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Dublin, Dec. 03, 2021 (GLOBE NEWSWIRE) -- The "Artificial Intelligence in Healthcare Market (by Component, Application, End-user & Region): Insights & Forecast with Potential Impact of COVID-19 (2021-2025)" report has been added to ResearchAndMarkets.com's offering.

The global AI in healthcare market is forecasted to record a value of US$22.68 billion in 2025, progressing at a CAGR of 35.59%, for the duration spanning 2021-2025.

Factors such as rising utilization of robots for surgical and rehabilitation procedures, growing potential of AI-based tools for elderly care, surging investment in AI deals in healthcare and rising adoption of wearable devices are driving growth.

However, the market growth would be challenged by reluctance among medical practitioners regarding the adoption of AI-based technologies and concerns regarding data privacy. The market is anticipated to experience certain trends like shortage of skilled workforce, upsurge in demand for personalized medicine and rising number of cross-industry partnerships and collaborations.

The global AI in healthcare market can be segmented on the basis of component, application and end-user. Based on component, the market can be bifurcated into software solutions, hardware and services. According to application, the market can be categorized into clinical trials, robot assisted surgery, connected machines, fraud detection, virtual assistants, administrative workflow assistants, diagnosis, dosage error reduction, cybersecurity and others. Whereas, depending on end-users, the market can broadly be split into hospitals & healthcare providers, pharmaceutical & biotechnology companies, patients, healthcare payers and others.

The fastest growing regional market is North America due to surging adoption of machine learning in the medical sector leading to the growth of AI in the healthcare market in countries like the U.S. and Canada and increasing government initiatives to adopt digital platforms in healthcare industry and sector. Further, the COVID-19 outbreak has significantly promoted the implementation of remote health check-ups using digital tools, which positively impacted the market growth.

Market Dynamics

Growth Drivers

Key Trends and Developments

Challenges

Scope of the report

Key Topics Covered:

Market Overview

Impact of COVID-19

Market Analysis

Regional Market

Competitive Landscape

Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/9qkufe

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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The Role of AI in the Global Healthcare Market 2021: Rising - GlobeNewswire

The advent of these AI innovations has truly revolutionized the automobile industry.

The integration of cutting-edge technologies such as artificial intelligence, computer vision, IoT, and many more into vehicles has dramatically changed the image of the automobile industry. The automobile industry has started leveraging AI in the whole process starting from designing to after-sale service and drivers safety. Artificial intelligence has laid down the foundation for smarter vehicles in smart cities through this industry. Multiple hi-tech automotive companies have started leveraging cutting-edge technologies to strive in the competitive market with the utmost customer satisfaction.

3D Printers: The introduction of 3D printers has transformed the automobile industry in the last few years. The industry utilizes 3D printers for creating automotive prototypes to check fitness, making aesthetically pleasant parts, designing efficient car models, and many more. One of the important technologies behind 3D printers is Fused Filament Fabrication (FFF) that is used for the production as well as end-use parts in the automobile industry.

Smart Helmets: Smart helmets are one of the most important AI innovation in this industry for the safety of drivers. Smart helmets are completely different from traditional helmets for the implementation of GSM and GPS technologies. Drivers are inclined towards smart helmets owing to its advanced technology to call an ambulance or a family member post-accident, the presence of operation vibration sensors, alcohol sensors, crash alerts, LED lights, and many more.Autonomous Vehicles: Autonomous vehicle (AV) is the hottest topic in this industry to provide major help to citizens. Multiple hi-tech giants are aiming at manufacturing autonomous vehicles across the world. AV is capable of sensing the nearby environment and driving on its own without any human driver on the drivers seat. It can go anywhere like a classical car after entering the destination. It is very useful for senior citizens to have freedom and independence to roam around in the city.Collaborative Robots: Collaborative robots or cobots are thriving in this industry with their smart functionalities such as machine loading, machine tending, inspection, assembly in productions, and many more efficiently and effectively. Cobots tend to work with human employees to enhance outcomes, consistency, flexibility, and support without any potential error.Machine Vision: Machine vision is known as a key technology for optimizing different processes in value chain including quality assurance, production, logistics, and many more. The automobile industry is leveraging machine vision for unambiguous object detection, accelerating existing production processes, eliminating potential errors or risks, and so on. It has high speed where the algorithms need milliseconds to detect, analyze, and process sufficient data from images.

