (15/10/2023)

What are AI governance practices about?

The main areas of focus for Artificial Intelligence governance are those related to justice, data quality, and autonomy. AI governance determines how much of a daily-life AI algorithm can shape and who monitors the work of these algorithms. Some of the key concerns AI governance addresses are:

• AI ethics, safety, and societal implications,
• Legal and institutional structures for the use of AI and AI technology,
• Rules around access and control of personal data,
• Moral and ethical questions related to AI,
• Sectors that can be AI-automated.

As you can see, AI governance adoption in businesses ensures that AI systems are safe, transparent, and compliant with legal requirements. While these principles behind AI software development have always been crucial, the impending EU AI Act emphasizes their significance even further.

Why do businesses need AI governance platforms?

Being packed with useful features, a powerful AI governance tool not only increases our capabilities but also enhances efficiency, precision, and safety. These features entail:

• AI model inventory, assessment, monitoring, auditing,
• Risk identification and mitigation,
• Compliance management,
• Transparency and explainability tools,
• AI governance workflow automation.

As a result, the AI governance platform provides us with numerous benefits, among them:

• Enhanced accountability: AI systems operate transparently and are accountable for their actions and decisions,
• Risk mitigation of potential ethical, legal, and operational risks associated with AI implementation,
• Regulatory compliance to adhere to evolving AI-specific, standard, and privacy regulations, like the EU AI Act,
• Trustworthiness of AI applications is increasing public safety,
• Continuous improvement and monitoring of AI systems, ensuring they are aligned with organizational and societal objectives.

How to choose an AI governance tool?

Before we move on to the AI governance tool comparison itself, let’s start with a short checklist. Going through all the steps below will help you define the goals you want to achieve with this tool and make the final decision.

Step 1: Identifying AI governance needs

When you choose an AI Governance tool, you should first start with the simple question, “Why is AI governance needed in my organization?’. In fact, each company will have slightly different AI governance needs.

Therefore, you should ask yourself:

• How Artificial Intelligence (AI) is used in your organization?
• What kind of data are you working with (for example, personal, anonymized, or sensitive)?
• What AI models are used and how complex are they?
• What applications will be transferring data to your AI models?
• At what scale does your organization operate?
• What is the volume of data being processed?

Going through all these questions will allow you to determine the level of oversight and control you need from the AI governance tool of your choice.

Step 2: Evaluating specific features

The next step is to evaluate and compare the features of different tools for AI governance and compare them with your needs. At this point, you need to ensure that the tool of your choice can address all aspects of AI governance, such as:

• data security,
• transparency,
• explainability, or
• data privacy.

It is also advisable to take into consideration other useful features like data lineage, monitoring and reporting, alerting, or generating insights. Moreover, all AI governance platforms should be compliant with regulatory requirements.

Step 3: Evaluating customization and integration possibilities

As already mentioned, governing AI across organizations has slightly different needs. Therefore, it is also important to check if the tool of your choice has customization options to match your specific requirements. These features may include adjusting to your existing workflows, implementing internal governance structures, or supporting multiple languages.

Another important point is the seamless integration with your existing data sources, analytics tools, and databases that data professionals, like data engineers or data scientists, use in your organization.

Step 4: Evaluating scalability

Another essential aspect to take into consideration is the scalability and expansion of AI governance software. As a result, it should adjust to increasing data volumes, AI models, and users, meaning it should support future business growth.

Truth be told, scalability evaluation is essential, as it ensures that the solution can grow and adjust to your organization’s evolving needs. Therefore, it should handle with ease increases in data volume, the complexity of AI models, and the number of users without dropping in performance or efficiency.

What is more, scalable AI governance software should not only fulfill current business expectations but also adjust to future expansion, simplifying the integration of new features and technologies.

Step 5: Evaluating the provider comprehensively

Finally, you should assess the provider of the Artificial Intelligence governance of your choice. You need to get a full understanding of their experience in AI governance and technology domains. To do so, you can:

• conduct due diligence, for example, customer reference checks,
• provider’s product roadmap assessment,
• support and maintenance check,
• pricing model comparison.

It is important to choose a provider that aligns with your values, has a good reputation among their customers, and is known for their transparency, ethics, and collaborative spirit.