Data-driven decisions often turn out to be better than those decisions made without the benefit of quantifiable evidence. As this idea has picked up mainstream traction, many decision-makers in both private companies and in public offices have turned to the acolytes of Big Data for guidance.

This trend has led to many good things. But “Big Data” is far from a magic elixir for good decision-making. It has its own baggage, it’s own “biases” and it can come with ramifications.

Teaching the Dark Side of Data

Big universities such as Harvard and MIT, and small liberal arts colleges like Denison, are now offering ethics courses in data analytics and regulation of artificial intelligence (AI).

From the NY Times:

The idea is to train the next generation of technologists and policymakers to consider the ramifications of innovations — like autonomous weapons or self-driving cars — before those products go on sale.

“Technology is not neutral,” said Professor Sahami, who formerly worked at Google as a senior research scientist. “The choices that get made in building technology then have social ramifications.”

“We need to at least teach people that there’s a dark side to the idea that you should move fast and break things,” said Laura Norén, a postdoctoral fellow at the Center for Data Science at New York University who began teaching a new data science ethics course this semester. “You can patch the software, but you can’t patch a person if you, you know, damage someone’s reputation.”

AI and neural networks are great at identifying patterns from a murky mass of data. What is important to remember (and all too easily forgotten) is that the pattern is not reality. If a pattern indicates that a person is prone to commit a crime under certain circumstances, such predictions do not trump constitutional provisions that guarantee presumption of innocence.

From the Times:

The Harvard-M.I.T. course, which has 30 students, focuses on the ethical, policy and legal implications of artificial intelligence. It was spurred and financed in part by a new artificial intelligence ethics research fund whose donors include Reid Hoffman, a co-founder of LinkedIn, and the Omidyar Network, the philanthropic investment firm of Pierre Omidyar, the eBay founder.

The curriculum also covers the spread of algorithmic risk scores that use data — like whether a person was ever suspended from school, or how many of his or her friends have arrest records — to forecast whether someone is likely to commit a crime. Mr. Ito said he hoped the course would spur students to ask basic ethical questions like: Is the technology fair? How do you make sure that the data is not biased? Should machines be judging humans?

The data ethics courses now popular in universities like Stanford and Cornell are supposed to equip future industry leaders and innovators with the ethical framework that would allow them to properly view their work in relations to its effects on society. The hope is that there will be less Travis Kalanicks and more Tim Berners-Lees in the future.