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Technological products nowadays as household appliances like some high-tech auto robots that help people with housework, various commuting vehicles, and dazzling apps on mobile phones are creeping into our daily life, leaving people more disposable time by sparing them part of arduous work. Such inventions changed people’s normal lives more than ever before. However, with a careful look at the whole picture, the word ‘ethic’ is now being more and more cited worldwide. Ethics guiding people on ‘how best to live’ is now facing some problems when the leading technologies such as data science needs data collected from users, but without appropriate policies, this would lead to data abuse, which arouses concerns from not only critics but also citizens among the nation.

One obvious argument is that although cutting-edge technology brings about most advanced tools like ‘deep learning algorithms’, which has been proved really powerful in decision making, there still remain some alarming ethical issues. Vallor and Rewak believe that lack of transparency of using data will lead to public concerns. In the past, experts in different industries such as bank manager who approve loans, employers decide to hire and so on, are mainly based on their experience and they can tell a reason why they make such a decision eventually. However, after Artificial Intelligence came out, such machine learning methods as deep learning are replacing humans’ job in decision making nowadays. When such a company gets the data from the users, and uploads them to the datasets which deep learning algorithms will be based on them. After iteration of computing, the algorithm finally gives a certain answer whether yes or no. According to Knight(2017)’s research at MIT, he points out unless we can interpret the reason why such a decision is made, for example, which index or coefficient make contribution to the final decision, then we can improve the decision-making progress more understandable and lead to better use of such technology. Vallor and Rewak mention an example that a married couple who wants to apply for business loans. Both the couple are promising and bank consultant tell them they should pass the test and get the loan. However, after the evaluation of computer software, they are rated as ‘moderate-to-high’ risk and cannot get approved without a rational reason. This brings much trouble to people’s everyday life.

Furthermore, more precise and up-to-date policies should be introduced by the government in order to supervise such misleading and abuse of data usage. People are still in lack of the protection of such laws and policies which may cause potential data leaks or abuse. Knight finds that the European Union starting from the summer of 2018 asked companies to give explanations for automated decision-making regarding their users. Besides, European Union also claimed that ‘individuals should not be subject to a decision that is based solely on automated processing (such as algorithms)’, which made a huge contribution to the legislation of providing data abuse.

Nevertheless, such policies and public concerns may also turn out to be a hypercorrection. There are some policies that were aimed at protecting some targeted vulnerable groups of people but led to even worse situations. For instance, McCabe(2021) told a story about age checks with regard to some internet applications such as YouTube, Twitter, and so on. People are currently facing stricter age checks than ever before since the policies were built for the protection of children. It becomes necessary for people to upload their information of credit card or identity card to prove they are entitled to certain-age contents, which ‘upends one of the internet’s central traits: the ability to remain anonymous’.

In conclusion, such cutting-edge technologies truly benefit people’s lives to some degree, however, they may lead to data abuse, for example, nowadays algorithms of deep learning may not be interpreted well as long as it is improved in the future. Meanwhile, it also faces a dilemma. On the one hand, the lack of completed laws and regulations so that companies are under insufficient governance may cause data leaks and abuse. On the other hand, over-supervising of data usage can lead to hypercorrections which would arouse public concerns.