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Thread: A.I. is Progressing Faster Than You Think!

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    Default Re: A.I. is Progressing Faster Than You Think!

    (A.I.) Robots (on the verge of) entering society
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    Exclamation Re: A.I. is Progressing Faster Than You Think!

    Quote The effects & difference between A.I. Algorithms versus Human Made Algorithms ("Routines" / Code) is huge! ... Did you know that Elon Musk admitted there is "NO BLACK BOX" (like for airplane accident examinations) in autonomous self driving Tesla cars using A.I. Produced Algorithms ... they (including Elon Musk) do NOT know exactly how it works, but does the job much better statistically than average human driver.

    Most know that A.I. can produce advanced Algorithms (google it).

    Kinect's A.I. breakthrough explained (Algorithms!)

    New Machine Learning Algorithms can lead to significant advances in automatic speech recognition. The biggest single advance occured nearly four decades ago with the introduction of the Expectation-Maximization (EM) algorithm for training Hidden Markov Models (HMMs).

    A beginner’s guide to AI Algorithms

    Fundamental Research in A.I. Algorithms

    Algorithms and AI

    [quote]: "It starts with (A.I.) Algorithm Literacy – this goes beyond basic digital literacy!

    Because algorithms are generally invisible – even often referred to as “black box” constructs, as they are not evident in user interfaces and their code is usually not made public – most people who use them daily are in the dark about how they work and why they can be a threat. Some respondents said the public should be better educated about them". [unquote].

    Source

    So, why does this matter? Conventional Artificial Intelligence (AI) Generates black box Algorithms. If the right to transparency is real, most AI infringes on that right.

    A recent Pew Research study explored "Americans' struggles with truth, accuracy and accountability." Increasingly, we expect that lack of transparency should be viewed with suspicion. Benchmarking suggests this is not paranoia. I have seen early peer-reviewed research that suggests a high correlation between low accountability and poor algorithm performance. The distance between low transparency and poor accountability is short.

    "Black Box Solutions Versus Legal Risks"

    A.I. Predictive Algorithms!

    “Facial Recognition Technology (A.I. Algorithms): Its Impact on our Civil Rights and Liberties

    A.I. in medicine: who watches the (A.I. produced) Algorithms?

    Alexa (= A.I. Algorithms), are you acting in my "best" interest?
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    Default Re: A.I. is Progressing Faster Than You Think!

    https://www.rt.com/news/473282-ai-pr...heart-attacks/

    Quote An artificial neural network has been used to accurately predict a person’s chances of dying within a year by analyzing heart scans, even where doctors can find no issues, leaving scientists equal parts baffled and thrilled.
    Researchers led by Brandon Fornwalt at the Pennsylvania-based Geisinger Health System put their machine learning model to work studying the results of some 1.8 million electrocardiogram (ECG) heart scans, hoping the neural network would derive patterns from the heaps of data.

    Predicting the risk of a heart attack or other heart-related issues, the AI performed better than its human counterparts, consistently scoring above flesh-and-blood doctors. Even for ECG results that cardiologists determined to be normal, the AI was able to pick up on other patterns and accurately predict fatal health risks within a year’s time.

    “That finding suggests that the model is seeing things that humans probably can’t see, or at least that we just ignore and think are normal,” Fornwalt said.
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  6. Link to Post #124
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    Exclamation Re: A.I. is Progressing Faster Than You Think!

    Technologies linking human consciousness to any sort of a cloud computing service could not just open the way for totalitarian mind control, but destroy the very essence of human relations, philosopher Slavoj Zizek says.

    A computer that can read the thoughts of many people at once would make normal human life impossible, the Slovenian cultural philosopher told RT in the wake of the World Artificial Intelligence (AI) Conference in Shanghai, which saw Alibaba’s chairman Jack Ma and Tesla CEO Elon Musk clashing over the future of AI.

