Electrifying New York — Again

 

Thomas Edison famously electrified New York City in 1882 when the first commercial central power plant in the world went online at 255-257 Pearl Street.  It took most of the next three decades to spread the blessings of electric power through the rest of the city, but it got done without much help from any government.  Electric lights were cheaper, safer, and just better than gaslight or kerosene lamps, and little government intervention was required to persuade millions of New Yorkers that going electric was the thing to do.

 

New York City now faces a new kind of electrification:  the electric car.  As an article in the New York Times recently described, the futuristic vision of having only all-electric vehicles inside the confines of the five boroughs that make up New York is being realized slowly, if at all. 

 

One significant drawback is the lack of public charging stations.  Even after setting a modest goal of installing 120 new charging stations, the city ended up as #93 in a recent survey that rated 100 cities in terms of how electric-car-friendly they were.

 

New York City's commissioner of transportation Hank Gutman is determined to change the situation.  His commission issued a report calling for 1,000 curbside chargers by 2025 and 10,000 by 2030.  Every municipal parking lot will have one-fifth of its slots equipped with chargers, if the plans in the report are carried out.

 

One might ask if those slots will be reserved for electric cars only, of which there are presently only 20,000 registered in all of New York City.  If only electric cars can park in those slots, all this means for the old-fashioned gas-guzzler driver is that the municipal parking lots will effectively shrink by 20%.

 

The electric car is perhaps one of the few major mass-market items whose main selling point is ideological.  From a purely pragmatic individual point of view—whether you are looking at personal safety, saving money, or convenience—there is really nothing an all-electric car can offer that a gasoline model can't also offer.

 

The ideological reason to buy an all-electric car is that it is one small step for a car buyer, but multiply that by a billion or so and it will be a giant leap toward a fossil-fuel-less future in which global warming is defeated.  And this reason cannot be discounted, because I think it is one of the main reasons people currently buy electric vehicles.

 

Whether it makes sense for someone to spend an extra ten to thirty thousand dollars on a car that requires careful logistical planning to make it between charging stations and may not in fact reduce carbon emissions at all if the local electric utility burns coal, is a decision that individuals are free to make.  But so far, despite the growing sales figures of upstarts such as Tesla, the prospect of gasoline vehicles going the way of kerosene lamps by 1910 actually looks pretty reasonable, if you give it another three or four decades.

 

In 1910, there were still lots of people who used kerosene lamps, and it would be another twenty or thirty years before such things were found only in extremely rural areas.  And it would take government intervention, in the form of the Rural Electrification Administration, to bring electricity to the remaining rural areas without electric power.  Still, nobody was forced to put away their kerosene lamps and get connected.  People in rural areas had to wait longer because it cost more to install the lines than in urban areas, but they still wanted electricity as much as their city cousins did.

 

Until all-electric vehicles are cheaper and easier to buy and operate than gasoline-powered ones, it will be like pushing on a string to get most people to buy one.  Some of the string moves when you push on it, but most of it doesn't.  There are those who feel that the chronic global-warming emergency is so urgent that fossil fuels should be effectively banned—taxed out of existence or otherwise made inaccessible to the average person.  This would represent a draconian market intervention by governments in an area where government has not exactly covered itself with glory, judging by similar historical interventions such as the price controls during the gasoline crisis of the 1970s.

 

The technological optimists among us (and on some days I count myself in that number) look to a day when some new and currently unthought-of technology improves battery storage capacity by another factor of 10 and lowers the price by the same factor.  If that happened, electric cars would simply out-perform and undercut the price of gasoline vehicles, which hold the record as being the most complicated mass-produced human-sized object in history. 

 

By contrast, the entire drive train of an electric car is a battery, some electronics, and electric motors hooked to the wheels.  The rest is software, and we all know how cheap software is.  I don't think we'll get to the point that companies will give away electric cars for free as long as you put up with the ads, but it might come close.

 

At that point, we won't need government subsidies or carbon taxes or prohibitions to make the transition from gas to electric vehicles.  People will want to do it of their own free will, and the market will be more than happy to oblige.  But it might not happen for a while yet.

 

One of the most scarce commodities these days is patience.  Even with the vastly superior performance of electric lighting, which was not cheaper than gas to begin with, it took the better part of four decades before most people were able to make the transition.  Heavy-breathing global-warming alarmists may say, "We don't have four decades! We've got to do something now!!"  We are just emerging from the results of two years of governments "doing something now" to fight COVID-19, and offhand I can think of only one of those things that had an unequivocally positive effect on the outcome:  the rapid development of vaccines.  Most other actions arguably did more harm than good, or at least mixed in a lot of harm with the good. 

 

Let's not make that mistake again.

