It Is Way Past the Time to Update the Communications Act of 1996

social media
Image by Pete Linforth from Pixabay

If you have been using the Internet for the past 25 years, you know how radically it has changed. And yet, no comprehensive regulations have been updated since then.

The news is full of complaints about tech companies getting too big and too powerful. Social media is often the focus of complaints. We often hear that these companies are resistant to changes and regulations, but that is not entirely true. 

On Facebook's site concerning regulations, they say "To keep moving forward, tech companies need standards that hold us all accountable. We support updated regulations on key issues."

Facebook may be at the center of fears and complaints, but they keep growing. Two billion users and growing.

There are four issues that address that they feel need new regulations.

Combating foreign election interference
We support regulations that will set standards around ads transparency and broader rules to help deter foreign actors, including existing US proposals like the Honest Ads Act and Deter Act.

Protecting people’s privacy and data
We support updated privacy regulations that will set more consistent data protection standards that work for everyone.

Enabling safe and easy data portability between platforms
We support regulation that guarantees the principle of data portability. If you share data with one service, you should be able to move it to another. This gives people choice and enables developers to innovate.

Supporting thoughtful changes to Section 230
We support thoughtful updates to internet laws, including Section 230, to make content moderation systems more transparent and to ensure that tech companies are held accountable for combatting child exploitation, opioid abuse, and other types of illegal activity.

The Telecommunications Act of 1996 was the first major overhaul of telecommunications law in almost 62 years. Its main goal was stated as allowing "anyone [to] enter any communications business -- to let any communications business compete in any market against any other." The FCC said that they believed the Act had "the potential to change the way we work, live and learn." They were certainly correct in that. But they continued that they expected that it would affect "telephone service -- local and long distance, cable programming and other video services, broadcast services and services provided to schools."

And it did affect those things. But communications went much further and much faster than the government and now they need to play some serious catchup. It is much harder to catch up than it is to keep up. 



coin tossI took one course in statistics. I didn't enjoy it, though the ideas in it could have been interesting, the presentation of them was not.

I came across a video by Cassie Kozyrkov that asks "What if I told you I can show you the difference between Bayesian and Frequentist statistics with one single coin toss?" Cassie is a data scientist and statistician. She founded the field of Decision Intelligence at Google, where she serves as Chief Decision Scientist. She has another one of those jobs that didn't exist in my time of making career decisions.

Most of probably had some math teacher use a coin toss to illustrate simple probability. I'm going to toss this quarter. What are the odd that it is heads-up? 50/50. The simple lesson is that even if it has come up tails 6 times in a row the odds for toss 7 is still 50/50.

But after she tosses it and covers it, she asks what is the probability that the coin in my palm is up heads now? She says that the answer you give in that moment is a strong hint about whether you’re inclined towards Bayesian or Frequentist thinking.

The Frequentist: “There’s no probability about it. I may not know the answer, but that doesn’t change the fact that if the coin is heads-up, the probability is 100%, and if the coin is tails-up, the probability is 0%.”

The Bayesian: “For me, the probability is 50% and for you, it’s whatever it is for you.”

Cassie's video about this goes much deeper - too deep for my current interests. However, I am intrigued by the idea that if the parameter may not be a random variable (Frequentist) you can consider your ability to get the right answer, but if you let the parameter be a random variable (Bayesian), there's no longer any notion of right and wrong. She says, "If there’s no such thing as a fixed right answer, there’s no such thing as getting it wrong."

I'll let that hang in the air here for you to consider.

If you do have an interest to go deeper, try:
Frequentist vs Bayesian fight - your questions answered
An 8 minute statistics intro
Statistical Thinking playlist
Controversy about p-values (p as in probabllity)


Law of Large Numbers

Image by Thomas Wolter from Pixabay

A recent episode of the PBS program NOVA took me back to my undergraduate statistics course. It was a course I didn't want to take because I have never been a math person and I assumed that is what the course was about. I was wrong. 

The interesting episode is on probability and prediction and its approach reminded me of the course which also turned out to be surprisingly interesting. Program and course were intended for non-math majors and the producers and professor focused on everyday examples.

I suggest you watch the NOVA episode. You will learn about things that are currently in the news and that you may not have associated with statistics, such as the wisdom of crowds, herd immunity, herd thinking and mob thinking.

For example, the wisdom of crowds is why when a contestant on a Who Wants to Be a Millionaire type of programs asks the audience and out of a few hundred people 85% answer "B," then there's an excllent chance that "B" is the correct answer. And larger samples get more accurate. Why is that?

One of the things I still recall from that class that the program highlighted was the law of large numbers. The law of large numbers states that as a sample size grows, its mean gets closer to the average of the whole population. It was proposed by the 16th century, mathematician Gerolama Cardano but was proven by Swiss mathematician Jakob Bernoulli in 1713.

It works for many situations from the stockmarket to a roulette wheel. I recall that we learned about the "Gambler’s Fallacy." The fallacy is that gamblers don't know enough math, or statistics. They stand by the wheel and see that red has won once and black has now won 5 times in a row. Red is due to win, right? Wrong. The red and black is the same as a coin flip. The odds are always 50/50. The casino knows that. They even list which color and numbers have come up on a screen to encourage you to believe the fallacy.

Flip the coin or spin the wheel 10 times and if could be heads or reds 9 times. Flip or spin 500 times and it will come out to be a lot closer to 50-50.

The "house edge" for American Roulette exists because there is that double zero on the wheel. That gives the house an edge of 2.70%. The edge for European roulette is 5.26%. 

Knowing about probability greatly increases your accuracy in making predictions. And more data makes that accuracy possible.