    While the two technopreneurs engaged in a heated discussion over the possibility of humans being controlled by machines in the future, the senior researcher at the Institute for Sociology and Philosophy at the University of Ljubljana shared his thoughts on the issue with RT.
    Our brain being connected to a machine is not a utopia

    What I am studying now is the so-called phenomenon of wired brains, a possibility of our brains being connected with strong digital machines. And that is not a utopia. In the media lab at MIT, Massachusetts, they already have simple machines like that. It is like a helmet, nothing intrusive, they put it on your head.

    And then something horrible happens - I saw the video - you think certain thoughts, you do not say anything, and the machine reproduces them either in writing or with artificial voice.

    The primitive level machines can already read your thoughts. It is ok, if it involves your agreement but what if it is done (and it is easy to imagine that) without you knowing it?

    Now, there is a serious option to read our thoughts, not just our emotional attitudes like being angry or sad but even the line of our thoughts in our mind. The next step in this “utopia” will be a computer that can read my thoughts and your thoughts that can connect us so that we can share our thoughts. If you and I are connected through the same computer, I can literally participate in your thinking directly without any external communication like word typing.

    As you probably know, modern technology theorists Ray Kurzweil and Melanie Swan called it a new form of divinity.

    It will no longer be a transcendent God but all of us sharing our thoughts through some AI cloud system. Millions of people will be participating together in a new form of awareness. I find this prospect pretty horrifying.

    We are entering a post-human era if computers can identify & share a person’s thoughts

    We as human beings are precisely what we are, free individuals as far as we can be sure that you do not know what I am thinking. I think what I think, I am free in my mind. What happens if I cannot be sure even of this?

    If I think about something and computer can identify what I am thinking and then share it with others, we are really entering a post-human era. I believe that we should not be just fascinated about what it means technologically.

    Do you not agree that we should worry about who will control these digital machines?
    I do not have fears about the machines controlling us. We are not there yet. However, who will control this, who will use this? What remains of our freedom? Private companies, like Google or Facebook, are already developing similar technologies.
    Thought-to-speech: Scientists say their AI can read out what happens in your brain Thought ‘transparency’ is biggest threat to our freedom today

    I see it as the biggest threat to our freedom. We will literally become transparent. Let’s think about the everyday consequences of this. All flirting will be out. I meet someone and instead of all the lovely games of erotic hints she can read ‘I want to go to bed with you’ and the eroticism will disappear.

    Another simple example is everyday politeness. Let’s say we know each other but we are not mega-close friends. I see you on the street and say the usual polite things like: “hello, how are you? I am glad to meet you.” But if you can read my mind this is nonsense because this is politeness and I do not mean it. Usually, I do not care how you feel.

    This intrigues me very much. What is happening? How will it affect our everyday manners, our old civilization social inventions? All our cultivated interactions are based on this.
    There is another thing.

    What new way of suffering and torture can be developed in this way? Can you even imagine someone controlling your mind? What can they do to you? What horrible thoughts can I implant into your mind? There might be images [of] your nearest terribly tortured, and so on. I know this is not a joke. It is a very serious thing.


    Lying will become more complicated and more privileged

    If we imagine this happening in a society where economic and power relations are structured the way they are now, I think, this will mean that the privileged ones will be those who will be able to conceal their minds, who will exclude themselves from this network.
    Not everybody will be controlled in the same way. That’s the first problem, who will control the game and who will be excommunicated?
    It is always like this. The first thought when a new spying device is developed usually is ‘how can I escape it?’ The privilege is to be outside of it. Lying will become more complicated but it will also become more privileged.


    Computers are smarter on mechanical level, but lack the ability to simplify

    An idea of AI beating humans in the field of intellect in fact depends on how you define being smart. If by smart you simply mean a more complex mathematical or logical operation as well as knowing more data, computers are definitely smarter. Yet, there is still hope for us.
    The greatness of [the] human mind is not in knowing all the details but in picking out from the multitude of data to catch the essence and simplify it.
    A book by a Soviet psychologist Aleksandr Luria titled ‘The Mind of a Mnemonist’ describes a guy with perfect memory. He remembered almost everything that he read and saw. The psychological consequences of this were horrible because he knew so much [that] he could not decide anything, the moment he wanted to take a decision, hundreds of other data popped up in his mind. He lacked the great ability of simplification.

