FEATURE : AI GOVERNANCE
Data governance refers to the overall management of the availability , usability , integrity , and security of data used within an organisation . Data governance includes establishing policies , standards , and procedures for data management , and ensuring that data is collected , stored , and used in a way that complies with legal and ethical requirements .
Ensure transparency and accountability
It is important to be transparent about the data and methods used to develop AI models and to have clear guidelines and processes for handling issues that arise .
Regularly update models
On the other hand , AI governance refers to establishing policies and standards as well as managing of the development , deployment , and application of AI systems in order to guarantee their safety , reliability , and objectivity , as well as ensuring that AI systems are used in a way that complies with legal and ethical requirements .
In other words , while data governance is focused on managing data as an asset , AI governance is focused on managing the development and deployment of AI systems as a technology . AI governance builds on the foundation of data governance but extends it to include the unique challenges and risks associated with AI , such as algorithmic bias , explainability , and accountability .
AI Governance can form part of your data governance framework , which can benefit from investments in data catalogues and other platforms that assist data stewardship .
Accurate results
Ensuring that AI delivers accurate and unbiased results is a complex and ongoing challenge . However , here are a few key approaches that can help :
Use high-quality , diverse data
AI models need to be regularly updated to ensure they continue to deliver accurate and unbiased results . This can include updating the data used to train the model , refining the algorithms , and testing the model in new situations . Overall , ensuring that AI delivers accurate and unbiased results requires ongoing effort and attention . It ’ s important to approach the development and use of AI with a critical and cautious mindset , while also recognising its potential to bring significant benefits to society .
Data quality
Data quality plays a crucial role in ensuring accurate results from AI systems . The quality of the data used to train and test AI models directly affects the accuracy and reliability of the results produced by the model .
Here are some reasons why data quality is important for AI :
Garbage data
If the data used to train an AI model is of poor quality or contains errors , the resulting model will produce inaccurate or biased results . This is commonly referred to as the “ Garbage In , Garbage Out ” principle , which highlights the fact that the output of an AI model is only as good as the input data .
The data used to train AI algorithms should be representative of the real-world situations in which the AI will be used . Diverse data sets , which include different types of people and experiences , can help prevent biased outcomes .
Regularly audit and test AI models
It is important to monitor and evaluate AI models regularly to ensure they are delivering accurate and unbiased results . Testing the model with new data , verifying the results , and comparing them to benchmarks can help identify and correct issues .
Involve a diverse team
Bias
Biased data can result in biased models . If the data used to train an AI model is biased , the resulting model will likely produce biased results . This is particularly concerning in applications where fairness and non-discrimination are important , such as hiring or lending decisions .
Results
Data quality directly affects the accuracy and reliability of results produced by AI models . Clean , accurate , and high-quality data leads to more accurate and reliable results from AI models .
Building an AI team with diverse backgrounds and perspectives can help identify biases and ensure that the AI models are designed to be fair and unbiased .
To ensure accurate results from AI , it is important to have a robust data quality management process in place . This process should include data cleaning , validation , and
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