Projecting Fragility

It’s finally happening. Trumps wave of hate crimes are rolling in starting with the ban of heroic transgenders from the military. Only problem is that Trump’s base still doesn’t seem to see the problem. The disagreement on the nature of the problem is actually more interesting than the problem it’s self. Let me explain.

Trans have, are, and will continue to serve in our military to some extent and in some fashion. They were not allowed before, and still managed to find ways to serve, and may be banned again, and still find ways to serve under the radar.

What is being ruled out is transgenders openly serving. This is not like the Tuskegee Airmen being allowed to fly despite the color of their skin. A ban on transgender does not actually bar them from serving. It bars certain types of behavior. The whole argument is over behavior, not the nature of transgender as a life.

The pro-argument goes like this: Trans people are people and should be afforded all the same opportunities as everyone else. One such opportunity is to serve in the military. Under the ban, a dedicated soldier could be dishonorably discharged or even court-marshaled just for the fact that they identify differently. That is discrimination

The con-argument goes like this: Anyone who can be a combat effective soldier should be allowed, but trans people have major complications that routinely distract and hinder combat effectiveness. They are fragile, possibly mentally ill, and complicate every rule set in place. If they really want to serve so bad, they can suck it up and pretend to be normal.

The key to all this is that transgender is an identity. Being a soldier is in part about dropping one’s identity and becoming a piece in the greater whole. If trans feel that giving up an open expression of their identity is enough to keep them out of the military, then they should not be in the military. Even if that identity is deeply rooted, it’s not a part of the soldiers’ job to express it. Having sex is another deep;y rooted behavior that the military does not permit while on the clock.


Is Trump Fixing the Environment?

Just a small follow up on my previous post about the Paris Accord: Trumps initiative to withdraw from a meaningless agreement has lead a number of large US companies to commit to greener operations.

AppleAmazon, Facebook, Google, Tesla, Lyft, and Uber are among the U.S. companies that have added their names to the “We Are Still In” campaign

In the absence of leadership from Washington, states, cities, colleges and universities and businesses representing a [sizable] percentage of the U.S. economy will pursue ambitious climate goals, working together to take forceful action and to ensure that the U.S. remains a global leader in reducing emissions

Just as was pointed out a number of times, the effects of the Paris accord were voluntary for the most part. Anyone who wanted to could still abide by it’s guidelines without having to shell out $100 Billion annually.

What is most interesting about this is that in a perverse way, Trump has managed to spark a green revolution in US Industry. The hatred everyone feels for Trump has lead to them becoming better businesses out of spite.

The question is: Is Trump fixing the environment? Is his strategy of rolling back Washington’s control over the matter impelling private companies and citizens to do their part? If so, was it intentional? We already know just talking about immigration during the campaign had a sizable effect on illegal immigration from mexico. Trumps words might be stronger than his actions.

In the same vein, Trump has announced that Apple is opening three new plants in the US

I spoke to [Mr. Cook], he’s promised me three big plants — big, big, big

Notice how that doesn’t sound like Tim Cook announcing three new plants. It’s Trump pigeon holing Cook into doing it. I see three options:

  • Apple makes the plants
  • Cook gives trump the middle finger because that’s whats expected of him, even if his original plan was for the plants
  • Apple commits to the the plants and then quietly never builds them

That last one seems most likely to me. It’s still a win for us though because Apple committing to US production sends a big message that America is open for business.

How to Elect Hitler

Since the outset of the election cycle and well into the first year of Trump’s presidency, we’ve seen the rise of a special type of political commentary: Tump apologetics. Trump-explainers or apologists, what ever you like to call them, are ideologues who spend their time telling a story about Trump that is counter to the current mainstream notion that our president is a sea-monster come on land. Most notable of these apologists is the cartoonist Scott Adams. He’s extremely convincing in his assurances that Trump is doing something other than tottering around knocking over buildings.

The problem with this is that there is a non-zero chance that Trump is Hitler. I’m personally of the opinion that he is not, but hey, enough Germans thought Hitler wouldn’t turn out to be Hitler. It’s clear that even monsters can be persuasive and charismatic leaders, otherwise we’d never have a problem.

So if Trump were Hitler after all, we’d probably feel pretty stupid for having supported him. We’d ask ourselves how we didn’t see the long line of awful terrible things he had done that lead up to his final ascension to God-Emperor of the Fourth Reich. And then we would realize, it was the fault of the Trump-Explainers for making his sins palatable.

Still, there is a certain set of circumstances that favor the rise of King-Trump. The Apologists alone cannot be blamed for his success. We must look at the factors necessary to allow the election to office and continued support of Litterally-Hitler.

(1) In order to elect Hitler, you have to make the opposition to Hitler worse than Hitler. At the very least, you have to make the opposition seem worse to the people who are likely to form Hitler’s support base. This means fill the opposition with harpies.

(2)Hammer on the small details, ignore the big stuff. If the opposition spends its time trying to make the monster down by biting at his ankles, it’s going to make them look petty. It’s going to flood everyone’s mind with petty shit until they basically ignore any valid negative info being broadcast about Hitler. At this point, most of the major blunders Trump has made have been drowned out by hysterical repetition of “Covefefe!”

