What's Happening?

There are a wide variety of interesting technological articles in this Association for Computing Machinery magazine. One that peaked my interest and is relevant to my field was the Computing Ethics, Engaging the Ethics of Data Science in Practice. The authors wished to seek more common ground between data scientist and their critics, and to discuss the possible issues that arise from the growing field of Data Science and its practitioners. They explain that there exists critical commentary of the field, and that these critics proclaim that data scientists do not recognize the power they wield and often times use such power in a reckless and unethical manner. These critiques are not new and are not based in much truth, There are some instances of Data Scientists and their firms abusing their analytical powers such as the Cambridge Analytica or Facebook controversies; but as a whole, Data Science is no more unethical than other computer science fields. It is the personal morals and end goals of specific people that lead to possible unethical situations. These accusations are based in ignorance of the field, and an overlook to the routine deliberate activities that these outsiders are thinking about when it comes to ethics. Solon Barocas and Danah Boyd, the authors of this article, provide examples of Data Scientist practicing ethics, much like many other fields. They explain that they engage in countless acts of implicit ethical deliberations while in the process of creating a meaningful machine learning model. Data Scientists have to deal with incomplete data, which the authors argue is as much a moral concern as it is a practical one. Choosing what data to use, and determining if it is useful based on where it came from is a common situation data scientist find themselves in. Validating a model, and how this said model will perform when deployed are also ethical concerns that are often overlooked by the outside community. There is a great need for careful judgement in this field, many times having to take into consideration the ramifications it will bring humanity, and how it will ultimately affect the world. Even attempting to address these ethical issues explicitly, practitioners face trade-offs that must be considered. The article then explains of a model with gender bias, and fixing the issue would have to sacrifice privacy. The authors want a collaborative and constructive dialogue between Data Science practitioners and their critics. They want the critics to realize the effort put in by these people, and the small, ethical decision that go into making their analysis. The commentary of the field is often created by people who are unaware of the actual practice of it. The authors argue that we need to make effort to work collectively to deliberate appropriately about the field which will reveal a common ground between the two groups, and lessen the gap in understanding.