ACERCA DE ESTE EPISODIO
Explore the thought-provoking podcast episode with Yonah Welker as he delves into the significance of embracing algorithmic diversity, AI, and ethics. Gain insights into the evolving landscape of artificial intelligence and its ethical implications.
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EN ESTE EPISODIO
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Welcome to the Technologies Impacting Society podcast, where we explore the profound ways that technology is shaping our world and transforming our lives today.
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I'm your host, Ina O' Murchu and in this episode, I got to explore algorithmic diversity with Yonah Welker on the topic of social technologies, AI and robotics addressing cognitive, sensory, physical and non physical differences, accessible to cities and ecosystems.
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Yonah's mission is to shape the future of algorithmic diversity, algorithms and policy addressing human capacity, ability, neuro diversity, well being such as social, emotional and human centered AI, emerging learning, work and city solutions. He has contributed to policy and ethics frameworks and curated exponential projects. Yonah has also spent over 60 World appearances to bring awareness to the neuro exclusion crisis that we have today. If you want to go ahead and introduce yourself, you and other listeners, and let us know a bit about your background.
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My name is Yonah Welker. And I'm focused on three levels of technology policy and creative projects addressing areas of a cognitive health neurodiversity human capacity.
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It includes technologies such as AI for dyslexia, autism, mental health disorders, such as depression, anxiety, but also other areas such as educational tools, platforms, social robotics, bionic technology, and all of this universe.
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We've seen the whole area of cognitive diversity and the disability, what are some of the key ways in which cognitive diversity can help to close? You know, the disability inclusion gap in the development and the implementation of AI technologies? In your opinion?
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Yes, it's a very good question. So first of all, in our community, people prefer to use the term neurodiversity.
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My work is a bit broader, and I call it cognitive diversity. So what's the difference? So neuro diversity is typically some kind of a natural differences we all have. Or sometimes it's more kind of a deeper spectrum, it incap encompass Attention Deficit Disorder, dyslexia, autism syndrome. But for me, of the work is a broader, I'm trying to address not only things were born with, but also disorders as well, because your attention can be affected by mental health by autoimmune disorders by tiredness, and it affects how you use particular applications. So for instance, you can look tired due to this fact facial recognition system or computer vision can identify you as a drunk, or it identify your net correctly. Or you can have some neurological disorders that affect your facial expressions. So my work encompass both neurodiversity and some kind of a differences driven by mental or other type of disorders. And it was why we need some kind of accommodation and consideration in terms of how we build algorithms, how we research people. When we build products and solutions. It includes sensory diversity, cognitive diversity, tactile experiences, how people interact with the devices, platforms, it helps us to have a better understanding how we build smart cities. And now there's a broader concept called cognitive cities is a further development of smart cities with the more use of AI, personalization, some kind of a interaction, and connection of people together.
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And finally, why it's so important. My goal is to bring awareness to how we see the universal design, because I believe not only design should be universal, but algorithms should be very flexible, in order to understand all of the layers, all the elements of a spectrum of your health of your diversity, have some kind of impairments you potentially have, in order to not exclude you to not bring some kind of harm and also actually bring positive impact specifically then we build AI for work technology or tailoring screening schools, medical applications, nursing, and even more special areas such as the police or military systems.
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So how can AI then be designed to better accommodate, the diverse needs and abilities of individuals, these individuals with disabilities, and what role can cognitive diversity play in this process?
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Yes, so there are a few elements. So first of all, should understand that area of a cognitive diversity is a very broad variety. Over billions of people who have some kind of affection driven by neurological disorder, mental health disorders is about many people, not some kind of a minority group. So it's at least like one in seven people in the world.
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Second, is about a research criteria. When we build algorithm when we design a system, when we provide audit of this system, we create the framework addressing on one hand, all of this research layers of a sensory cognitive criteria, but also other things such as which stakeholders involved in how exactly they use this application. So for instance, when we design platform for autistic children, we take into account not only the child but also the caregiver or parent. And we have a two interfaces or two versions of apps or two screens, which helped us to take different type of data input from both of them.