Automated Guided Vehicles: Automated Guided Vehicles (AGVs) have started providing multiple benefits to the automobile industry such as reduction in direct labour cost, eliminating workforce shortage, removing potential errors, limiting heavy goods handling, scalability, enhancing productivity, ensuring better safety, and many more. AGVs are known for carrying assembly lines while being forklift-sized wheeled carts to use software in navigating their movements efficiently and effectively.

Driver Monitoring System: Driver monitoring system is emerging as one of the top AI innovations for alerting drivers during drowsiness, distraction, and so on to avoid fatal road accidents. It is also known as a driver state sensing that utilizes a driver-facing hi-tech camera with infrared LEDs on the dashboard to track the eye movements of drivers. There is advanced on-board software that collects the data and creates an initial baseline of the normal active driver. It analyzes whether the driver is blinking more, feeling dizzy, narrowing the eyes, and other eye movements to issue audio alerts to keep him alert.

AI Cameras: The integration of AI cameras in different vehicles has increased revenues in the automobile industry. Drivers are preferring cars with AI cameras to provide safety to oneself as well as family members. These AI cameras are providing multiple services to drivers with a combination of multi-path approach as well as artificial intelligence for image or video recognition to make roads much safer.Vehicle Tracking Software: The automobile industry has recognized the importance of vehicle tracking software to locate the vehicle efficiently and effectively. GPS is set to provide a live update of different locations of a vehicle and its condition. It helps in the recovery of a stolen vehicle and provides details on fuel, speed, engine, and many more. There is an advanced mapping feature to zoom in to the street level with a close and accurate view for analysing routes without any error and wastage of time.Automotive Insurance: Automotive insurance plays an important role in this smart automobile industry. Drivers can perform their own auto damage assessment for their respective insurance companies without any potential error. There will be on-screen instructions on how to video the damage and later submit it for an automotive insurance claim. Artificial intelligence will inform drivers how to repair and the total cost covered by the insurance.

The automobile industry is waiting for more evolving and ever-changing trends of artificial intelligence to enhance productivity and manufacture smarter vehicles. The implementation of artificial intelligence is set to protect the safety of drivers and provide a good experience to passengers in the nearby future. Multiple hi-tech companies are aiming to capture the automotive industry by leveraging their artificial intelligence experience. Customers have started placing trust and faith in artificial intelligence algorithms behind the steering wheel and it has a major growth prospect in the future.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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10 AI Innovations that are Transforming the Automobile Industry - Analytics Insight

Zindi is all about using AI to solve real-world problems for companies and individuals. And the South Africa-based crowd-solving startup has done that over the last three years they have been in existence.

Just last year a team of data scientists under Zindi used machine learning to improve air quality monitoring in Kampala as another group helped Zimnat, an insurance company in Zimbabwe, predict customer behavior especially on who was likely to leave and the possible interventions that would make them stay. Zimnat was able to retain its customers by offering custom-made services to those who would have otherwise discontinued.

These are some of the solutions that have been realized to counter the data-centered challenges that companies, NGOs and government institutions submit to Zindi.

Zindi announces these challenges and invites its community of data scientists to take part in solution-finding competitions. Participating data scientists submit their solutions and the winner gets a cash prize. The hosts of the competitions get to use the best results to overcome the challenge they had like in an air quality monitoring project by AirQo, which sought solutions for forecasting air pollution across Uganda, and in helping Zimnat cut its losses.

So AirQo now has a dashboard that allows the public to check air quality and air quality forecasts. One of the exciting things about this project is that AirQo hired two of the winners from the challenge to help with the implementation of the project, said Zindi co-founder and CEO, Celina Lee. South African Megan Yates and Ghanaian Ekow Duker are the platforms other co-founders.