    Computers can be smarter than us on some mechanical level – and even in learning – but I do not think they can deal with the phenomenon of simplification.


    Robots will not take menial jobs, they will be tasked with planning

    Today’s paradox is that we are afraid of robots that could supposedly take our jobs, but those of us who work, work more than ever. Second, we still have the idea that robots will do the primitive work for us and we will just plan what they are doing. In many companies, from McDonald’s to those which do day services, it is robots that do the planning and individuals that execute it.

    Human brains outdated? 'Inventive’ AI making abstract art just filed for two PATENTS

    In McDonald’s everything is programmed by robots and ‘stupid’ people just serve other people. It eventually depends on the social order, if we remain in the same capitalist order, in which we are, it will be even worse than today.

    There is a possibility, a hope, that we will work less. Yet, many new stupid forms of amusement might fill our free time. I still believe in work and creativity. If we do not have enough things to do, even if we will feel happy just sitting, watching films and drinking, it will be a very stupid existence. It will soon get dire.
    It is not technology as such, it is how we will use technology socially.

    Source
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    Default Re: A.I. is Progressing Faster Than You Think!

    New Generation of A.I. can (partially) Write Code (Algorithms!) Themselves!

    There are 1000s of examples already!

    Bayou is an AI application that uses deep learning to generate code by itself. It has been trained on open source Java code from Github. In a similar to Google search way, Bayou uses a few keywords to predict what program a software developer is writing and suggests the specific steps needed to complete it. The project is funded by Google and DARPA.

    DeepCoder is another project in this area. Developed by Microsoft and Cambridge University researches, this AI app searches through a large code database to help programmers solve simple coding problems. For now, though, it can only build programs that consist of a few lines of code.

    Ubisoft, a French game development company has also built a system that simplifies developers’ work. It’s called Commit Assistant and it’s an AI tool that helps programmers prevent mistakes in their code. It uses a huge software library to learn where in code mistakes have historically been made and how they were corrected. And then it detects a potential bug even before a developer makes it. According to Ubisoft, the effort that goes into fixing bugs in the process of game development takes up to 70% of project costs. Commit Assistant can save all that money that goes into fixing software errors.

    Software development is expensive. And if technology can make it cheaper and faster there is no reason why the AI code shouldn’t happen.
    Well, there is one.

    Evans Data Corporation conducted a survey of 550 programmers and found out that being replaced by artificial intelligence is the most worrisome thing in the software programmer’s career.

    • Deep coding: when the machine learns to code by itself
    • Meet the AI That Can Write Its Own Code
    • Google's AI Now Creates Code Better Than its Creators
    • The AI Text Generator That's Too Dangerous to Make Public [Students copied it and did it anyway]
    • An A.I. can now write its own code (Algorithms!).

    • New A.I. application can write its own code (Algorithms!).


    A.I. can now compose (write code) pop music and even symphonies. Here's how composers are joining in.
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    Default Re: A.I. is Progressing Faster Than You Think!

    🔴 Society Already Punishes People Who Speak The Truth, It's Going To Get Worse, Jeffrey Matte
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    Exclamation Re: A.I. is Progressing Faster Than You Think!

    This Robot would kill 5 People | AI on Moral Questions | Sophia answers the Trolley Problem
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    Exclamation Re: A.I. is Progressing Faster Than You Think!

    Will Self-Taught, A.I. Powered Robots Be the End of Us?

    Artificial Intelligence Robots Development Until 2019 - Machine Learning Robot Ep. 06

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    Default Re: A.I. is Progressing Faster Than You Think!

    The Race to Harness Quantum Computing and A.I [The Singularity]
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    Default Re: A.I. is Progressing Faster Than You Think!

    Quantum Computing Explained with a Deck of Cards | Dario Gil, IBM Research:

    I urge all of you to watch this fully!

    cheers,
    John
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    Angry Re: A.I. is Progressing Faster Than You Think!