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Also, we take into account that such people use not only one app, but sometimes few platforms. So this technology become modular. For instance, you have a sum app, which helps you to keep your four cues or build daily tasks, but also you have some kind of a Fitbit or wellbeing tracking device. And all of these devices, accumulate some data and can be used in interconnection. So this ecosystem become connected. And finally, is awareness about AI so and how it can benefit such groups. So for instance, just last year, we created the summit where we had, the Museum of AI driven by the story of a neuro divergent individual is a life cycle from birth to adulthood, to charity, and so on. So and we show in how the virgin individuals can interact with the technologies is a child is an adult, how it was how is different and in this way, we are able to explain to researchers to UX and UI designers, how we can create this curriculum of a research and development, how we can make it in proper way. Another example, one of the companies, our circle is called a rumba kind. And in our example, examples from Europe like Lux AI, they focus on social robotics for autism. And both mentioned that for them. Not only developing this robot is critical, but building the curriculum of how to adopt it properly, how to create the connection between the parent educator classroom, that type of a spectrum. So this company is literally become kind of our learning in education companies and their work. It's not only about technology anymore, is about social studies is about identification of professional and competence framework it's about curriculums is about really deep intersectional research approach. And also let's do not forget about such aspect as a comorbidity, comorbidity is underlying conditions. So for instance, from 25% to 40% of people with learning disabilities, also experienced mental health problems, also such individuals are more likely to have allergies or other conditions also we should take into account the aspect of gender. So for instance, girls are they are very frequently misdiagnosed in typically is not about AI, but generally in medical field. And this bias, this wrong approach has become a part of a data set and AI system. So as AI developers, you should take into account all of this existing biases in order to eliminate them.
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There are obviously ways that AI will have to be better designed to cater for the diverse range of humanity that we have, what can we do about the bias in the dataset if the data isn't clean this is neutral you know, it's starting off of
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I'm very interested in thing because we're having such a lot of discussion about open AI today.
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And similar companies like deep mind who create a massive projects related regenerative AI, and we had a lot of discussion about the main problem is not about technology anymore. The main problem we have is about society. I mean, for instance, 85% of people with autism are not employed, not due to technology, but due to the social bias due to the existing stance against these people, due to how we see, let's say, people talent, because typically we identify identify extrovert people is like, smarter, more positive. And it's our perception, being active, being very high, have very high ability. And communication is like our sign and symbol of intelligence of a productivity of a passion. And we put this bias into the system. And incident like a hypothesis. Just recently, I uploaded my my content, by the way, related to AI bias to one of educational platforms, and platforms, identified that I'm not alive.
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Because this particular vision, how your face typically moving or shifting, then you're talking about something. But just imagine if you have a neurological disorder, which affect your face, your mimic posture, your gesture, how you move your hands. So the system are based on our vision of people of variability of their intelligence of their talent, all of the social issues are just put into the systems and it relates to our ability, racism, many other problems we have in society.
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There are other challenges in this area for sure. What are some of the challenges and the limitations Yonah associated with incorporating cognitive diversity into the AI development? And how can these be overcome to promote greater inclusion and accessibility?
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Yes, it's actually very complex task. And I believe the first thing to start is that representation. In disability community, we have a motto nothing is about about us without us. And it means that if you would love to eliminate bias towards particular group, you should immediately integrate this group into your company into your project into your research group. And these people should be involved in development of this technology.
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And for instance, very popular, our platform for hiring artistic people is called all turnouts.
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And 75% of their employees are also on the spectrum. So it's created by this people for these people. There's what representation is the first thing, second thing is accessible vocabulary. We have a lot of discussion, for instance, what is the correct word like neurodiverse, or neuro divergent? Now we know if we're talking about individual, we use the term neurodivergent. But also there is a Walding framework related to human rights protection, children rights, bioethics, medical terminology, which immediately become a part of the teams and kind of a common language, we're all used to better understand the problem of bias and more importantly, this historical exclusion, why is happening. So, we bring this social studies to the board to be a part of education and a part of a discussion across all of the stakeholders. One year ago I work on AI justice framework and how we could bring this discussion to different levels of stakeholders including executive level our board members and this is a very complex task to create this vocabulary which clear for everyone and another thing is a following existing frameworks because actually very a lot of things which are done on the higher level to find it there is a UNESCO AI ethics recommendation is open source document with provide us with the guidance. There is a UNICEF AI for children. And also UNICEF AI for girls with disabilities is a special document which was released just two few months ago, there is a AI recommendation visit World Health Organization recommendation there is a World Economic Forum recommendation.
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There are frameworks driven by particular companies like an AI ethics framework by Deloitte. So There are open documents which you can use to learn how to work towards your framework and my team work on on search framework. It was driven by European Commission it was released two years ago. And it's also accessible in open source.
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And after that, we can talk about audit. So when we build the representation, a cap vocabulary framework, we can use the audit, checking the data sets, systems, algorithms, at all of the levels of development, taking it into account that aspect of fairness, accountability, transparency, and it worked in silos, it worked in black box, then we are not able to explain what's happening behind the model, how we ensure that all of the data input in different stakeholders are taken into account how the whole ecosystem of devices or tools or platforms are connected, and so on,
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Then how can artificial intelligence be used to promote the human rights of neurodivergent individuals, no, such by providing greater access to education, employment and other opportunities? And, again, obviously, there are some challenges that are going to go into need to be overcome. But what will be the main challenges that companies like you mentioned Deloitte there? How can they implement it and make it easy for these individuals?
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It's a very tough question. So first of all, it's a social problem, and is a problem of a diversity itself.
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Not only cognitive diversity, but any kind of a diversity. I believe there are some steps made on the corporate level to bring diversity. But I believe a lot of people are not really sincere about this, I think they try to do something because they would love to be to look better as a brand, or as a company or a public perception. But they're not truly passionate about diversity themselves, specifically, then they're just pushed by society to make this change. And I believe these groups of people are companies are very challenging to actually bring these changes. One of my peers, she's working on the platform called Tobias. And this platform is focused on racial justice, and she tried to incorporate across different organization in some times, it's very challenging, even due to some political reasons she can share even more, she's still developing this project and share some updates from time to time and from neurodiversity for disability is the same. So 85% of people with autism were not employed. People with a mental severe mental health disorders such as schizophrenia, or bipolar disorder, their unemployment is up to 70% for Down syndrome is up to 45%.
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Until today, the public spaces were not optimized for people with a different type of disabilities. And some people just believe that when we try to incorporate and include these people, they make a favor, but they do not understand that.
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Actually, if you provide them with a very small accommodations, such as remote learning, remote working, or remote learning, or just let's say, less communication type of the workplace focus on more solitary activity, these people actually can be extremely smart out competing majority of our people, specifically in particular areas of their interest. So they're extremely competitive. And based on the statistics, inclusion actually can bring up to 30% more revenue to companies. So even if you're not good person in you're not caring about people, it least inclusion actually bring a lot of change to the economy.
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Because these people are smart, they are talented. They just need this small accommodation, or optimization, or personalized experience or maybe remote learning or working. And if we're bringing this change, we actually can do a lot. So the main challenge here is a social perception is existing bias is a high level stakeholders, board members and executives sounds very big, specifically conservative thinking companies or governments, we need to change their thinking about the problem. We do not make their favor. We must do it because neurodiversity and cognitive disability inclusion, benefit the whole society. It will improve in change with technology for everyone because adaptive and personalized technology can innovate work in platforms and you occasional platforms, smart cities for everyone, it will improve innovate companies in ecosystems where themselves so we should work on it further.
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How is the artificial intelligence being used to help diagnose? And I suppose this might have been a question I might I could have asked you earlier, there must be a way that artificial intelligence can be used to help diagnose what what are the potential benefits and risks associated with these AI applications?