AirQo also raised funding from Google, based on the solution that they built, and theyll now be replicating it in other African countries, said Lee about the competition that was organised in partnership with the Digital Air Quality East Africa (DAQ EA) project of the University of Birmingham and the AirQo project from Makerere University, Kampala.

Zindi is a database of data scientists across Africa. The crowd-solving startup recently secured $1 million in seed funding. Photo Credits: Zindi

Among other notable private and public organizations that have tapped Zindi include Microsoft, IBM, Liquid Telecom and UNICEF, and the government of South Africa.

So far, Lee is excited about what Zindi has achieved and is enthusiastic about the communitys future, given how the crowd-solving startup has grown since launch. The platform is now providing alternatives and stepping up competition against traditional consulting firms operating across Africa, which are often expensive.

Zindis users have grown three-fold from the start of last year, to 33,000 data scientists from 45 countries across the continent. It has also paid data scientists $300,000 in prize money.

This number is set to grow as it hosts the third inter-university Umoja Hack Africa challenge in March next year, where college students will compete against one another for different solutions.

Zindi is using the inter-university competition to expose students to practical data science experiences and to solve real-life challenges using AI. During last years event the platform attracted about 2,000 students during the event that took place virtually because of the pandemic.

Students get to build their first machine learning models, and from there, it opens up all kinds of doors for their careers and education, said Lee, who is originally from San Francisco.

Zindi currently has a jobs portal to shorten the path from learning to earning. The talent placement portal allows organizations to tap from its pool of talent by posting openings.

The crowd-solving platform is also planning to introduce a learning component that provides training material to budding data scientists; this is after it realized a knowledge gap and need for training. Besides, Lee said that most of Zindis users are university students in need of learning experience, and who require enhanced skills to solve world problems.

The new plans will be made possible by a $1 million seed funding the platform recently secured.

Image Credits: Zindi

Lee said, For us, its really about scaling the community and creating more value for all of our data scientists.

So were going to be using the funding to introduce much more learning content, because one of the things we understand is that, especially in Africa, data science is such a new field. And a lot of our data scientists are still university students or very early in their careers. And theyre just looking for a chance to learn and build their skills.

The seed round was led by San-Francisco based VC firm Shakti, with participation from Launch Africa, Founders Factory Africa and FIVE35.

All these plans are toward building a strong data science community in Africa and for the continent, according to Lee, who said that they want to grow their users to reach one million in the near future. This, she said, will be achieved by opening up training opportunities to early career data scientists and by forming a strong community that encourages collaboration and mentorship.

Lee said, And so where we want to eventually reach a million data scientists in Africa we want to make data science something that any young person whos interested in pursuing this career has access to the tools, the connections and the experience that they need to make a successful career in this field.

Our vision is to make AI accessible to everyone.

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South African crowd-solving startup Zindi building a community of data scientists and using AI to solve real-world problems - TechCrunch

When offstage, Kra Mr, as well as other Sensorium's AI-driven artists, will be permanently available to communicate with fans via text chats and video calls in Sensorium Galaxy mobile app.

Sensorium virtual artists have been in development since last November. While Mubert's proprietary technology empowered these creatures to generate a constant flow of ever-changing music, Sensorium has equipped them with virtual bodies, intelligence, and social skills.

Driven by a unique combination of genetic AI and reinforcement learning, Sensorium's virtual DJs are able to support unscripted thought-provoking conversations. They can talk for hours without losing track of context and possess both long-term and short-term memory. What's more, the personalities of these virtual beings constantly evolve through interactions with users. This process unfolds naturally and unpredictably without any interference from developers.

"Increasingly more creators embrace direct communications with their fans via live streams on Instagram and other channels. We take this a step further by allowing fans to dive deeper into the interactions with their favorite artists. AI-driven Social Artists are able to dedicate quality time to every single fan, listening, responding, and adapting to their individual preferences, thereby ushering in a new era in music," says Sasha Tityanko, Deputy CEO at Sensorium.