(3) Make people okay with demolishing existing power structures. Hitler did one thing particularly well, took out his political opposition until he could move unhindered. Trump has promised to drain the swamp and his base is ecstatic about the notion. Congress has begun to look like an immobile heap of corruption. The judiciary looks like rouge Marxist sophomores in robes. Neither one is interested in upholding the constitution and bettering the lives of the people. At least that’s how things look to Trump’s base, and their ready to get rid of those roadblocks. What is not being thought of is the fact that those are the same roadblocks that stand between Hitler and launching his nation into global war.

And for those who suggest that draining the swamp is about reducing corruption, consider how difficult it would be to do. Term limits would fill the rows with new, weak, pliable members who know that their only hope of influence is to play ball with the current administration.

The conditions are right for another Hitler to strike, but it’s not the fault of Hitler himself. It is the fault of the opposition team and the incumbent political players for making people willing to toy with such forces.

The Ultimate Process/Outcome Thinking Test

I’m constantly on the look out for good examples of the divide between those who utilize process thinking and those who use outcome thinking. It’s not a new concept, but it caught my attention as being one of the purest examples of this dichotomy.

Take a look and tell me what you think of this article:

If you think it might be a legitimate concern and future AI should be designed to negate this effect, you’re a dirty outcome thinker.

If you think machine learning is pure and any bias shown is just a result of real world outcomes, you’re still a dirty outcome thinker.

Here is why: the mechanism of bias in machine learning is never reveled . There are two basic ways a machine can learn. Either (A) the system is learning from raw data and making predictions about outcomes on it own, or (B) the system is learning from a human counterpart and and copying their performance.

Lets take granting a loan for example.

A is troubling because there’s a possibility the system is making accurate predictions about real outcomes. It’s possible that on average, people of a certain race, background, or even eye color, have a higher default rate. The system wouldn’t know why that is, it would just know the smart money isn’t on those people.

B is just down right dumb. A system is taught, not using real world data, but rather using the history of human decisions, it will never learn anything humans don’t already know. It will just learn to be very good at playing human. Machine learning scientists don’t usually take this approach for that reason, so the bias is usually not derived from human decision, but rather from historical data.

A is still dumb because it’s looking at historical data hoping that it will be able to infer future outcomes. It’s not concering itself with why a trend exists or even if the information being processed is relevant. Machine learning can come up with some crazy accurate correlations in historical data that have zero predictive power. Understanding a process, a reason why, is necessary to predict the future. That’s why machine learning alone is not the solution to better loan application processing.

Black people being turned down for loans is not symptom of a racially biased system. It’s the symptom of a system designed for the sole purpose of turning people down. The computer’s job is to find ways to discriminate. The idea that you might ask a computer to select half a group of applicants to reject, and then become confused when the group selected showed similarities in at least some respects is appallingly stupid.

Here is why the talk of bias in machine learning is a red flag for process thinkers:It’s rejecting the outcome of a system that does nothing but analyse outcomes.

Machine learning based selection processes like loan approvals and parole appeals are just another facet of the paper man problem I wrote about previously. A machine is only able to make selections based on digitized information. All ability to use social persuasion, character reference, or intellectual debate to effect the outcome of a decision is gone. It further strengthens the selections for men and women who lack real world skills but display exemplary resumes.



The Wage Wall

Rather consistently we hear mentions to the gap in pay between women and men. The line that is most often use is that women make $.75 for every man’s dollar. This of course has been soundly refuted by a number of studies that show the apparent difference in pay is more a result of career and life choices than systemic oppression.

What I find more interesting than the truth of the statistic, is it’s persistence. You would think if there were such a noticeable difference, it would be clear as day. And yet the effects, if ever there were any, remain invisible to us. We need the use of sophisticated statistics to show what should be everyday common knowledge. The wage gap is supposed to be pervasive meaning people should be able to tell if they are direct victims of it or not. But we can’t and the reason for that is much more dangerous that the issue of the wage gap.

Employers work hard to obfuscate wages. Culture has a shyness towards discussing the topic of wages. Everything in society is fashioned around the idea of keeping wage information compartmentalized and isolating workers. Let’s imagine that there was a wage gap, it would be great for employers. If they could cut costs on personnel by 25%, you bet they’d do it, sexism or no.

The ability of employers to segment their workforce and manipulate wages without transparency should worry everyone, not just women. and this is where I see the divide between process thinkers and outcome thinkers.

Right now the outcome is being shouted from the roof-tops and presidential podiums – It’s bad that employers are paying women unfairly. The acceptable solution to this is that women not be paid unfairly. A mandate might be put in place that wages not be assigned with any knowledge of the employee’s gender. There might be audits to asses any statistical preferential treatment of one sex over the other. It’s possible that there is a good solution to fix this exact problem.

But the process thinker is more concerned with how the problem might come about in the first place. Why are employers able to operate with such impunity that they could segment a half of the workforce into a lower wage bracket? Why, on a case by case basis would workers accept these lower wages? Why is the force of the free market favoring job creators over job workers?

The most robust answer to all of these questions is that we have begin wage comparison. It needs to become not only acceptable but encouraged to discuss wages with one’s peers. This will not only fix any sex wage gap that might exist, but any race, orientation, marital status, and what not discrimination.  Most importantly, it will put a stop to the A/B testing style wage assignment.