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Yeah, so first of all, I would love to mention some of my favorite technologies, because when we say an AI is a quite broad definition. So I would love to mention some technologies, which I like which help to accommodate and assist people on the spectrum. For instance, one example is the social robotics.
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I've mentioned, some companies like RoboKind or Lux AI, which helped to create adaptive learning for kids so we can incorporate it in classrooms.
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But a search social companions also can be used at workplaces, for mental health survey purposes or for anxiety control for more balanced workplaces. So this field of social companions is a very big, or another example is a company brainpower and they work on smart glasses with augmented reality, they collaborate with the Google Glass, and it allows to identify emotions. So people with particular impairments such as autism, they have issues identifying human emotions. So for instance, child is not able to recognize mother emotion, and technology help to use in facial recognition to provide the description for the child, and better understand it. Once again, a similar technologies can be used across workplaces for different type of imperfect training, there are projects made through virtual reality or augmented reality. Try yourself and someone shoes some time ago, it was a project which helps people to understand what it's like to be in jail, what is like to be marginalized, what is like to be wrongly attacked by police, to be in someone's shoes to experience in train this empathy skills. Or another example is a company called be me.ai. And it's focused on the whole autism spectrum and wellbeing tracking. So, it can collect all of information about your performance, about your nutrition related to autism spectrum, and such type of platforms can be used for different conditions as a mental health. So, in terms of a company's in terms of ecosystems, such technologies can be easily integrated, but at the same time, there are a few other elements which should be taken into account. First is the digital competence framework. So if you integrate such technologies, you should bring trainings and upskilling related to how to use this technology properly. We should provide the training for instance, we cooperate with some agencies who train organization how to use this technology, how to make disability inclusive workplaces, both from a social perspective, but also using particular tools.
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So it's about this training. And third, as I mentioned, is it bringing this accessible vocabulary. So everyone across workplaces are able to discuss existing problems, problems of exclusion problems of harassment because individuals on the spectrum are frequently is an object of attack of harassment or abuse. So we should be open to discuss it use appropriate language. So trainings addressing vocabulary, particular themes, topics, at all of the levels of organizations, including developers, researchers, executives is very important as
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00:24:32.480 --> 00:25:16.789
You see them completely as tools and not replacement, obviously for individuals because you still need you can't totally automate the area of medicine there's absolutely it's impossible. Even though these tools you said to be able to scan the eye and be able to detect certain diseases really incredible. It's what they can do. Now, I suppose this leads into the next question then what also there are obviously risks with using those technologies. And like as you said, Being neurodivergent associate just on the spectrum, but there's obviously potential risks and benefits of using AI to monitor and track neurodivergent behavior. But how can we ensure these technologies are used in ways that are consistent with our human rights and dignity?
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Yeah, so if we talk about neurodiversity, we can treat it because it's not disorder. But natural spectrum if we're more talking about mental health, because it's important to mention that people with autism frequently have some mental health disorders, and some comorbid conditions, sometimes autoimmune disorders.
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So how AI can help. So first of all, the first thing is not AI, but for sure is the ecosystem in medicine. So people diagnosed done by AI, they diagnosed by doctors, it's not completely my area of expertise because I'm not a doctor. In terms of AI, there are a few elements, First of all, AI is actively used for MRI scans, and helps to analyze your brain. Also, there is a use AI for a condition like a multiple sclerosis. So it can analyze your eyeball and neuro system and predict particular disorders such as a multiple sclerosis are simple similar conditions. Also, we can use facial recognition or computer vision in order to identify a particular pattern in your facial expressions, also analyzing your behavior. Another thing is adaptive learning in training platforms. So for instance, used in workplaces or training, then patient or student, provide data input participate in particular testing. And using adaptive mechanism, we can identify a learning spectrum that similar technologies are used for dyslexia. So using some we work on something similar in Denmark.