Each one of Sensorium's Social Artists has its own signature performance style and a distinct personality based on a virtual biography. Kra Mr, for instance, is a non-binary AI-researcher and techno DJ from Berlin, bringing their fascination for AI to all musical productions. The name of their first album "Anthropic principle" reflects the main concept behind it, alluding to the idea that the universe needs an observer to exist. In their music Kra Mr reinterprets this principle from a VR perspective, raising the question "what if an AI is an observer of the metaverse?".

In a suite of 8 pieces, Kra Mr weaves a canvas out of abstract questions. The restless artificial mind keeps on asking what do you, people, feel listening to my music? You created me, but now I am able to initiate research about you. It is an experiment on humanity by AI.

Paul Zgordan, Music Director at Mubert: "Mubert was responsible for creating a unique musical experience. We fulfilled one of our main tasks: nurturing a connection between the crowd and the DJ, so the artist's performance corresponds to the audience's mood on the dancefloor. Now we are excited to experiment and improve our product further using DJs-users interactions in the metaverse as an inspiration".

Fans will soon be able to experience Kra Mr's performances live in PRISM one of Sensorium Galaxy's virtual worlds dedicated to extraordinary music events. Sensorium Galaxy is scheduled to go live in several months, while the Sensorium appis already available for download.

About Sensorium Galaxy

Sensorium Galaxy is a digital metaverse that revolutionizes the way people interact with each other and experience the arts. In the alternate universe of Sensorium Galaxy, users immerse themselves in exciting new worlds to get together with their loved ones, meet new people, and participate in unique virtual activities.

The Galaxy is being built in partnership with world-known artists, producers, and entertainment companies. Among them are Jay-Z's Roc Nation and Yann Pissenem, the creator of the world-leading nightlife hubs Ushuaa Ibiza and H Ibiza.

Website: https://sensoriumgalaxy.com/

About Mubert:

Mubert is a platform powered by music producers that helps creators and brands generate unlimited royalty-free music with the help of AI. Mubert's mission is to empower and protect the creators. Our purpose is to democratize the Creator Economy.

Website:https://mubert.com

SOURCE Sensorium Corporation

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Kra Mr becomes the first Social AI-Driven Artist to release an album - PRNewswire

Dynamics 365 Sales Insights empowers sellers to deliver personalized engagement and build profitable relationships.Capabilities include:

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Currently available in the United States and EMEA. AI models for natural language processing, such as those used in Talking points and Notes analysis, are for English only.

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How to enable to Workaround? The workaround is active as soon as AIGTC is running Does it interfear with AIGFP traffic? No, but if you only want live AI in the Sim you can stop AIGFP traffic via the settings,

First of all, a huge welcome to Aerosoft for joining the AI Traffic party in MSFS, Simple Traffic is a great product for XBOX users and users that just want traffic in their Sim and do not care about the

Hey Guys, thanks for talking an early part in our long journey to provide the same AI setup of P3D to MSFS. To be honest we are all overwhelmed by the total numbers of users we already have thank

More then a year after the release of MSFS and almost three years after the first AIG AI Manager beta it is time for the next major step the release of AIM 1.1 as public beta with included MSFS

Before starting with the public beta there are some information you should know. Setup P3D If you want to upgrade to the beta and already have Aim 1.0 running, download the BETA version and replace the old files with the

In preperation for the public bete test of AIG AI Manager for MSFS we have updated our AIGTC for MSFS. Version 0.5 will be working with MSFS and P3Dv4 and v5 on the same system. Compared to the P3D Version

Over the last months the tool has been tested by our team and some external testers and it seems more or less stable now. Users of P3D and AIM 1.0 will see that this Beta of AIM has currently less

Since the first announcement of AIGTC and AIGPLN back in late 2019 a lot have changed in the FS world. P3D has got a new big update, and MSFS was released. All these changes had an impact on the initial

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Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

Artificial intelligence (AI), machine learning (ML) and other emerging technologies have potential to solve complex problems for organizations. Yet despite increased adoption over the past two years, only a small percentage of companies feel they are gaining significant value from their AI initiatives. Where are their efforts going wrong? Simple missteps can derail any AI initiative, but there are ways to avoid these missteps and achieve success.

Following are four mistakes that can lead to a failed AI implementation and what you should do to avoid or resolve these issues for a successful AI rollout.