 

Sources:  Ginia Bellafante's article "New York's Electric Car Future Faces Several Challenges" appeared in the Mar. 13, 2022 edition of the New York Times.

Judging the Judgments of AI

 

If New York City mayor Bill De Blasio allows a new bill passed by the city council to go into effect, employers who use artificial-intelligence (AI) systems to evaluate potential hires will be obliged to conduct a yearly audit of their systems to show they are not discriminating with regard to race or gender.  Human resource departments have turned to AI as a quick and apparently effective way to sift through the mountains of applications that Internet-based job searches often generate.  AI isn't limited to hiring, though, as increasing numbers of organizations are using it for job evaluations and other personnel-related functions. 

 

Another thing the bill would do is to give candidates an option to choose an alternative process by which to be evaluated.  So if you don't want a computer evaluating you, you can ask for another opinion, although it isn't clear what form this alternative might take.  And it's also not clear what would keep every job applicant from asking for the alternative at the outset, but maybe you have to be rejected by the AI system first to request it.

 

In any case, New York City's proposed bill is one of the first pieces of legislation designed to address an increasingly prominent issue:  the question of unfair discrimination by AI systems. 

 

Anyone who has been paying attention to the progress of AI technology has heard some horror stories about things as seemingly basic as facial recognition.  An article in the December issue of Scientific American mentions that MIT's Media Lab found poorer accuracy in facial-recognition technologies when non-white faces were being viewed than otherwise. 

 

Those who defend AI can cite the old saying among software engineers:  "garbage in, garbage out."  The performance of an AI system is only as good as the set of training data that it uses to "learn" how to do its job.  If the software's designers select a training database that is short on non-white faces, for example, or women, or other groups that have historically been discriminated against unfairly, then its performance will probably be inferior when it deals with people from those groups in reality.  So one answer to discriminatory outcomes from AI is to improve the training data pools with special attention being paid to minority groups.

 

In implementing the proposed New York City legislation, someone is going to have to set standards for the non-discrimination audits.  Out of a pool of 100 women and 100 men who are otherwise equally qualified, on average, what will the AI system have to do in order to be judged non-discriminatory?  Picking 10 men and no women would be ruled out of bounds, I'm pretty sure.  But what about four women and six men?  Or six women and four men?  At what point will it be viewed as discriminating against men?  Or do the people enforcing the law have ideological biases that make them consider discriminating against men to be impossible?  So far, none of these questions have been answered.

 

Perhaps the best feature of the proposed law is not the annual-audit provision, but the conferral of the right to request an alternative evaluation process.  There is a trend in business these days to weed out any function or operation that up to now has been done by people, and replace the people with software.  There are huge sectors of business operations where this transition is well-nigh complete. 

 

Credit ratings, for example, are accepted by nearly everyone, lendors and borrowers alike, and are generated almost entirely by algorithms.  The difference between this process and AI systems is that, in principle at least, one can ask to see the equations that make up one's credit rating, although I suspect hardly anyone does.  The point is that if you ask how your credit rating was arrived at, someone should be able to tell you how it was done.

 

But AI is a different breed of cat.  For the newest and most effective kinds (so-called "deep neural networks") even the software developers can't tell you how the system arrives at a given decision.  If it's opaque to its developers, the rest of us can give up any hope of understanding how it works. 

 

Being considered for a job isn't the same as being tried for a crime, but there are useful parallels.  In both cases, one's past is being judged in a way that will affect one's future.  One of the most beneficial traditions of English common law is the custom of a trial by a jury of one's peers.  Although trial by jury itself has fallen on hard times because the legal system has gone in for the same efficiency measures that the business world goes for (some judges are even using AI to help them decide sentence terms), the principle that a human being should ultimately be judged not by a machine, but by other human beings, is one that we abandon at our peril.

 

Theologians recognize that many heresies are not so much the stating of something that is false, as they are the overemphasis of one true idea at the expense of the other true ideas.  If we make efficiency a goal rather than simply a means to more important goals, we are going to run roughshod over other more important principles and practices that have given rise to modern Western civilization—the right to be judged by one's peers, for example, instead of by an opaque and all-powerful algorithm. 

 

New York's city council is right to recognize that AI personnel evaluation can be unfair.  Whether they have found the best way to deal with the problem is an open question.  But at least they acknowledge that all is not well with an AI-dominated future, and that something must be done before we get so used to it that it's too late to recover what we've lost.

 

Sources:  The AP news story by Matt O'Brien entitled "NYC aims to be first to rein in AI hiring tools" appeared on Nov. 19 at https://apnews.com/article/technology-business-race-and-ethnicity-racial-injustice-artificial-intelligence-2fe8d3ef7008d299d9d810f0c0f7905d.  The Scientific American article "Spying On Your Emotions" by John McQuaid (pp. 40-47) was in the December 2021 issue.