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And this project was supported by European Commission, the technology used eyeball tracking in order to to identify your reading pattern, how your eyes are shifting from one word to another, what the speed, what's the pace, what the pattern, and it helps you to identify existing level of disorder if presented, and also help to provide you with the support and recommendation. And there are similar technologies and apps related to attention deficit. So they help to train your identify your focus your spectrum, how efficiently you use your time, or how efficiently you're involved, in particular learning experience, enough to provide commendation. So I would say AI slightly more important for supporting doctors, it doesn't replace them. And it's specifically beneficial for a more personalized type of type of accommodation. But I always repeat that AI is a tool. It's not a replacement for nurses.
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It's not replace for replacement for MRI scans, experts who will check and actually identify the disorder or type of a damage. AI just helped to get access to more datasets to more a collection of a similar results to provide the doctor or educator with a some kind of advice about particular condition, or element of a spectrum. And the same was a training and personalization applications,
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What legal frameworks and policies, you seem to be involved in a number of frameworks, what do we need to have put in place to govern the use of artificial intelligence in the context of neurodiversity and human rights?
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And how can we ensure that these frameworks that have been worked on are responsive to the evolving needs and concerns of the neurodivergent community?
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It's a very interesting question, because I'm both technologists and I'm also work on ethics. And basically, I limit myself you know, typically people take one of the camps by a driver entrepreneurs and they try to build big companies, or they're more like lawyers and human rights advocates. Because it's easier to criticize someone than then you do not build these technologies. At the same time, then you build something you do not criticize yourself because you create the framework which will create more limitation considerations and ethical boundaries for your work. But I believe we have no choice specifically when we address children then we address people with disabilities when they address someone who are a part of a assistive support a fixed system and they can leave without caregivers. So few levels of a potential are risks in by the way they are addressed by European Commission EU AI act, AI act address four levels of risks including unacceptable risk, high risk, medium and low or no risk. And similar in our era AI for mental health, or cognitive impairments, on one hand, is a silos when we try to replace, it's not acceptable when we try to replace actual educational process. With the AI with personalized platforms, we put people into some kind of a digital jail. And it's unacceptable, because it become informational echo chamber, they are not able to interact with the other people. That's why they would typically call it filter bubble is the same keywords should know how semantic analysis work. So if you put the same keywords, the same interest, and you're part of this bubble, it become the same, it means you basically become emotionally intellectually isolated. And this silos is a very dangerous.
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Second thing is a harassment. As I've mentioned, there is a lot of bias related to disabilities and part of this bias and social network algorithm as well. So as I mentioned, when I uploaded, my content on site was identified is not alive due to the specifics of my face expressions, and people with a disability is can be attacked, it can be abused by both algorithms, or people who are not able to correctly identify them due to the specifics of their behavior. And also take into into account point one and point two is a manipulation and disinformation. If we put these people into the silos, if we create the situation, then we're not able to protect them from harassment from abuse, they can be manipulated, they can be an object of disinformation, because when we completely replace someone is a teacher is a family or is a society with AI, we create some kind of universal, a model of a truth.
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But this way, we're not able to bring critical thinking. And critical thinking or come in from a different experiences, sometimes experiences some mistakes or errors in actual relationships. And in this way, is a point number four is a safety and privacy. People who are not experienced in critical thinking, they are not able to identify the boundaries of their personality in their behavior at social networks, they are very fragile, they are very easy attacked by someone. And what's why two years ago, I came up with a quite big a framework.
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Identifying all of this aspects, it includes actually even more, it includes aspects of accountability, transparency, fairness, human involvement. So for instance, if we use social robotics with a child, how educator or how medical professional is involved, it's also a question of ownership. So for instance, if I create something with an AI, how I keep my ownership as a creator in terms of IP, if I'm only exist in this silo, also the problem of a caregiver ecosystem. So how we identify the rights and the legal status of every user who's involved, and this ecosystem, because sometimes I have an app, I use this app. And there's my mother, there's educator. And finally, the problem of a technical fixes, technical fixes is an aspect of frequently used by social networks and companies like meta. So what does it mean it means that if you have a bug or error in your system, you use another autonomous system, in order to identify this problem and fix it, it means there is no human involvement. So the error made by system also fixed by the system. But you can imagine if the system was biased, it will perpetuate the same type of a logic it and it will not fix the problem. It just creates some kind of temporary replacement.