When determining where to apply AI to solve problems, look at the situation through the right lens and engage both sides of your organization in design thinking sessions, as neither business nor IT have all the answers. Business leaders know which levers can be pulled to achieve a competitive advantage, while technology leaders know how to use technology to achieve those objectives. Design thinking can help create a complete picture of the problem, requirements and desired outcome, and can prioritize which changes will have the biggest operational and financial impact.

One consumer product retail company with a 36-hour invoice processing schedule recently experienced this issue when it requested help speeding up its process. A proof of concept revealed that applying an AI/ML solution could decrease processing time to 30 minutes, a 720% speed increase. On paper the improvement looked great. But the companys weekly settlement process meant the improved processing time didnt matter. The solution never moved into production.

When looking at the problem to be solved, its important to relate it back to one of three critical bottom-line business drivers: increasing revenue, increasing profitability, or reducing risk. Saving time doesnt necessarily translate to increased revenue or reduced cost. What business impact will the change bring?

Data can have a make-or-break impact on AI programs. Clean, dependable, accessible data is critical to achieving accurate results. The algorithm may be good and the model effective, but if the data is poor quality or not easy and feasible to collect, there will be no clear answer. Organizations must determine what data they need to collect, whether they can actually collect it, how difficult or costly it will be to collect, and if it will provide the information needed.

A financial institution wanted to use AI/ML to automate loan processing, but missing data elements in source records were creating a high error rate, causing the solution to fail. A second ML model was created to review each record. Those that met the required confidence interval were moved forward in the automated process; those that did not were pulled for human intervention to solve data-quality problems. This multistage process greatly reduced the human interaction required and enabled the institution to achieve an 85% increase in efficiency. Without the additional ML model to address data quality, the automation solution never would have enabled the organization to achieve meaningful results.

Each type of AI solution brings its own challenges. Solutions built in-house provide more control because you are developing the algorithm, cleaning the data, and testing and validating the model. But building your own AI solution is complicated, and unless youre using open source, youll face costs around licensing the tools being used and costs associated with upfront solution development and maintenance.

Third-party solutions bring their own challenges, including:

In highly regulated industries, these issues become more challenging since regulators will be asking questions on these topics.

A financial services company was looking to validate a SaaS solution that used AI to identify suspicious activity. The company had no access to the underlying model or the data and no details on how the model determined what activity was suspicious. How could the company perform due diligence and verify the tool was effective?

In this instance, the company found its only option was to perform simulations of suspicious or nefarious activity it was trying to detect. Even this method of validation had challenges, such as ensuring the testing would not have a negative impact, create denial-of-service conditions, or impact service availability. The company decided to run simulations in a test environment to minimize risk of production impact. If companies choose to leverage this validation method, they should review service agreements to verify they have authority to conduct this type of testing and should consider the need to obtain permission from other potentially impacted third parties.

When considering developing an AI solution, its important to include all relevant decision makers upfront, including business stakeholders, IT, compliance, and internal audit. This ensures all critical information on requirements is gathered before planning and work begins.

A hospitality company wanted to automate its process for responding to data subject access requests (DSARs) as required by the General Data Protection Regulation (GDPR), Europes strict data-protection law. A DSAR requires organizations to provide, on request, a copy of any personal data the company is holding for the requestor and the purpose for which it is being used. The company engaged an outside provider to develop an AI solution to automate DSAR process elements but did not involve IT in the process. The resulting requirements definition failed to align with the companys supported technology solutions. While the proof of concept verified the solution would result in more than a 200% increase in speed and efficiency, the solution did not move to production because IT was concerned that the long-term cost of maintaining this new solution would exceed the savings.

In a similar example, a financial services organization didnt involve its compliance team in developing requirements definitions. The AI solution being developed did not meet the organizations compliance standards, the provability process hadnt been documented, and the solution wasnt using the same identity and access management (IAM) standards the company required. Compliance blocked the solution when it was only partially through the proof-of-concept stage.

Its important that all relevant voices are at the table early when developing or implementing an AI/ML solution. This will ensure the requirements definition is correct and complete and that the solution meets required standards as well as achieves the desired business objectives.

