Big Data, Plainly Spoken (aka Numbers Rule Your World)

Three ethical viewpoints applied to the Clinton email scandal

Last week, I gave an invited talk at the INFORMS Analytics Conference on ethics in data analytics. This is a follow-up to the HBR article from last year. In the talk, I discussed the need to have some frameworks to think about ethics. There are three main ethical philosophies that one can use to make ethical arguments.

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The Hillary Clinton email scandal is a good place to apply these ethical theories. (See this Wiki page for a review of the scandal.)

Many journalists and people in the computer business are eager to ascertain if Clinton's use of a private server caused harm, as indicated by hacking. For instance, the headline of this New York Times article is "Security logs of Hillary Clinton's email server are said to show no evidence of hacking." This argument falls right in line with the "consequentialist" school of ethics. Followers of this school consider an action unethical if it causes bad consequences.

President Obama is not a consequentialist. We know this because he said, "Here’s what I know — Hillary Clinton was an outstanding secretary of state. She would never intentionally put America in any kind of jeopardy." His argument is that Clinton is a virtuous person and therefore her actions are ethical. This type of argument aligns with "virtue ethics," the second of three major schools of ethical philosophy. People who believe in virtue ethics do not judge individual actions.

By contrast, a consequentialist would not take intention into account. If you think you have developed a green product but it ended up making a thousand people sick, the consequentialist will still regard your action as unethical.

Others have said they lost trust in Clinton. This Quartz article talks about the violation of transparency and accountability. Trust, transparency, accountability: these are moral values that many of us treasure. Those who make this type of argument are engaging in "value ethics," which is the third major school of ethical thinking. Under this view, being ethical means one must follow moral norms of behavior.

Intention is clearly important to value ethics, as it is to virtue ethics. Consequence is of less import to either. The three philosophies overlap in places but disagree on some issues. When we discuss matters of ethics, we should have a clear picture of which general ethical approach we are using.

The field of data analytics is filled with ethical dilemmas. So I hope this overview is useful in sorting out your own thinking.

 

 

Myths of Data Science and Analytics

Last week, I gave some brief remarks at the INFORMS New York Metro Student-Practitioner Forum (link), attended by a large group of enthusiastic students eager to enter the field of data science and analytics. (By the way, if you are at the INFORMS Analytics Conference in Orlando, come and find me. I am speaking on Ethics on Tuesday morning.)

I told the students that it is not too early to dispense of some myths of the data science and analytics career. The sooner they get rid of these wrong ideas, the brighter their future in this field.

Myth 1: Data science & analytics is all about coding and tools.

Wrong. Data science & analytics is all about problem solving. Coding and tools are useful for problem solving but it is more important to be able to frame the business problem, collect good data, understand your data, etc.

 

Myth 2: Coding is hard.

Wrong. Coding is easy. It may not feel that way. The media tells us that we need coding bootcamps. Many academic degrees focus their training on R, Python, and other coding platforms. The truth is Google, StackExchange and similar websites have made coding as trivial as copying and pasting bits and pieces of text. The hard part is to know what you want to do with the data - once you know that, it is only a few clicks to code it up. [PS. This is not meant to say there aren't good vs. less good coders.]

 

Myth 3: Data analysis is easy.

Wrong. Data analysis is really hard. The myth that it is easy arises because we have great tools that generate output quickly with a few clicks. But using a standard methodology does not guarantee good results. I have various examples of good methods delivering horrible results in the Prologue to my book Numbersense (link).

 

Myth 4: Data science & analytics is fun.

Wrong, unless you like laborious and tedious work. As an analyst, you will spend 80 percent of your time doing grunt work, such as diagnosing errors in the data, and correcting data issues. Something as simple as correcting the format of a date may require you to move data through multiple servers (see here). You will need to probe to the deepest level of the data generation process, which frequently means you need to get in the face of engineers and others, who have other pressing concerns. This part of life is to be tolerated in order to enjoy the fun parts.

 

Myth 5: Machines will replace humans.

To end my comments on a high note, I believe this career is unlikely to get replaced by machines. Here is some food for thought:

Last year, I did an article for 538 about New York City restaurant health care ratings. In the dataset, each restaurant is classified by cuisine type, which has its share of errors. Imagine that there is a business called Ivory that is described as a Thai restaurant in the data. You live next to Ivory, and so you know that it is an Ethiopian restaurant, not Thai.

How would a machine figure out this error? It doesn't. If one wants to argue, one could say that the machine can go and collect a huge dataset, then find all co-mentions of the restaurant Ivory with a cuisine type, eventually compute relative frequencies, and finally select the cuisine with the greatest likelihood of being correct.

So we have humans who need a sample size of one to get a guaranteed correct answer, and machines who need massive data to get a sometimes-wrong answer.