Thus we get quotes from them like "overpopulation on Mars"
I said that people whose expertise is in being experts on what others should do are going to not do a good job.
Let's take a concrete recent example.
http://time.com/5209144/google-search-engine-algorithm-bias-... decries how Google search results are racist and are biased against black girls. It is written by someone whose background is clearly not machine learning, but who qualifies as an expert in the ethics of machine learning. She has a PhD, tenure at a well-regarded university, and a book on the subject.
While her article complains about the results of machine learning algorithms, and how horrible they are, she said nothing that showed an understanding of how they actually work. Why they produce the results that they do. Her diagnosis is that Google is a racist and sexist company. Her proposed solution is that Silicon Valley companies should be forced to hire blacks, and particularly black women, for the express purpose of keeping companies accountable for their racist environments.
The truth is that Google's internal environment is neither racist nor sexist - quite the opposite in fact. The actual problem is in the data available to Google of what people click on. How you can modify the algorithm to be slanted towards socially acceptable results is a tricky problem that requires a CS background. How you can modify the incentives so that companies are motivated to produce good results is an even more tricky problem.
Yet if she got to create the regulations, what would happen? We would literally get a requirement to hire politically correct thought police!
How well do you think that would work?
What she did say is that it's not surprising that Google search produces biased results, given that minorities are underrepresented and topics like ethics are barely discussed in engineering schools.
It sounds to me like she is attempting to raise awareness of a systemic issue in the tech industry, which requires no qualifications other than knowledge of the issue.
> We need people designing technologies for society to have training and an education on the histories of marginalized people, at a minimum, and we need them working alongside people with rigorous training and preparation from the social sciences and humanities. To design technology for people, without a detailed and rigorous study of people and communities, makes for the many kinds of egregious tech designs we see that come at the expense of people of color and women.
So perhaps not a direct call to hire minorities, but a direct call to hire people versed in identity politics and various non-white studies.
- The company is bad because it doesn't hire enough black women.
- The company needs to hire people versed in identity politics to critique what they are doing.
- Programmers should be required to take the kind of course that she teaches.
- Programmers should be only allowed to work alongside the people versed in identity politics who can tell them what they are doing wrong.
In short, this is a power grab. "They done wrong, so people like me need to be given authority to make sure that they don't continue doing it."
That may seem like an extreme example, but it is along a natural progression for people whose profession is figuring out what should be complained about. Everyone wants to believe that what they do is important. That they should be given more power and authority. What that means in practice varies by profession. But that is always the tendency.
I'm trying to picture if the government went around to all highly qualifying AI PhDs asking them if they would like to join a prominent ethics panel to guide and aid in AI ethics regulation in industry. So, you're claiming they're going to decline that or accept and sabotage under the assumption that they're a. all members of a giant AI consortium and b. any ethics regulation is going to destroy their industry?
As bad of an example as this is - look at other industries. Banking officials in government who set banking policies come from the finance industry. While this certainly has its problems and how they get there comes into question, they actually understand the industry they're regulating.
We already have an ethics discipline. This is an ethics problem.
Do you also propose that for example self-driving cars accidents should be investigated by AI PhDs which worked on self-driving cars algorithms, and they should be the ones proposing regulations? Are you saying that a regular "car accident specialist" is ill equipped to handle these, and it's opinions aren't as valuable because he's an expert in "human car accidents" not "AI car accidents"?
There is a legal term for what you are describing - it is called "conflict of interest". No, I do not think professionals should be involved in making decisions in matters in which they have a conflict of interest. Not all AI researchers are stakeholders in Uber, Waymo, or Tesla, hence you would select those people to investigate, as you would analogously do in any other matter of human affairs.
In fact, I'll go a step further and say that an "AI specialist" should be working alongside a "car accident specialist" and "laywers". I am arguing that this is a better combination than "Ethics specialist", "car accident specialist" and "lawyers" to investigate the accident.
Think if this would work in finance: asking JP Morgan people to investigate the Barclays Libor scandal. Smart people understand that a scandal against one of their peers will taint them also.
Do you hear today Google/Amazon/.... or other big private data hoarders makeing noise against Facebook? The one which spoke, Apple, doesn't have and rely on private data as much.
I would say that a serious ethics panel would have opposed the way Facebook handles private data, in a way that engineers working on them don't. Because engineers didn't study ethical/political problems of the past, and they don't understand how being lax with this can cause harm.
BTW, in all universities, doing studies on humans/animals requires first getting approval from an Ethics comitee. Googling now a bit I see that they have people from all faculties - Engineering, Humanities, Law, ... So universities, which give those AI PhDs consider that it's valuable to have a diverse ethics panel, not just one composed of Engineering professors if they meet to discuss an engineering experiment ethics.
Further, comparing the reactions of large companies with divergent interests to scandals is only tangentially relevant; that does not imply anything meaningful of the bias of hiring autonomous individuals from divergent backgrounds in AI; they do not all work on autonomous cars or <sell your data> at Big N. Finance has different social conventions as well.
"engineers didn't study ethical/political problems of the past, and they don't understand how being lax with this can cause harm" - I would wager that the average CS professor at a top school is significantly more well-read in history, ethics, and many other subjects than the "AI Ethics major" being proposed. But okay, let's set up an AI McCarthy panel with "experts", we'll get bored of Trump scandals on TV eventually.
This sounds like the jokes of when a plumber comes in to fix the pipes of a physicist, and the physicist is like "Whats so hard here? It's just basic physics"
Wait, what? To become a professor you have to spend the vast majority of your time teaching and/or studying/producing research in your specific discipline. How do you reach the conclusion that someone who doesn't exclusively focus on a topic would be more well-read on it than someone who does?
As btilly is referencing above, you already have a clear example of what type of people this field will attract: people with poor intellectual ability and zero understanding of inefficient, large-scale bureaucracies, basic probability or statistics, or software systems, painting simple organizational ineptitude as deliberate malfeasance and racism with the intent of bolstering themselves and their social group financially. It's clear there is an issue and it needs to be addressed as a growing pain, but deliberately training moral police with power over the software industry may lead to something like a dark age in technology.
Context, please? :)
It is such a distant threat and yet such a focus, and all the while we sit in our lovely apartments 40 minutes from nuclear incineration.
the conflation of machine learning and AGI causes confusion yet again..
https://nickbostrom.com/papers/survey.pdf
>We thus designed a brief questionaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity.