Big Data Needs to Go Public
December 16, 2016
Big Data touches every part of our lives and we are unaware. Have you ever noticed when you listen to the news, read an article, or watch a YouTube video that people say items such as: “experts claim, “science says,” etc.” In the past, these statements relied on less than trustworthy sources, but now they can use Big Data to back up their claims. However, popular opinion and puff pieces still need to back up their big data with hard fact. Nature.com says that transparency is a big deal for Big Data and algorithm designers need to work on it in the article, “More Accountability For Big-Data Algorithms.”
One of the hopes is that big data will be used to bridge the divide between one bias and another, except that he opposite can happen. In other words, Big Data algorithms can be designed with a bias:
There are many sources of bias in algorithms. One is the hard-coding of rules and use of data sets that already reflect common societal spin. Put bias in and get bias out. Spurious or dubious correlations are another pitfall. A widely cited example is the way in which hiring algorithms can give a person with a longer commute time a negative score, because data suggest that long commutes correlate with high staff turnover.
Even worse is that people and organizations can design an algorithm to support science or facts they want to pass off as the truth. There is a growing demand for “algorithm accountability,” mostly in academia. The demands are that data sets fed into the algorithms are made public. There also plans to make algorithms that monitor algorithms for bias.
Big Data is here to say, but relying too much on algorithms can distort the facts. This is why the human element is still needed to distinguish between fact and fiction. Minority Report is closer to being our present than ever before.
Whitney Grace, December 16, 2016
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