    Me: "Synthetic Biology = Genetic Engineering on Steroids! ... Mass Invasion of (partial cancerous, toxic, weaponized) GMO's everywhere pollution the Earth much faster with almost NO real oversight!" and they know it. When damage is done there is no way for a "quick fix" afterwards ... which they do NOT care!

    Meet Five Synthetic Biology Companies Using A.I. To Engineer Biology

    How Close Are We to Harnessing Synthetic Life?

    Can Synthetic Biology Inspire The Next Wave Of A.I.?

    A.I. “Potentially Game-Changing” for Synthetic Biology, study finds.

    AI4SynBio.org

    Synthetic biology is a kind of mixture of biology, engineering, computing, and many other fields. There’s a combination of scientists (who tend to look for answers to natural phenomena) and engineers (who tend to build things). There are computer experts and microbe experts. And there are people from universities as well as small and large companies. So the people who call themselves “synthetic biologists” tend to come from many different backgrounds and perspectives. Synthetic biology tends to attract people who are curious about other disciplines and naturally open to collaborating with others.

    Meet Eight Tech Titans Investing In (A.I.) Synthetic Biology

    Microsoft Building a platform for "Programming Biology" (using A.I.)



    Asimov Selected by DARPA to Develop Artificial Intelligence Design Engine for Synthetic Biology

    Synthetic Biology, Artificial Intelligence, and Quantum Computing

    Patenting Considerations for Artificial Intelligence in Biotech and Synthetic Biology

    Intelligent Design of Living Systems

    List of Synthetic Biology Companies

    [quote]: What can synthetic biology offer to artificial intelligence (and vice versa)? .. From self-modifying to self-replicating machines ... The present: emergence, superorganism and the foundations of cognition ... The future: synthesizing superintelligence. [unquote]


    Source

    How Synthetic Biology could Wipe Out Humanity -- and how we can stop it | Rob Reid


    Me: ... In above YouTube video they mentioned Synthetic Biology can help "Terra Forming" Mars ... but to do that they have to "Terror Forming" the Earth first ... Giant experiment (a.o. via chemtrails/geoengineering?) with millions of uninformed guinea pigs & lab rats called "Humans 1.0" making soon room for "Humans 2.0" aka "H+" aka Transhumanists

    To monitor the progress of this giant massive (insane) experiment they may use "5G Surveillance Smart Grid" A.I. controlled (Military Grade) Beamforming Microwaves & mmWaves everywhere 2022+

    --o-O-o--

    Would it be "far fetched" to say that Synthetic Lifeforms using Quantum A.I. are (partially) "The Greys" of The Future? ... Who are NOT "Alien" but perceived as such now! ... It would also explain the "lack of emotions".

    I have had multiple UFO encounters and to me SOME of those experiences seems like they are us, from the future but not 100% as they are genetic enhanced.

    Maybe our current unaltered "healthy" genetics is partially their origins seeking answers they can not solve ... like what is it, what we have they don't ... Soul?

    cheers,
    John Kuhles 23-11-2019
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    Exclamation Re: A.I. is Progressing Faster Than You Think!

    Sam Harris on AI (from Joe Rogan Experience #804)

    Joe Rogan Talks Artificial Intelligence with a Yale Professor
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    Default Re: A.I. is Progressing Faster Than You Think!

    Never gets old:
    Amazon’s A.I. Alexa is a CRAZY SJW LIBERAL! | Louder With Crowder
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    Default Re: A.I. is Progressing Faster Than You Think!

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    Default Re: A.I. is Progressing Faster Than You Think!

    Posted by Hervé (here)
    1.2 BILLION people’s data – including social media profiles and contact info – found on unsecured Google Cloud server

    RT
    22 Nov, 2019 22:23
    Updated 13 hours ago
    Get short URL

    If (Quantum) A.I. has, lets say, 1 hour access to all that personal (Meta) Data would be equivalent to how many humans do the reading physically in what amount of time?

    Most do not really comprehend WHAT exactly you can do (what kind of tasks) using (Quantum) A.I. accessing all of that in short period of time that normally would be impossible for humans to do in their own life-time.

