Monthly Archives: May 2022

Three systems that verify peoples age with AI technology to prevent minors from being exposed to harmful content have been endorsed by Germanys Commission for the Protection of Minors in the Media. EURACTIV Germany reports.

The commission is Germanys central supervisory body for the protection of minors on private nationwide television and the internet.

These AI systems, which the body has given a positive rating, are trained by machine learning to assess a persons age based on biometric characteristics.

The fact that AI can now also be used for age verification and thus to protect children and young people from problematic content is an important, new step, said the commissions chairman, Marc Jan Eumann, in a press release last week (24 May).

According to Eumann, the use of AI in this area is a milestone in the technical protection of children and young people in media.

Positive assessment

The supervisory body has now positively assessed three different AI systems for age verification. These are the facial age estimation software, as well as the Age Verification and the Yoti software, which are being examined as possible age verification systems.

As a kind of safety mechanism for children who look older than they are, the child protection authority has determined a five-year buffer.

Individuals must be recognised by the system as being at least 23 in order to gain access to 18+ rated content, it said.

Another control feature stipulates that age verification cannot simply be bypassed using still images.

The procedures for this auto-identification are being developed in constant consultation with regulators and security authorities, Rebekka Wei, Head of Trust and Security at the digital association Bitkom, told EURACTIV.

Digital identification has not only become much more efficient through such procedures but is also less prone to errors than human identification, said Wei.

Interstate treaty

According to the Interstate Treaty on the Protection of Minors in the Media, to which Germanys sixteen Bundeslander have agreed, youth-endangering content may only be distributed in telemedia if the provider can ensure only adults can access it.

Therefore, Germanys youth protection body is encouraging companies to review their youth media protection strategies and ensure they comply with legal requirements.

Data protection

According to Maximilian Funke-Kaiser, digital policy spokesman of the liberal Free Democratic Party (FDP) in the German Bundestag, it should not be forgotten that age verification and the processing of data ensure data protection.

The responsible handling of biometric data is all the more important when involving children and young people. The processing of personal data requires a legal basis and consent declared by the person with parental authority, Funke-Kaiser told EURACTIV.

According to him, data collected in the context of age verification may in no way be used for commercial purposes.

EU directive

In this context, the Audiovisual Media Services Directive obliges EU countries to take action to ensure adequate child protection since it entered into force in 2013.

This includes instruments for age verification which are supposed to be very strict, especially for pornography and violence.

According to the youth protection body, Germany has already made significant progress with its implementation, and other countries like France are also catching up.

[Edited by Oliver Noyan, Daniel Eck/Alice Taylor]

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German youth protection body endorses AI as biometric age-verification tool - EURACTIV

TOKYO--(BUSINESS WIRE)--Mitsubishi Electric Corporation (TOKYO: 6503) announced today that it has developed a technology based on its Maisart1 AI technology that automatically constructs knowledge graphs by acquiring key phrases, authors, citation relationships and whole-part relationships of elements in various materials, including figures and tables, and then visualizes the relevance of the information so that users can identify and understand the most necessary information quickly and intuitively. The new technology is expected to greatly reduce the amount of time users spend gathering information.

Conventionally, it can take much time to collect necessary information from the overload of information people are exposed to these days. Moreover, to quickly find necessary or interesting information, the information must not only be digitalized but also managed based on information relationships within or among the materials. Mitsubishi Electrics new AI technology structurally digitalizes materials and data by extracting important information and estimating the interrelationships in advance. Thereafter, when users explore the information, they can grasp the strengths of relationships based on the varying widths of bands in diagrams (see figure above) to quickly and intuitively find necessary, interesting and even previously unnoticed information.

For the full text, please visit: http://www.MitsubishiElectric.com/news/

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Mitsubishi Electric's AI Creates Knowledge Graphs from Text and Graphics to Visualize Information Relevance - Business Wire

Theres nothing more personal than ones health and wellbeing. While receiving care, patients often face extreme challenges. They rightfully expect personalization, but despite the countless amounts of data, research, and scientific discoveries that have dramatically improved the quality of care, patients often leave healthcare facilities feeling like a number.

According to McKinsey, patients expect the same digital experiences they have grown accustomed to in retail, insurance, and banking. Those sectors long ago adopted innovations and processes that allow them to use technology to enhance the experience and put the customer first. Patients who have grown used to such benefits now demand a similar engagement from their healthcare providers and payers.

Patients expect to be at the center of their care. They want their providers and payers to use omnichannel methods to reach them. They are demanding transparency in decision-making and the ability to manage administrative actions and finances independently. They expect interactions to be frictionless across their preferred methods of communication, regardless of the industry. Patients demand quick and efficient service without repeating or resharing information.

