AI algorithms
intrude
into our lives:
we need
ALGORITHIC
TRANSPARENCY
More than 3 billion individuals now have access to the internet and every minute an estimated 300 hours of new videos are uploaded to YouTube, 350.000 tweets are produced on Twitter, 4.200.000 posts are made on Facebook, 1.700.000 photos are upload on Instagram, 110.000 calls are made on Skype, etc.
On average, over the last 30 years, the data generated has quadrupled every three years or so. This enormous rate of growth in data – accompanied by the spread of cloud computing, which provides access to great computing power at low cost, and the overarching development of AI and machine learning (ML) algorithms in particular – on the one hand make the entire knowledge of the universe available "free of charge" to the individual and on the other hand allow Big Tech to implement increasingly sophisticated algorithms thanks to the personalised data that each of us, consciously or not, spreads.
Source:[2] Source:[2.3]One of the ways I try to get people to understand just how wrong feeds from places like facebook are is to think about Wikipedia. When you go to a page, you're seeing as other people. So, it's one of the few thing online that we at least hold in common. Now, just imagine
for a second that Wikipedia said "we're gonna give each person a different customized definition, and we're gonna be paid by people for that". So, Wikipedia would be spying on you.
Wikipedia would calculate what's the thing I can do to get this person to change a little bit on behalf of some commercial interest, and then it would change the entry.
Can you imagine that?
Well, you should be able to, 'cause that's exactly what's happening
on Facebook. It's exactly what's happening in your Youtube feed.
– Jaron Lanier
Source:[6]
ML algorithms are able to automatically improve their performance through experience (i.e. through exposure to data). One of the main tools behind the success of machine learning is artifical neural networks (ANN), complex mathematical systems that emulate the behaviour of human decision making process. Thanks to ANNs, ML algorithms can provide answers without being explicity programmed to address a given question, learning to do so autonomously on the basis of the available data. As a result, the computers on which they are implemented learn automatically and improve their learning capacity through ANN training made possible by ever new data. The fields of application are virtually infinite: from automation to artificial vision (with application to autonomous driving), from voice recognition to written text recognition (i.e. automatic translators), from text mining to data analytics.
By resorting algorithms of "hyper-reality", or augmented reality, we are in fact constructing a parallel, virtual reality, no less "real" and effective in its ability to describe various physical process. We are opening the door to many realities, but in essence we are "only" representing reality, with all extraordinary difficulties, accurately and with predictive potential.
Going beyond the present,
simulating the future.
This reveals one of the most threatening aspects of artificial intelligence algorithms: the increasing intrusion into our lives. AI algorithms can reveal our physical and psychological weaknesses, lay bare our desires, our sport or political passion, our preferences as consumers or as readers.
They can orient machine learning algorithms to convey information to us with a STRONG BIAS, casting social or religious movements in a good or bad light, or some of the candidates in the upcoming elections, through blatant violations of fast checking.
When you go to Google and type in ‘Climate change is’ you're going to see different results depending on where you live. In certain cities, you're gonna see it autocomplete with ‘climate change is a hoax’.
In other cases, you're gonna see ‘climate change is causing the destruction of nature’. And that's a function not of what the truth is about climate change, but about where you happen to be googling from and the particular things Google knows about your interests.
– Justin Rosenstein
By the way, Trump tweeted:
The concept of global warming was created by and for the Chinese in order to make US manufacturing non-competitive.
Source:[6]
brainstorming the concept of
Global Warming @StableDiffusion
Machine Learning algorithms may end up knowing us better than we know ourselves. The dreaded risk is that our employers or rulers may demand to know what their employees or citizens really want. If, for example, our governments have access to these algorithms and know our ideas and tastes, they will be able to intervene in our lives in the name of good. Of course, algorithms capable of knowing ourselves to perfection can also come in handy, for example by showing us which choices could make us happier or healthier, which professional opportunities are best suited to us, and which production or marketing strategies can increase our company's profits. The real issue is therefore to prevent these knowledge tools from remaining exclusively in the hands of others (and not ours). The alternative to erecting a shield to protect our privacy is that of ALGORITHMIC TRANSPARENCY, i.e. the claim that the structure, wills, and decisions hidden in each algorithm are clear and explicit. One could demand that those who process our data to create knowledge about ourselves are legally obliged to return that knowledge to us. In this regard, someone applied the slogan nothing about us without us to the age of artificial intelligence. Difficult, however, to imagine that this could be accepted by Big Tech without a decisive transnational political initiative.
Source:[2] Source:[2.3]