Broadband Redlining

The National Digital Inclusion Alliance (NDIA) recently asked the FCC to investigate the practice of digital redlining, where big ISPs only bring the best technology to more affluent neighborhoods while ignoring poor ones.

The NDIA has statistics to back up it’s claims. They used the FCC’s data in 2017 to look in detail at how AT&T had deployed DSL in Cleveland. Years ago, AT&T deployed the first generation of DSL almost everywhere in the market. However, the company became far more selective in where they upgraded to faster DSL.

NDIA mapped where AT&T had deployed VDSL and later DSL technologies in Cleveland and found that the company had deployed faster DSL mostly in affluent neighborhoods and in the suburbs while leaving older downtown neighborhoods with the older DSL. VDSL offers speeds of at least 18 Mbps, up to nearly 50 Mbps when deployed using two copper pairs. NDIA found that the 55% of the census blocks in downtown Cleveland still had DSL speeds of 6 Mbps or less while 22% had speeds below 3 Mbps, with some as slow as 768 Kbps. It’s likely that AT&T marketed all versions of DSL the same, advertising ‘up-to’ speeds that described the fastest product in the market.

The AT&T deployment in Cleveland is not an isolated incident and the same is true in communities across the country. It’s not just AT&T that’s done this and Verizon deployed its fiber FiOS product in a similar manner and largely ignored northeast downtowns in favor of serving suburbs. We also tend to think of cable company networks being deployed ubiquitously in cities, but there are pockets in every major city that don’t have cable broadband.

In the industry this practice is generally referred as cherry-picking. It means deploying a new network in the places where the costs are lower or the expected penetration rates are higher – and ignoring the parts of a market that don’t fit a desired financial profile.

Historically the big telcos weren’t allowed to cherry-pick or redline. AT&T was still largely a regulated company when the first DSL was deployed. But the trend over time to deregulate telephone providers has led to laxer regulation, and obviously in Ohio and many other states the telcos were not required to build later generations of DSL everywhere.

One of the reasons we see so much cherry-picking is that many states have adopted statewide cable franchising. Cable franchises were historically negotiated in each community, and cities insisted that a cable provider build to the whole community as a condition for getting a franchise. However, AT&T and other broadband companies lobbied hard for statewide franchising rules, using the storyline that they wanted to deploy fast DSL to bring cable service. The statewide franchises generally give a cable provide the ability to build anywhere in a state. The telcos argued that the cost of negotiating with every community was killing innovation and deployment of faster broadband. What these companies really wanted was the ability to cherry-pick with no obligation to serve whole communities.

The practice of cherry-picking is still common today and most commercial fiber overbuilders engage in it to some degree. Most overbuilders have limited financial resources and they deploy fiber or other broadband technologies in those places where they get the best return for their investment. Many communities have seen fiber built to businesses and to new subdivisions while ignoring the rest of the town.

It’s hard to fault s smaller fiber overbuilder for maximizing the return on their fiber investment. On the flip side, there are few communities that don’t want fiber everywhere. However, most communities are realistic and know that if they always insist on getting fiber everywhere they might not get it anywhere.

Communities that really care about good broadband everywhere are the ones that are building fiber themselves or trying to attract a partner that will build the whole community. However, there are numerous states that hinder or prohibit communities from building broadband networks, and many other cities find the costs to build new networks to be prohibitive. The majority of communities must rely on the good behavior of the incumbents, and unfortunately they don’t always do the right thing.

The Downside of Big Data

DARPA_Big_DataBig tech companies have been crowing about some of the amazing things that can be done using big data. For example, in the area of interacting with people, retailers are working hard to create personalized shopping experiences aimed at individual shoppers. Specials will pop up on cell phones as someone walks by a display that are aimed at them specifically. While many will feel this is an invasion of privacy, others are looking forward to an enhanced shopping experience. Big data promises to also personalize things like health care so that every doctor you ever see will truly understand your health history and they can guard against conflicting medicines and other things detrimental to your health.

But there are already downsides to big data. Big data is being used to put together a detailed portrait of everybody. And that leads to various degrees of profiling. The very same data that can be used to make your shopping experience better can also be used for many negative purposes. Consider some of the following examples:

  • The Chicago Police department apparently used big data to create a list of the 400 people in the community that they think are most likely to commit a murder. But then they went so far as to contact these people to tell them they were watching them. If anybody remembers the movie Minority Report, this feels like we are already reaching that time where the police convict people for crimes they are going to commit in the future.
  • Big data contains a lot of information about us – our age, race, sexual orientation, religion, weight, general health, number of kids or pets, state of our finances, etc. That kind of data can be easily used to discriminate against people in a variety of settings. We start entering a scary societal place when we use this kind of data to profile people for consideration for housing, employment, etc. There is already an industry of firms who sell this kind of profiling data to anybody for a fee. Where a prospective landlord used to check your credit report they can now find out everything about you. Let’s face it – people are bigoted or just biased and the availability of this kind of data makes it easy to redline or discriminate.
  • There is a big uptick in scams against the elderly who are being found through big data. The scams themselves are as old as the hills, but it’s the use of big data to identify the most vulnerable among us that is disturbing.
  • It was reported in 2012 that Staples displays different on-line prices to different customers based upon where they live. For example, customers who live close to a competitor might get cheaper prices than somebody who does not. But this same ability makes it easy to price differently based upon other factors and again can lead to redlining.
  • I have read where it is fairly easy to buy databases of people who have something in common – such as having diabetes, having tried to quit smoking, or nameless other traits. These lists can be used to market products specific to an ailment, but they also have been used for scams, blackmail and other nefarious purposes. It’s not hard to picture being able to take advantage of people with a gambling addiction or some other such problem.
  • The FAA’s Do Not Fly list is another result of big data and is notorious for containing names of toddlers and others who are obviously not a threat to national security. The list even ended up including several US Congressmen.

This all points to the need for some sort of legal protection of people from the misuse of big data. This is a hot topic in Europe right now but is not yet commonly debated here. Several civil rights groups have identified big data as a big threat and a new source for discrimination. But misuse of big data can go far beyond discrimination based upon race, religion or sexual orientation. Unfortunately it’s now possible to discriminate based upon a whole lot of other reasons as well.