    For one you can search for anomalies in (super?) human behaviors. Tracking certain individuals with "suspicious" behavior. That if "flagged" by the (Quantum) A.I. system exposing 1000s of extraordinary gifted people in almost all countries who MAY serve & help to unslave humanity but are seen as a (possible) "threat" to the Deep State.


    Cheers,
    John Kuhles 24-11-2019
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    Default Re: A.I. is Progressing Faster Than You Think!

    NEW Thought Police Tool - A.I. Mind Reading PRODUCES VIDEO of Human Thoughts in REAL TIME!

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    Default Re: A.I. is Progressing Faster Than You Think!

    I want to add a bit to this one post

    There is a difference between machine learning and A.I. Even though machine learning is currently part of A.I. it doesn't mean that by itself it can achieve the same results

    On this video, it is made as if machine learning equals A.I. which is incorrect

    Machine learning takes an input and produces an output. An statistics based output, so you can interpret those results on the human end, it's all math in the end

    True A.I. in the receiving end would not require the human part to interpret those results

    So, interpreting patterns created by a human brain, that then go through a software engine that process them and outputs statistics that help locate patters in a population group, that then are interpreted by another human? That's not A.I. That's the use of machine learning to help normal humans identify patterns in the population. Even if they use it for questionable purposes

    It's a simple pattern, gather this electrical info from brains, process it through the software engine, produce averages, see the red spots and those people are our marks. It requires manual human input/output/Interpretation. So it is not truly A.I.

    I have seen this before and there is a considerable effort to make anything related to machine learning become "A.I" in the eyes of people, but it's not the actual truth, A.I is way above running a simple algorithm that calculates electrical averages based on patterns produced by a brain, for example
    Last edited by Mashika; 29th November 2019 at 05:07.
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    Exclamation Re: A.I. is Progressing Faster Than You Think!

    These Google AI experiments are crazy! This is the future.
    Last edited by ExomatrixTV; 2nd December 2019 at 18:16.
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    Default Re: A.I. is Progressing Faster Than You Think!

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    Exclamation Re: A.I. is Progressing Faster Than You Think!

    In the Age of AI (full film) | FRONTLINE


    Artificial intelligence has already started to shape our lives in ubiquitous and occasionally invisible ways. In its new documentary, In The Age of AI, FRONTLINE examines the promise and peril this technology. AI systems are being deployed by hiring managers, courts, law enforcement, and hospitals — sometimes without the knowledge of the people being screened. And while these systems were initially lauded for being more objective than humans, it’s fast becoming clear that the algorithms harbor bias, too.

    It’s an issue Joy Buolamwini, a graduate researcher at the Massachusetts Institute of Technology, knows about firsthand. She founded the Algorithmic Justice League to draw attention to the issue, and earlier this year she testified at a congressional hearing on the impact of facial recognition technology on civil rights.

    “One of the major issues with algorithmic bias is you may not know it’s happening,” Buolamwini told FRONTLINE. We spoke to her about how she encountered algorithmic bias, about her research, and what she thinks the public needs to know.

    This interview has been edited for length and clarity.

    On her first encounter with algorithmic bias.

    The first time I had issues with facial detection technology was actually when I was an undergraduate at Georgia Tech, and I was working on a robot. The idea with this robot was to see if I could get it to play peek-a-boo with me. And peek-a-boo doesn’t really work if your robot can’t see you, and my robot couldn’t see me. To get my project done, I borrowed my roommate’s face. She was lighter skinned than I was. …That was my first time really using facial analysis technology and seeing that it didn’t work for me the same way it worked for other people. …

    I went on to do many things and became a graduate student at MIT and I started working on projects that used facial analysis technology, face detection. So one project I did was something called the Aspire Mirror. You look into a mirror, a camera detects your face and then a lion can appear on you, or you can be somebody you’re inspired by [It wasn’t detecting my face consistently, so I got frustrated. So what do you do when you get frustrated with your program? You debug. I started trying to figure out ways to make it work. I actually drew a face on my hand, and the system detected the face on my palm. And I was like, “Wait, wait, wait, if it’s detecting the face I just drew on my palm, then anything’s a possibility now.” So I looked around my office and the white mask was there. So I was like, “There’s no way! But why not?”

