Prior to large data collection many processes both in business and in public sectors were more expert- or public- driven. For example, a store owner will have his own estimate of the demographics of his clients, and make advertisement or even operational decisions based on that subjective assumption, which could have been biased (in extreme cases discriminatory or racist). Will she be objective having data-driven tools or will those tools generalize more and miss the nuances of the business? Will those tools bring the bias of their creators or bias of data collection?

Another example, a bike sharing system that follows the demand may (this is known problem) end up contributing to inequity by offering service in areas with wealthier populations (Jahanshahi et al). Also by using credit cards and mobile apps those systems are building data-driven modern operations, and at the same time do not allow some people to use them unless they have an access to credit card and phone (even if the fees are comparable to one in public transportations).

Another example is the online apartment rental which also supports inequality (Boeing, 2020).

These topics become more sensitive in the public sector, where public service or policies should be fair, and promote equity, for example, data-driven approach to policing (predictive policing) may not be free of bias, reduce transparency, and even result in algorithm-based discriminations (Meijer and Wessels, 2019) (Please read this paper it gives a really good insight into data-driven methods).

We learned that data analysis is a sequence of proper data sourcing, data preparation, analysis and conclusions. And each of those may go wrong and contribute to inequity. For example, face recognition may perform differently depending on the subject's race (Turner Lee, 2018).

Given those examples I have three questions for you:

1. From your own experience, share what industry/company/public service which you use/or have used has changed drastically by using data or technology driven solutions? I understand that it is not always easy to think of data driven solutions as you may need to know the backend of the operation in that particular case, so technology driven solutions, which became possible because of the IT progress, are equally acceptable as a response.

2. Use your example or one from available literature to argue whether a modern IT-driven world promotes or decreases equity, and does data and data analysis play any role in that or is it more defined by the societal rather than technological advances?

3. How we can fix it? Is awareness enough or we need to have more laws to regulate those data-relatated practices to ensure equity?