Machine Intelligence is here.
We're now using computation to make all sorts of decisions, but also new kinds of decisions.
We're asking questions to computation that have no single right answers. They are subjective, and open-ended and value-laden.
We're asking questions like, "Who should the company hire?"
"Which update from which friend should you be shown?"
"Which convict is more likely to reoffend?"
"Which news item or movie should be recommended to people?"
Look, yes, we've been using computers for a while, but this is different.
This is a historical twist, because we cannot anchor computation for such subjective decisions the way we can anchor computation for flying airplanes, building bridges, and going to the moon.
You know, Are airplanes safer?
Did the bridge sway and fall?
There, we have agreed-upon, fairly clear benchmarks, and we have laws of nature to guide us.
We have no such anchors and benchmarks for decisions in messy human affairs.
What does Tufeci mean by "historical twist"? Computers are being used to solve subjective problems the first time in history.计算机有史以来第一次被用来解决主观问题。
Machine intelligence has been used for objective questions for many years.
Machine intelligence has been used in a wide range of fields.机器智能已被广泛应用于各个领域。
Ethical issues will disappear with the development of machine intelligence.
With the development of machine intelligence, algorithms are now being used to answer subjective questions.
... clearer benchmarks are available for all kinds of questions.
... all sorts of decisions should be made by computation.
... we have developed better guidelines on how to use it.
If something reflects your personal value, it is value-laden.
value-free, valuable, value-neutral.
To make things more complicated, our software is getting more powerful, but it's also getting less transparent and more complex.
Recently, in the past decade, complex algorithms have made great strides.
They can recognize human faces.
They can decipher hand writing.
They can detect credit card fraud and block spam, and they can translate between languages. They can detect tumors in medical imaging.
They can beat humans in chess and Go.
Much of this progress comes from a method, called "machine learning".
Machine learning is different than traditional programming, where you give the computer detailed, exact, painstaking instructions.
It's more like that, you take the system and you feed it lots of data, including unstructured data, like the kind we generate in our digital lives.
And the system learns by churning through this data.
And also, crucially, these systems don't operate under a single-answer logic.
They don't produce a simple answer; it's more probabilistic: "This one is probably more like what you're looking for."
Now, the upside is: this method is really powerful.
The head of Google's AI systems called it, "the unreasonable effectiveness of data".
The downside is, we don't really understand what the system learned. In fact, that's its power.
This is less like giving instructions to a computer; it's more like training a puppy-machine-creature we don't really understand or control.
So, this is our problem.
It's a problem when this artificial intelligence system gets things wrong.
It's also a problem when it gets things right, because we don't even know which is which when it's a subjective problem.
We don't know what this thing is thinking.
Which of the following best describes machine learning? It enables computers to process complex data and learn from it.
It copies human intelligence for subjective questions.
It finds solutions based on single-answer logic.
It improves people's understanding of how data is processed.
What is one characteristic of traditional programming? It require explicit instructions.
It gives reasonable answers to open-ended questions.
It enables computers to evolve by themselves.
It provides clear standards for subjective questions.
To make great strides means ... to achieve significant progress.
...to walk with steady steps.
... to talk with too much pride.
... to find out the top priority.
So, consider a hiring algorithm, a system used to hire people, right? using machine-learning systems.
Such a system would have been trained on previous employees' data, and instructed to find and hire people like the existing high performers in the company.
Sounds good.
I once attended a conference that brought together human resources managers and executives, high-level people, using such systems in hiring.
They were super excited.
They thought that this would make hiring more objective, less biased, and give women and minorities a better shot, against biased human managers.
Look, human hiring is biased.
I know. I mean, in one of my early jobs as a programmer,
my immediate manager would sometimes come down to where I was, really early in the morning or really late in the afternoon, and she'd say, "Zeynep, let's go to lunch!"
I'd be puzzled by the weird timing. It's 4 pm. Lunch?
I was broke, so free lunch, I always went.
I later realized what was happening.
My immediate managers had not confessed to their higher-ups, that the programmer they hired for a serious job was a teen girl who wore teen's jeans and sneakers to work.
I was doing a good job. I just looked wrong and was the wrong age and gender.
So, hiring in a gender- and race-blind way, certainly sounds good to me.
But, with these systems, it is more complicated, and here's why:
Currently, computational systems can infer all sorts of things about you, from your digital crumbs, even if you have not disclosed those things.
They can infer your sexual orientation, your personality traits, your political leanings.
They have predictive power, with high levels of accuracy.
Remember, for things you haven't even disclosed.This is inference.
What does Tufeci's personal experience with her immediate manager suggest? Human bias is a problem in workplace.
Many young people want to become programmers.
Flexible working hours increase efficiency.
Workplace hierarchies are often rigid.
A hiring algorithm would find and hire strong candidates by ... basing its criteria on existing employees.
...evaluating individual candidates' political inclination.
... examing data of managers.
... inferring the personality traits of each candidate.