    I pick up the white mask, and I put it on and it’s instantaneous when I put on that white mask, and I mean just — the symbolism of it was not lost to me. This is ridiculous that the system can detect this white mask that is not a real person, but cannot necessarily detect my face. So this is really when I started thinking, “Okay, let’s a dig a bit deeper with what’s going on with these systems.” …

    On digging a bit deeper into facial analysis technology.

    Here was a question: Do these systems perform differently on various faces? There was already a 2012 report that actually came out from an FBI facial analysis expert showing that facial recognition systems in particular worked better on white faces than black faces. They didn’t work as well on youthful faces. And they didn’t work as well on women as compared to men. This was 2012, and why I keep bringing that up is this was before the deep learning revolution…

    Now we had a different approach that was supposed to be working much better. My question was, given these new approaches to facial analysis and facial recognition, are there still biases? Because what I’m experiencing, what my friends are experiencing — and what I’m reading about with reports that say, “Oh, we’ve solved face recognition,” or “We’re 97% accurate from benchmarks” — those reports were not lining up to my reality. …

    What I focused on specifically was gender classification. …I wanted to choose something that I thought would be straightforward to explain, not that gender is straightforward — it’s highly complex. But insomuch as we were seeing binary gender classification, I thought that would be a place to start. By this time my weekend hobby was literally running my face through facial analysis and seeing what would happen. So some wouldn’t detect my face and others would label me male. And I do not identify as male. This is what led down that corridor.

    On finding the “gold standard benchmarks” were not representative.

    When I ran this test, the first issue that I ran into which gave me some more insight with the issue we’re talking about — algorithmic bias — was that our measures for how well these systems perform were not representative of the world. …We’ve supposedly done well on gold standard benchmarks. So I started looking at the benchmarks. These are essentially the data sets we use to analyze how well we’re doing as a research community or as an industry on specific AI tasks. So facial recognition is one of these tasks that people are benchmarked on all the time.
    “What I started to see was something I call ‘power shadows’ — when either the inequalities or imbalances that we have in the world become embedded in our data.”

    The thing is, we often times don’t question the status quo or the benchmark. This is the benchmark, why would I question it? But sometimes the gold standard turns out to be pyrite. And that is what was happening in this case. When I went to look at the research on the breakdown of various facial analysis systems, what I found was one of the leading gold standards, labeled “Faces in the Wild,” was over 70% male and 80% white. This is when I started looking into more and more data sets and seeing that you had massive skews. Sometimes you had massive skews because you were using celebrities. I mean, celebrities don’t necessarily look like the rest of the world. What I started to see was something I call “power shadows” — when either the inequalities or imbalances that we have in the world become embedded in our data. …

    All this to say, the measures that we had for determining progress with facial analysis technology were misleading because they weren’t representative of people — at least the U.S. in that case. …We didn’t have data sets that were actually reflective of the world, so for my thesis at MIT, I created what I call the Pilot Parliaments Benchmark. I went to UN women’s websites, I got a list of the top 10 nations in the world by their representation of women in parliament. … So I chose European countries and African nations to try to get a spread on opposite ends of skin types, lighter skin and darker skin. After I ran into this issue that the benchmarks were misleading, I needed to make the benchmark.

    On what her research found.

    Then finally, I could get to the research question. …So I wanted to know how accurate are they at this reduced task of binary gender classification — which is not at all inclusive — when it comes to guessing the gender of the face? And it turned out that there were major gaps. This was surprising because these were commercially sold products. … You know how the story goes. It turns out, the systems work better on male-labeled faces than female-labeled faces, they work better on lighter faces than darker-skinned faces.

