Imagine, you are taking a picture of a cat in your neighborhood and there is an AI-based filter you have been using in your camera to detect animals. Unfortunately, it has detected the cat as a dog. So, you try again on another cat but this time focusing more closely. Surprisingly, this time filter does not make the mistake and correctly detects the cat as a cat. Consequently, the question that generally arises what has made the same AI cat detection algorithm to fail in the earlier context while it passes with flying colors in the latter context. The answer is not straight forward and often entailed with much complexity. However, such problems in AI models arise out of misclassification due to the mislabeling of data or non-uniform distribution.
Concurrent AI technologies are equipped with very powerful and robust deep learning models which essentially show really good performance when large amounts of data are fed to them. So, one of the key aspects of the deep learning models in use is the quality of the input data to the model for training, validation, and testing. The model is trained, tuned and evaluated on this labeled data. The labeling of data controls the quality and correctness of the data input to the model which invariably introduces the human element in the pipelines of AI technology development. Humans can not only control the accuracy and precision up to which the input data is labeled but also take the pragmatic decisions to solve the problems stemming from mislabeling. Taking this into account it is quite natural to feel that AI technologies are more humane than automated does it not?
To overcome the problem of mislabeling, it is necessary to have a human interference and refinement of the existing labels. Some aspects of the solution to this problem are as follows:
The other problem is the non-uniform distribution of validation and test sets. Elaborately speaking, when the deep learning model has a training set and validation/test set commencing from different distributions then the distributions are non-uniform. When this happens, the models are unable to generalize well to the dev and test sets as they come from a different distribution and thereby, the models make a good number of misclassifications. As the problem of mislabeling, human intervention is indispensable when it comes to tackling the problem of non-uniform distributions. This provides another avenue in which the human element in the AI pipeline calls all the shots.
The following aspects introduce the human interventions that can be made to tackle the problem of non-uniform distribution and decrease the error rate of the models sufficiently.
AI can reshape human lives and the outlook towards the world of technology. The main impact of AI lies in the fact that it can replace, reduce and remove human efforts to introduce maximum levels of automation. However, qualitative problems of the dataset and their subsequent solutions require human intervention. Consequently, there is more human effort to AI technology pipeline than it is generally apparent. Hence, AI is more humane than we let on and the human element is still the key element in many cases to improve the performance of AI technology.
Abelling is a data enrichment service provider. We understand the impact of quality input for a better future with AI. Tell us about your project, we would love to help out in any way possible. We provide consultancy and fully managed services for all approaches to labeling.