How can we reduce bias in data? What are data scientists in India doing about it? How can we contribute and be a part of this?

So a bias in data science and machine learning is an important problem. Because what are we doing in data science and deep learning, we are trying to essentially learn automated rules between inputs and outputs. So if you already have bias in the data set, then the algorithm will simply learn to copy that bias, right. So there is heated debate happening, some of the debate is healthy debate, some of the debate is unhealthy, because some people expect that bias will go away on its own. Whereas in society today, we actually have bias, right. So bias is not just an artifact of data science, there is bias in everything that we do day to day life also. So,  bias is there, but we can do things about it, or we should be aware of what we can do to avoid it. For example, one of the facial recognition models, were detecting the faces of white people by let's say Western people, whereas they were not doing well, you have a darker skinned person, right. So, black people were not being detected properly and what can you do about it? Well, you should give more data to the models on the population that is not well represented in this case, darker skinned people, right.I was just saying that we should be careful to ensure that every class that is represented in the real world also has some representation data set that which is important. So this is an important question. So those of you who have understood deep learning, machine learning etc, well, should look at this as a way to do further research, but also in companies. companies are now very, very sensitive to this because it can be a PR nightmare, if there is a model that they have released, which contains bias. So this is an important area for those already familiar with deep learning