    But one thing we did for this study, which I would stress for anybody who’s thinking about doing research in algorithmic bias or concerned with algorithmic bias and AI harms, is we did an intersectional analysis. We didn’t just look at skin type. We didn’t just look at gender. We looked at the intersection. And the inspiration for this was from Kimberlé Crenshaw, a legal scholar who in 1989 introduced the term of intersectionality. …What would happen with the analysis is if you did it in aggregate just based on race, or if you did it in aggregate based on just gender, you might find based on those axes that there isn’t substantial evidence of discrimination. But if you did it at the intersection, you would find there was a difference. And so I started looking at the research studies around facial analysis technologies and facial recognition technologies and I saw that usually we just have aggregate results — just one number for accuracy. People are just optimizing for that overall accuracy, which means we don’t get a sense of the various ways in which the system performs for different types of people. It’s the differences in the performance, the accuracy disparities that I was fascinated by, but not just on a single axis but also on the intersection. So when we did the intersectional breakdown — oooh, it was crazy. …

    We weren’t doing anything to try to trick the system. It was an optimistic test. This is why I was very surprised, because even with this optimistic test, in the worst-case scenario for the darkest-skinned women, you actually had error rates as high as 47% on a binary classification task. …

    I shared the results with the companies and I got a variety of responses. But I think the overall response, at least with the first study, was there was an acknowledgement of an issue with algorithmic bias.
    On how AI is already affecting people’s lives.

    There’s a paper that just came out from Science which is devastating, showing risk assessment algorithms used in health care… actually have racial bias against black patients. We’re talking about health care where the whole point is to try to optimize the benefit and what they were seeing was because they used how much money is spent on an individual as a proxy for how sick they were, it turned out it was not a good proxy because black patients who were actually sick were being said to be not as sick as they were. …

    “When these systems fail, they fail most the people who are already marginalized, the people who are already vulnerable.”

    You also have AIs that are determining the kind of ads people see. And so there have been studies that show you can have discriminatory ad targeting. Or you can have a situation where you have an ad for CEO and the system over time learns to present that CEO ad to mainly men. You were saying, how do you know if you’ve encountered bias — the thing is you might never know if you’ve encountered the bias. …Something that might happen to other people — you see phenotypic fails with passport renewals. So you have a report from a New Zealand man of Asian descent being told that his eyes are closed and he needs to upload another photo. Meanwhile, his eyes are not closed. You have, in the UK, a black man being told his mouth is open. His mouth was not open. You have these systems that are seeping into every day.

    You have AI systems that are meant to verify if you’re who you say you are. And so one way that can happen is with ride share apps. Uber, for example, will ping drivers to have them verify their ID. There’s actually a report from trans drivers who were saying that they were being repeatedly [asked] to submit their IDs because they were not matching. They were being either kicked out of the system or having to stop the car, test it again, which means you’re not getting the same level of economic opportunity. …

    When these systems fail, they fail most the people who are already marginalized, the people who are already vulnerable. And so when we think about algorithmic bias, we really have to be thinking about algorithmic harm. That’s not to say we don’t also have the risk of mass surveillance, which impacts everybody. We also have to think about who’s going to be encountering the criminal justice system more often because of racial policing practices and injustices.

    On what the public needs to know about algorithmic bias.

    There’s no requirement for meaningful transparency, so these systems can easily be rolled out without our ever knowing. So one thing I wish people would do more of and something that companies also ought to do more of is having transparency so that you even know that an AI system was used in the first place. You just might never get the callback. You just might pay the higher price. You would never actually know. What I want the public to know is AI systems are being used in hidden ways that we should demand are made public.

    The other thing I want the public to have is actually choice — affirmative consent. Not only should I know if an AI system is being used, but let’s say it makes the wrong decision or something that I contest. There’s no path to due process that’s mandated right now. So if something goes wrong, what do you do?

