This is part 3 of the 3 series post.Let's pick it up from where we left in last post.Here are few ways to reduce biasing

Choosing a suitable learning model for the problem:

It is important to understand the problem first and then identify the best model for a given situation. The machine learns from the data given to it. Now in unsupervised models , it learns from their data set and can show biased output as the model can mix up the data. Whereas in supervised models it allows more control over bias in data selection. Hence, one should take time and look through the various vulnerabilities, troubleshoot ideas and adopt different strategies when building the model.

Choose a representative training dataset:

The training data should be diverse with different groups. It can have different models for different groups but if the data is insufficient for one group, then usually weighting is considered to increase the importance in training. But this can cause biasing and the model is forced to study the limited data. Therefore, one should be careful with weights, especially large ones.

Monitor performance using real data:

Usually after training, one uses testing data to verify the model which might not be similar to the real data. The developer fails to realize the vulnerability of the model. Therefore, real data should be used to test the model and biasing can be identified by the developer and they can rectify it.

A model for monitoring purposes:

Another possible way to address the problem is to build a machine learning model that can independently identify biasing in the data provided before it can be used in the intended model. This model has already been provided with ideal unbiased data and hence it is able to identify the possible vulnerability in the input data.

Setting the Boundary:

Another apt practice is to set a boundary between automation and human intervention. It’s necessary to decide to what extent should the processes or systems be automated. If the whole system is automated without any human intervention, it will become prone to disasters and is highly risky. For example, a model in a hiring company might be biased unintentionally or might have become biased over the time due various reasons as discussed earlier. This might lead to a biased output and thus causes troubles to both, the job seeker and the employer. Therefore, the best practice would be an that an AI system / algorithm gives the advices and a human double checks the decision of the AI system.This will improve the quality of the algorithm too and will avoid any sort of discrepancies in the functioning. Both, the humans and the AI can work hand in hand if this is followed.

Increasing the availability of data:

More diverse the data, more will be the accuracy. This is what we have seen in the AI systems but there is also another benefit of feeding more data to the system. It can considerably reduce the risk of biasing. Giving more data during the initial training makes the model ‘understand’ the data well and not just ‘learn’ it. Let us take an example to consider this. Consider a high school student learning maths and appearing for the exam. Though we say memorizing maths is difficult, it is not impossible. So, there is a probability that the student might memorise the math questions if the questions are limited if the same pattern of questions reappear in the paper. Hence, we can say that the best way to avoid this is to not follow the pattern in the paper to ensure that the students “understand” math well.The same analogy can be used for our model. It should have a variety and vast amount of data in order to reduce the risk of bias along with increasing the accuracy.

There are many ways to overcome this biasing, however it is majorly linked to human error, inadequate and low quality data, model training and monitoring.There are many significant developments taking place in this field and It is extremely necessary that the AI system shouldn’t fall into wrong hands or else we well have no control over the severe consequences that follow. AI is such a powerful system that many crimes are taking place with the help of AI . But on the other hand AI is also serving as an effective system in order to curb crime, for example, the fraudulent transaction detection helps the banking sector to strengthen their sector. But the bane that tags along is also very dangerous. Many people who are into studying and developing AI systems are working to provide resources for organizations which are looking to deploy AI fairly. It is not a set point which can be told to curb this bias problem, but it’s a long process which should be followed inorder to minimize the biasing problem which is seen in the AI industry. AI has the potential which is enough for business, economy and to tackle some of the serious issues which are present in society, but that will be possible only if humans trust the AI system and to gain this trust unbiased results which is a need. Hence, we can conclude saying that, when AI systems can assist us , there is also a need for the AI systems to be helped in order to help us efficiently.