« Myths of Data Science and Analytics | Main | Dreadful analysis shows the importance of numbersense »


Feed You can follow this conversation by subscribing to the comment feed for this post.


The legal system often uses a mix of schools. If someone attempts to kill someone and fails it is attempted murder and results in a lower penalty. Murder becomes manslaughter if it is not deliberate. Being an otherwise good person may reduce the sentence.


A nit on philosophy #1 (cause harm). It's not just causing harm, but the likelihood of causing harm.

For example, if someone texts while driving and causes an accident, that's clear harm. But it's also wrong to text while driving, even if that particular time there was no accident, because of the likelihood of harm.

Apply this to Clinton. "There's no evidence of hacking" means we can't say there was harm, but we can say that this arrangement is likely to have raised the probability of harm given what seems to be a lackadaisical level of security. (In addition to the fact that hackers may just have left no trace.)


This is really a good viewpoint of the data analytics. Now the data science is developing very fast and most of the ethical rules have not been established. Maybe we should really pay more attention to the ethical issues in the data analytics.

However, I think nowadays the building of an ethical system to supervise the data analytics is very difficult. In my mind, the most important problem for the data ethics is:

Talking about data ethics nowadays just likes talking about environment protection in 1950. We have not developed a complete data analytics system to utilized the data mostly. Which algorithm is best? How can we efficiently run a big data project? These problems are the most urgent ones which people want to solve. The professtional people have not enough time to build a data ethics system. There are a lot of utilization issues or problems for them to deal with. If these utilization problems cannot be fully solved, the outcome of the data analytics will be bad and then neither the data analytics nor the data ethics will exist. So now, maybe not many people will pay attention to the data ethical issues.

That is, I think, a contradiction or a balance problem. But most people will choose to focus on the productivity at first and then ethics.

Only some naive ideas, welcome to discussion. :)


The comments to this entry are closed.

Get new posts by email:
Kaiser Fung. Business analytics and data visualization expert. Author and Speaker.
Visit my website. Follow my Twitter. See my articles at Daily Beast, 538, HBR, Wired.

See my Youtube and Flickr.


  • only in Big Data
Numbers Rule Your World:
Amazon - Barnes&Noble

Amazon - Barnes&Noble

Junk Charts Blog

Link to junkcharts

Graphics design by Amanda Lee

Next Events

Jan: 10 NYPL Data Science Careers Talk, New York, NY

Past Events

Aug: 15 NYPL Analytics Resume Review Workshop, New York, NY

Apr: 2 Data Visualization Seminar, Pasadena, CA

Mar: 30 ASA DataFest, New York, NY

See more here

Principal Analytics Prep

Link to Principal Analytics Prep