    Sometimes I’ll hear, at least in the research community, efforts to “de-bias” AI or eradicate algorithmic bias. And it’s a tempting notion, let’s just get rid of the bias and make the systems more fair, more inclusive, some ideal. And I always ask, but have we gotten rid of the humans? Because even if you create some system you believe is somehow more objective, it’s being used by humans at the end of the day. …I don’t think we can ever reach a true state of something being unbiased, because there are always priorities. This is something I call the “coded gaze.” The “coded gaze” is a reflection of the priorities, the preferences and also the prejudices of those who are shaping technology. This is not to say we can’t do our best to try to create systems that don’t produce harmful outcomes. I’m not saying that at all. What I am saying is we also have to accept the fact that being human we’re going to miss something. We’re not going to get it all right. …
    “What I want the public to know is AI systems are being used in hidden ways that we should demand are made public.”

    Instead of thinking about “Oh, we’re going to get rid of bias,” what we can think about is bias mitigation — knowing that we have flaws, knowing that our data has flaws, understanding that even systems we try to perfect to the best of our abilities are going to be used in the real world with all of its problems. …

    Before we get to the point where it’s having major harms with real world consequences, there need to be processes in place to check through different types of bias that could happen. So, for example, AI [systems] now have algorithmic risk assessments that they have as a process of really thinking through what the societal impact of the system are in its design and development stages before you get to the deployment. Those kinds of approaches, I believe, are extremely helpful, because then we can be proactive instead of reacting to the latest headline and playing bias whack-a-mole. …

    On proposals for oversight and regulation.

    You have a proposal for an Algorithmic Accountability Act, this is a nationwide push that would actually require assessing systems for their social impact. And I think that’s really important. We have something with the Algorithmic Justice League that’s called the Safe Face Pledge, which outlines actionable steps companies can take to mitigate harms of AI systems. …

    I absolutely think regulation needs to be the first and foremost tool, but alongside regulation providing not just the critique of what’s wrong with the system, but also steps that people can take to do better. Sometimes the step to take to do better is to commit to not developing a particular kind of technology or particular use case for technology. So with facial analysis systems, one of our banned uses is any situation where lethal force can be used. So it would mean we’re not supporting facial recognition on police body cameras. Or facial recognition on lethal autonomous weapons. …


    And I think the most important thing about the Safe Face Pledge that I’ve seen is one, the conversations that I’ve had with different vendors, where whether or not they adopt it actually going through those steps and thinking about their process and changes they can make in the process I believe has made internal shifts that likely would not hit the headlines. Because people would rather quietly make certain kinds of changes. The other thing is making it where the commitments have to be part of your business processes. Not a scouts’ honor pledge, just trust us. If you are committed to actually making this agreement, it means you have to change your terms of service and your business contracts to reflect what these commitments are. …

    On what should be done to fix the problem.

    One, I think, demand transparency and ask questions. Ask questions if you’re using a platform, if you’re going to a job interview. Is AI being used? The other thing I do think is supporting legislative moves. …
    When I started talking about this, I think in 2016, it was such a foreign concept in the conversations that I would have. And now, today, I can’t go online without seeing some kind of news article or story about a biased AI system of some shape or form. I absolutely think there has been an increase in public awareness, whether through books like Cathy O’Neil’s Weapons of Math Destruction. There’s a great new book out by Dr. Ruha Benjamin — Race After Technology.

    People know it’s an issue and so I’m excited about that. Has there been enough done? Absolutely not. Because people are just now waking up to the fact that there’s a problem. Awareness is good, and then that awareness needs to lead to action. That is the phase we’re in. Companies have a role to play, governments have a role to play and individuals have a role to play.

    When you see the bans in San Francisco [of facial recognition technology by the city’s agencies]… what you saw was a very powerful counter-narrative. What we were hearing was that this technology is inevitable, there’s nothing you can do. …When you hear there’s nothing you can do, you stop trying. But what was extremely encouraging to me with the San Francisco ban — and then you have Somerville that came from the folks who are in Boston — people have a voice and people have a choice. This technology is not inherently inevitable. We have to look at it and say: What are the benefits and what are the harms? If the harms are too great, we can put restrictions and we can put limitations. And this is necessary. I do look to those examples and they give me hope.

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    Last edited by ExomatrixTV; 5th December 2019 at 18:30.
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