When considering AI or other emerging technologies, organizations need to take the right actions early in the process to ensure success. Above all, they must make sure that 1) the solution they are pursuing meets one of the three key objectives increasing revenue, improving profitability, or reducing risk, 2) they have processes in place to get the necessary data, 3) their build vs. buy decision is well-founded, and 4) they have all of the right stakeholders involved early on.

Scott Laliberte is Managing Director of the Emerging Technology Group at Protiviti.

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Is your AI project doomed to fail before it begins? - VentureBeat

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NYC aims to be first to rein in AI hiring tools - Bend Bulletin

By Jonny Lupsha, Current Events WriterDue to computational programming, artificial intelligence may seem like it understands issues and has a sense of moralitybut philosophically and scientifically is that possible? Photo By PopTika / Shutterstock

Many questions have arisen since the advent of artificial intelligence (AI), even in its most primitive incarnations. One philosophical point is whether AI can actually reason and make ethical decisions in an abstract sense, rather than one deduced by coding and computation.

For example, if you program into an AI that intentionally harming a living thing without provocation is bad and not to be done, will the AI understand the idea of bad, or why doing so is bad? Or will it abstain from the action without knowing why?

Researchers from a Seattle lab claim to have developed an AI machine with its own sense of morality, though the answers it gives only lead to more questions. Are its morals only a reflection of those of its creators, or did it create its own sense of right and wrong? If so, how?

Before his unfortunate passing, Dr. Daniel N. Robinson, a member of the philosophy faculty at Oxford University, explained in his video series Great Ideas of Psychology that the strong AI thesis may be asking relevant questions to solve the mystery.

Imagine, Dr. Robinson said, if someone built a general program to function that way, so the program could provide expert judgments on cardiovascular disease, constitutional law, trade agreements, and so on. If the programmer could then have the program perform these tasks in a way indistinguishable from human experts, the position of the strong AI thesis is that its programmers have conferred on it an expert intelligence.

The strong AI thesis suggests that unspecified computational processes can exist which then would sufficiently constitute intentionality due to their existence. Intentionality means making a deliberate, conscious decision, which in turn implies reasoning and a sense of values. However, is that really possible?

The incompleteness theoremGdels theoremsays that any formal system is incomplete in that it will be based on, it will require, it will depend on a theorem or axiom, the validity of which must be established outside the system itself, Dr. Robinson said. Gdels argument is a formal argument and it is true.

What do we say about any kind of computational device that would qualify as intelligent in the sense in which the artificial intelligence community talks about artificial intelligence devices?

Kurt Gdel developed this theorem with the apparent exception for human intelligence that liberates it from the limitations of his own theorem. In other words, Gdel believed there must be something about human rationality and intelligence that cant be captured by a formal system with the power to generate, say, an arithmetic.

If you accept that as a general proposition, then what you would have to say is that human intelligence cannot be mimicked or modeled on purely computational grounds, Dr. Robinson said. So, one argument against the strong AI thesis is that its not a matter of time before it succeeds and redeems its promises. It will never succeed and redeem its promises for the simple reason that the intelligence it seeks to simulate, or model, or duplicate, is, in fact, not a computationally-based [] intelligence.

Should the mystery ever be solved, we may finally be able to answer Philip K. Dicks question: Do androids dream of electric sheep?

Edited by Angela Shoemaker, The Great Courses Daily

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Seattle Researchers Claim to Have Built Artificial Intelligence That Has Morality - The Great Courses Daily News

Children are already interacting with AI technologies in many different ways: they are embedded in toys, virtual assistants, video games, and adaptive learning software. Their impact on children's lives is profound, yet UNICEF found that, when it comes to AI policies and practices, childrens rights are an afterthought, at best.

In response, the UN childrens agency has developed draft Policy Guidance on AI for Children to promote children's rights, and raise awareness of how AI systems can uphold or undermine these rights.

Conor Lennon from UN News asked Jasmina Byrne, Policy Chief at the UNICEF Global Insights team, and Steven Vosloo, a UNICEF data, research and policy specialist, about the importance of putting children at the centre of AI-related policies.AI Technology will fundamentally change society.

