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AI applications, systems and products are minimizing human intervention in the workflow, processes and operations for which the AI application is deployed. To ensure the quality of these applications it is mandatory to go through rigorous, continuous and comprehensive testing. In this article we are walking through the different dimensions of the quality which are required to make a Generative AI application as a quality application.
SwissCognitive Guest Blogger: Advait Avinash Sowale – “Quality Dimensions of Generative AI Applications”
Being an IT professional, we are usually never amazed with the results provided by Gen AI but look forward to making it better and better. Same as human, Gen AI tries to improve its last performance. We just help it to be there.
There are four distinct types of machine learnings. Supervised, Unsupervised, Semi-Supervised Learning and Reinforcement Learning. Till now we were surfing in the world of Supervised, Unsupervised, Semi-Supervised Learning by using these learning for the development of some smart applications, products and systems but the beauty of Gen AI is, it’s a first sphere towards the universe of Reinforcement Learning.
In many geographic the use of Gen AI is increased heavily. Various domains like Pharma, Education, Retail, Entertainment and many others are getting the solutions from Gen AI.
When we use traditional software, we do rigorous testing of the software. The software needs to pass extreme tests for functionality, performance, security, usability and for many other aspects.
Any AI enabled application minimizes human intervention. It is working on its own and hence it should work seamlessly. When we use any AI enabled application or systems, we minimize our dependency towards the task for which we have deployed the AI and therefore it is important to address the quality of that system.
For all the AI enabled applications rigorous testing is needed and Gen AI is not an exception to it.
Like in traditional testing, Gen AI testing includes Unit Testing, Integration Testing, System Testing, Functional Testing , Non-Functional Testing includes performance, usability and security.
The major difference between traditional and AI application testing with the dimensions of the quality parameters
The dimensions of the Gen AI testing majorly consist of Accuracy, Robustness, Ethics and Compliance. Extreme testing on these dimensions helps in making the Gen AI application a strong Gen AI application.
It is mandatory for the Gen AI to be a quality product because we are now transforming from the era of Weak AI to Strong AI.
When we talk about the quality of AI OR Gen AI application another aspect is the EAST.
So, what is EAST?
EAST stands for Explainability, Accountability, Security and Transparency.
These four aspects are utmost important when we are talking about comprehensive AI or specific to any AI like Gen AI.
The only context would be different. As Gen AI is providing the results for large numbers of types and pattern of data then it is mandatory to check for the explainability as how the outcome has been generated. With which process it is understanding the input, analyze the data and after processing, it is provided the output.
Accountability is another important aspect as there should be some responsible body, authority OR resource behind every output provided by Gen AI model. This can be achieved by maintaining and analyzing the entire process logs. Tracking of how the process is following defined ethical guidelines also helps to manage accountability.
Security has a vast spectrum for the Gen AI. Input, Content, analyzed data, output and learning and other miscellaneous all these factors are coming under security and for that it is needed to define security KPIs at various levels. Examples are Data, Authentication, Authorization, Incident Response, Vulnerability Management, User Behavior, Monitoring and many others. Data protection, model integrity and user privacy are some of the key factors which need to be addressed on the security front.
Finally, Transparency.
The output and the process of output should be transparent. Here the major important part is algorithm transparency. It leads to build the model confidence as transparency in algorithms is useful to understand how the algorithm works on different datasets. Model designing, Decision making process and overall process communication are among other factors which should consider to maintain the Transparency.
Now all these aspects are working to bring the quality of Gen AI model and add to that it is associated with the important factor of ethics. The Gen AI model should be ethically strong. Its output should not show any layer of bias and fairness. The points we have touched above must be considered at the extreme level with their KPIs and methodologies for the testing purpose.
Thus, the contribution of all these approaches and methodologies help to make a quality Gen AI.
About the Author:
Advait Avinash Sowale A Pune-based IT Professional with a decade of diverse expertise. Advait boasts an extensive career spanning over 14+ years, encompassing various domains such as Analysis, Designing, Development, Quality, and Delivery within the IT industry. Throughout his journey, he has contributed his skills and knowledge to renowned IT giants, catering to a global clientele.
Der Beitrag Quality Dimensions of Generative AI Applications erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.
