Thursday, October 10, 2013

"Evil" Analytics and a Code of Ethics

While talking with a seasoned developer at an Austin Startup Week event last night, the topic of data science ethics came up. He mentioned a project he was familiar with that involved using social media data to identify which of your competitor's employees would be easiest to poach. While such a project is not on the level of what the NSA is up to, it certainly raises the issue of how easy (and tempting) it is to use data for questionable purposes.

Ethics is often casually mentioned when discussing the impact of big data, but rarely is ethics given anything more than a cursory acknowledgment. However, the ethical implications of big data are staggering and need to be seriously discussed. It is better to have this tough conversation now, rather than wait until it can't be ignored. Indeed, if tales of ethical lapses on the part of data scientists pile up, the damage to the profession could be irreversible--we'd find ourselves in the position of bankers, but with less pay and no political connections. Now is the time to lay the rules down, so that data projects violating mainstream ethical standards can be labeled as such, and their negative impact to the field lessened.

Via a colleague of mine in UT Austin's Business Analytics program, I learned about a recent effort to establish a set of ethical standards for data science. While there's been a recent proliferation of data science/analytics/big data organizations, hopefully the focus on ethics will make this attempt successful. And you can join for free. Let's finish this conversation now, before people outside the data science field finish it for us.

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