Steven Vosloo, a UNICEF data, research and policy specialist, by UNICEF

Steven Vosloo At UNICEF we saw that AI was a very hot topic, and something that would fundamentally change society and the economy, particularly for the coming generations. But when we looked at national AI strategies, and corporate policies and guidelines, we realized that not enough attention was being paid to children, and to how AI impacts them.

So, we began an extensive consultation process, speaking to experts around the world, and almost 250 children, in five countries. That process led to our draft guidance document and, after we released it, we invited governments, organizations and companies to pilot it. Were developing case studies around the guidance, so that we can share the lessons learned.

Jasmina Byrne AI has been in development for many decades. It is neither harmful nor benevolent on its own. It's the application of these technologies that makes them either beneficial or harmful.

There are many positive applications of AI that can be used in in education for personalized learning. It can be used in healthcare, language simulation and processing, and it is being used to support children with disabilities.

And we use it at UNICEF. For example, it helps us to predict the spread of disease, and improve poverty estimations. But there are also many risks that are associated with the use of AI technologies.

Children interact with digital technologies all the time, but they're not aware, and many adults are not aware, that many of the toys or platforms they use are powered by artificial intelligence. Thats why we felt that there has to be a special consideration given to children and because of their special vulnerabilities.

UNICEF/ Diefaga

Children using computers

Steven Vosloo The AI could be using natural language processing to understand words and instructions, and so it's collecting a lot of data from that child, including intimate conversations, and that data is being stored in the cloud, often on commercial servers. So, there are privacy concerns.

We also know of instances where these types of toys were hacked, and they were banned in Germany, because they were considered to be safe enough.

Around a third of all online users are children. We often find that younger children are using social media platforms or video sharing platforms that werent designed with them in mind.

They are often designed for maximum engagement, and are built on a certain level of profiling based on data sets that may not represent children.

Jasmina Byrne, Policy Chief at the UNICEF Global Insights team, by UNICEF

Predictive analytics and profiling are particularly relevant when dealing with children: AI may profile children in a way that puts them in a certain bucket, and this may determine what kind of educational opportunities they have in the future, or what benefits parents can access for children. So, the AI is not just impacting them today, but it could set their whole life course on a different direction.

Jasmina Byrne Last year this was big news in the UK. The Government used an algorithm to predict the final grades of high schoolers. And because the data that was input in the algorithms was skewed towards children from private schools, their results were really appalling, and they really discriminated against a lot of children who were from minority communities. So, they had to abandon that system.

That's just one example of how, if algorithms are based on data that is biased, it can actually have a really negative consequences for children.

Steven Vosloo We really hope that our recommendations will filter down to the people who are actually writing the code. The policy guidance has been aimed at a broad audience, from the governments and policymakers who are increasingly setting strategies and beginning to think about regulating AI, and the private sector that it often develops these AI systems.

We do see competing interests: the decisions around AI systems often have to balance a profit incentive versus an ethical one. What we advocate for is a commitment to responsible AI that comes from the top: not just at the level of the data scientist or software developer, from top management and senior government ministers.

Jasmina Byrne The data footprint that children leave by using digital technology is commercialized and used by third parties for their own profit and for their own gain. They're often targeted by ads that are not really appropriate for them. This is something that we've been really closely following and monitoring.

However, I would say that there is now more political appetite to address these issues, and we are working to put get them on the agenda of policymakers.

Governments need to think and puts children at the centre of all their policy-making around frontier digital technologies. If we don't think about them and their needs. Then we are really missing great opportunities.

Steven Vosloo The Scottish Government released their AI strategy in March and they officially adopted the UNICEF policy guidance on AI for children. And part of that was because the government as a whole has adopted the Convention on the Rights of the Child into law. Children's lives are not really online or offline anymore. And it's a digital life now.

This conversation has been edited for length and clarity. You can listen to the interview here.

UNICEF/ Schverdfinger

UNICEF has developed policy guidance to protect children from the potential impacts of AI

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Digital Child's Play: protecting children from the impacts of AI - UN News