Private Mug Shots and Big Data
As the months roll by in 2014, it’s a reminder of how quickly things are changing around us. Issues of privacy are prevalent and Big Data knows our every move.
Whether we believe it or not there is basically always a camera, or a website, or a drone or a bot of some type scooping up our information.
Personal information is influencing how insurance companies treat us, whether or not we will get that job we are looking for and where you do your shopping.
An adversarial conversation might be should someone who gets arrested have the right to maintain privacy of their mug shot.
Read more about these topics in this issue and please contact me to professionally assess any of your inquiries.
Discrimination by Big Data
Discrimination comes in many forms and has always existed, however, today there is a new tool that allows for discrimination in, but not limited to, housing and employment. It’s BIG DATA.
A review in the Associated Press, which is soon to be published, states that both public and private firms are using collected data to discriminate against people by mapping where they live.
John Podesta, White House counselor, has undertaken a 90-day study of “big data” which was commissioned by President Obama in January of this year. Part of Obama’s proposal on this is: “…ask the FISC to renew the data collection program as-is for another 90-day cycle, according to senior administration officials who spoke with the NYT”. Read more about this here NSA reform efforts.
Civil Rights leaders raised the prospect of employers using data to map where job applicants live, fearing that bulk data information may lead to discrimination in hiring. For example, a candidate may live far from the place of employment and the employer may use that as an excuse not to hire that candidate: inferring that the candidate may not stay with the company because of the distance, or will be consistently late.
Identifying socio-economic groups by monitoring the use of social media, for example whether items are purchased online or at brick and mortar stores, enables the mapping of clusters that companies such as Experian use in order to target products at the most receptive sector, whether wealthy or financially vulnerable.
Clusters are mapped out and labeled: “Established Elite”, “Power Couples”, “American Royalty”, and “Just Sailing Along” or “X-tra Needy”, “Burdened By Debt: Singles”, “Meager Metro Means”, “Ethnic Second-City Strugglers”.
Experian, for example describes one label, “Hard Times” like this:
Older, down-scale and ethnically-diverse singles typically concentrated in inner-city apartments. This is the bottom of the socioeconomic ladder; the segment with the poorest lifestyle in the nation. ‘Hard Times’ are older singles in poor city neighborhoods. Nearly three-quarters of the adults are between the ages of 50 and 75; this is an underclass of the working poor and destitute seniors without family support. One-quarter of the households have at least one resident who is retired.
This type of discrimination, says Chris Calabrese, a lawyer with the American Civil Liberties Union, can turn predatory. Banks can target people who post something on social media about losing a job as being likely candidates for high-interest loans. Indeed, in such a difficult financial situation, a person may be open to a cash loan without fully considering the high interest rate.
You are individually targeted for a loan based on inclusion on one of these lists and get a high interest rate. That is in spite of the fact that if you walked in off the street you might qualify for a lower rate. You never know that you are being targeted individually since you just click on an ad on the side of a website. That is big-data discrimination in action.
Such bulk data analysis can have altruistic ends as well, such as that used by the New York City Fire Department which, in January, took into account some 60 different factors correlated with high likelihood of fires – including poverty, building age, electrical issues, presence of elevators, number and location of sprinklers – and built an algorithm that assigns a risk score to each one of the city’s 330,000 inspectable buildings.
Another good action taken was by the Memphis Police’s Blue CRUSH system by amassing data on every crime reported in the city which was used to track and map all crimes over time. Officers were sent to the areas that stood out when patterns of criminal activity emerged from the data. Over 6 years, the program was credited with reducing murders by 36%, robberies by 36%, and motor vehicle thefts by 55%. So the price for the good is compromised privacy. Unfortunately US Cities are ignoring privacy.
“According to AP, Podesta said that when he presents the new big-data usage review next week, he’ll recommend an update to the Electronic Communications and Privacy Act of 1986.”
Currently the law stipulates how the government can access private communications for law enforcement purposes and Nuala O’Connor, president of the Center for Democracy and Technology stated:
There are certainly gaps in the law. The technology is outpacing regulatory and legislative change.
Innocuous data is being collected in bulk, including our social media musings and our addresses, and they reveal far more than was intended by its original use. That data, in turn, can be used against us in disarmingly inventive ways.