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And if we create this vicious cycle, we're not able to actually protect our community.
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And there are many, many other aspects and as a constant work and as I mentioned, UNICEF came up with a framework AI for girls with disabilities. Why because disability for girls is different, specifically in marginalized communities. And we should take into account aspects of intersectionality, of a gender of marginalized communities. Because these people experience this biases even differently in even more, and technology can harm them even more. So all this aspect is a universal of how we can fight and protect the human rights. So first of all, one of the differences of this area, we're working on the two parallel type of frameworks, and it's sometimes even free. On one hand, art is an AI in data documents and frameworks. So everything was started with the GDPR. And data privacy framework released in 2018. And after that European Commission artificial intelligence Act was proposed in 2021. And it's still in the process of discussion. And there is also California Consumer Privacy Act in 2018. So is an AI in data privacy type of frameworks is one level. Second Level is a framework addressing disability. So there is a United Nations and global Convention of Human rights of people with a disability. And there is a there are specific organizations addressing such issues. And one of the example as a European Disability Forum. And we work recently with them presenting issues of our children and human rights. And working with the AI and social organization. They provide specific suggestions. So for instance, two years ago, just after a European Commission AI Act was released over 100 organizations, including European Disability Forum, signed the Open Letter of how to make AI safe for disabled community. And myself and our community. We prepared similar letter addressing neurodiversity, addressing cognitive disability and mental health. And we also send it as a suggestions. So is a second level third level as ethics of a technology in general and hopefully, more specific communities is driven by MIT Media Lab, Montreal AI Ethics Institute during the Institute in UK, many initiative driven by European Commission, and hopefully all of them finally, identify all the aspects of a human rights including intersectionality in gender, disability and ability, economic and social criteria, historical marginalization of particular groups, we still, by the way, plan, one of the projects is a festival of AI for indigenous communities in Australia. So this focus also is growing to understand all of the historical context, so we could edit this knowledge to the frameworks.
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Interesting. You mentioned also, the Museum of AI, where is this?
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It's still in the process of a confirmation if it will happen. It will happen in September this year in Australia. It happened last year. It's September also was a Saudi Arabia, I've created a Global AI Summit for the good of humanity. And significant part inspiration behind the program was a neuro diversity. And we exhibited many amazing teams, including social robotics for autism, some assistive technologies, and the whole spirit. And why are the program and task forces which were presented, but were driven by all of the layers of humanity, including technology, policy, and the rule of law and experiences and creative expression of this movement?
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Are you a leader, like the main leader for the frameworks, Yonah, for the getting involved for the area in neurodiversity?
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I would say I'm the main activist. There are a lot of very passionate, very smart and very talented people.
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Some of them present more technology area, some of them present more like a training, or working with it, child perspective, or kind of educational perspective, or some more specific children focused educators. My work is been activist and connect all together, and bring my perspective base based on my companies and projects and experiments and experiences also as a patient myself, because majority of the projects I've created, they were driven by my own needs as well. Because some people then people ask me, you know, you mentioned there are so many dangerous sides of AI. But what is really good about AI, what drives your passion to work in both on policy but also on all of these technologies and all of this experience, experiments, museums is because technology trained me, technology helped me to become a part of this society. I don't want to sound rude people didn't help me, technology helped me. I really spent years working through remote learning platforms, which provide me with the guidance with the advices, which helped me to build this vision of my occupation, profession and skills. So it I literally tried it in recent 18 years on myself. And it helped me to build a way to be independent explorer and researcher for the biggest university in the planet been excluded by almost every ecosystem before. So I still have a lot of hope about technology is an extremely powerful tool. And as an artist is a painter, you should you just should use this tool in the right way.
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