Over the past few months, there has been a lot of hype surrounding ChatGPT and its incredible capabilities. It is clear that ChatGPT is popularizing Artificial Intelligence among the masses and has the potential to bring immense value to specific use cases.
However, it is important to acknowledge that sometimes AI tools can exhibit flawed logic and inaccuracies in basic arithmetic.
Now imagine if these flaws were present when calculating your risk profile to determine your eligibility for a lower interest rate or buying your first house.
Does this mean that all AI is inherently bad? No, but it emphasizes the significance of AI governance and data integrity. We cannot simply assume that AI or Machine Learning will seamlessly work with any dataset and any use case.
We must thoroughly test and manage changes to both the data and the model to ensure the integrity of the results.
What is AI governance?
AI governance refers to a set of principles, policies, and regulations that guide the development, deployment, and use of AI systems. The main objective of AI governance is to ensure that AI is developed and used in a safe, transparent, ethical, and accountable manner.
There are several reasons why we need AI governance. Firstly, AI is rapidly advancing and has the potential to significantly impact various aspects of society, including employment, healthcare, and security.
Therefore, it is crucial to ensure that AI is developed and used responsibly and ethically. Secondly, AI systems can introduce biases or make unfair and discriminatory decisions, which can have severe consequences for individuals and groups.
AI governance helps mitigate these risks and ensures that AI is used in a fair and equitable manner. Lastly, AI governance helps build trust and confidence in AI systems, which is essential for their widespread adoption and use.
Key Components of AI Governance
Standards and Guidelines: AI governance establishes standards and guidelines for the development and deployment of AI systems. These standards and guidelines ensure that AI systems are developed and used in a manner that aligns with ethical, legal, and social norms.
Oversight and Accountability: AI governance ensures that individuals and organizations are held accountable for the development and use of AI systems. Oversight mechanisms such as audits and assessments are implemented to ensure transparency in AI systems and provide explainable outcomes.
Risk Assessment: AI governance involves assessing the risks associated with the development and use of AI systems. This includes identifying potential risks such as bias, discrimination, and privacy violations, and implementing measures to mitigate these risks.
Collaboration and Engagement: AI governance promotes collaboration and engagement with stakeholders, including industry, government, civil society, and the public. This collaboration ensures that the development and use of AI systems align with the needs and values of society.
Overall, AI governance is necessary to ensure that AI is developed and used in a manner that benefits society and aligns with our values and ethical principles.
The Difference Between Data Governance and AI Governance?
Data governance and AI governance are related concepts but have distinct focuses and scopes.
Data governance refers to the overall management of data within an organization, including its availability, usability, integrity, and security. It involves establishing policies, standards, and procedures for data management, and ensuring compliance with legal and ethical requirements in data collection, storage, and usage.
On the other hand, AI governance refers to establishing policies, and standards, and managing the development, deployment, and application of AI systems to ensure their safety, reliability, and objectivity. AI governance ensures that AI systems are used in a manner that complies with legal and ethical requirements.
In simpler terms, data governance primarily focuses on managing data as an asset, while AI governance focuses on managing the development and deployment of AI systems as a technology.
AI governance builds upon the foundation of data governance and extends it to address the unique challenges and risks associated with AI, such as algorithmic bias, explicability, and accountability. It can also be integrated into your data governance framework. This can further be enhanced through investments in data catalogues and other platforms that facilitate data stewardship.
By Gary Allemann, MD at Master Data Management