Labor shortages and Covid-19 surges continue to pressure providers and payers. They are having trouble meeting these demands with available staff and tools. But voice AI technology has the potential to help. It can drive the next wave of healthcare innovation by making each patient advocate or service representative smarter and nimbler to meet the healthcare consumers growing expectations, with the added benefit that AI technology is always on and unbiased.

Shifting customer expectations

When the first wave of Covid-19 taxed care facilities, patients became more tolerant. According to the latest Pega Healthcare Engagement Survey, which surveyed more than 2,000 U.S. consumers and 200 healthcare industry leaders, 63% of patients said they would switch doctors due to poor communication and engagement (down 23% from the previous year). This year, however, personalized healthcare has become a higher priority.

Health experts know that better patient engagement can lead to better health outcomes. With attention, patients become active members of their care team. They regularly communicate throughout the process as they manage their health. They ultimately become better informed and more involved in their treatment options and care decisions.

Because each patient has varying levels of both health literacy and willingness to participate in the care process, engagement can be a challenge. However, AI-driven service and care solutions can remove barriers and bias, personalize interactions, improve health access and equity, and automate access to knowledge and insight. Benefits such as these can free service agents to serve and focus on the patient experience instead of processes.

Conversational AI solutions operate as copilots for the agent. The technology takes in keywords and phrases, interprets them, and then recommends steps to resolution and additionally eliminates tedious manual processes, such as error-prone data entry or data searches. They also analyze intent to guide caregivers (and agents) in the most efficient and empathetic service possible.

With voice AI capturing data and guiding the conversation, agents can mitigate distracting back-end tasks and better focus on listening and delivering quality services. In addition, care providers are armed with the tools and resources to better answer questions rapidly and accurately in real-time, from human to human.

Putting the care back into healthcare

In the past, AI has often been thought of as cold and impersonal, but over time, AI capabilities have become usable in a more intuitive and empathetic way. With continuous advancements and powerful algorithms, AI can interpret the unique circumstances and patient preferences that play a critical role in care delivery and service.

AI has the power to be used to minimize care gaps, improve early prevention, and give patients the best chance of getting the right treatments quickly while avoiding complications. The technology can predict and coordinate with patients to proactively set up appointments on a regular cadence through their preferred communication channels. It can also adapt over time. For example, if a patient doesnt reply to an email but answers text messages, AI will learn these preferences and automatically update them. It can also continually configure messaging, incorporating empathetic language for the appropriate situation. Imagine a patient is in an area with forest fires; the agent can be prompted to ask how the person is doing and if they are safe before continuing the call, thereby personalizing the customers experience.

The intersection of human connection and technology through voice AI holds the power to revolutionize how patients interact with their healthcare providers and payers. As a result, healthcare organizations across the value chain that prioritize high-quality personalized service and care will improve both business and health outcomes and will be better positioned to retain and increase their patient populations, even as the pandemic persists and beyond. We need personalization, and a fundamental way forward is with voice AI technology.

Photo: berya113, Getty Images

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The healthcare industry has a problem with personalization: How voice AI can help - MedCity News

By Joseph Wang, Junior Data Scientist at VRGL

The wealth management business has traditionally been late to adopt new technology, primarily because of caution about security. It took nearly a decade for firms to trust putting data in the cloud. Today, many other industries are leveraging artificial intelligence (AI) to improve processes. The wealth management industry is still using processes that are tedious and manual. But that is about to change.

AI will not only accelerate but automate both client acquisition and service in the wealth management space. Acquiring clients is very relationship-driven, often consisting of numerous dinners and rounds of golf. Today, when a relationship between the prospective client and wealth manager is established, the wealth manager will begin the manual process of entering the clients statement data into analytics software to become familiar with the portfolio. After that, a proposal is generated to bring forth ways to improve the current portfolio.

Unlock hidden potential

Once a decision is made to hire the wealth manager, the process begins of maintaining the ongoing relationship, managing the portfolio, trading, compliance, among other responsibilities. Described above is an example of the traditional prospecting journey for wealth managers. It can take several months to even years as it is challenging to build trust and time intensive to extract, process, and analyze statement data manually. How can AI accelerate this process and simplify decision-making for wealth managers?

In addition to all the process improvements AI can bring to wealth managers, it can also simplify decisions in both client acquisition and investment management. As mentioned above, the client acquisition process for a wealth manager can be long and arduous, but AI can provide solutions as to whether a prospect should be pursued at all. Using AI, a recommender system can be built to suggest wealth managers to clients based on the clients risk tolerance, ESG beliefs, and other characteristics.

Recommender systems have been in use for decades and are how Google suggests ads to you based on your search history or how Netflix highlights movies you may want to watch based on your history. Based on a clients past investments and current characteristics, a recommender system could provide a list of recommended wealth managers who are suitable. Not only does this save the prospect time in finding an appropriate wealth manager, but also allows the wealth manager to save time not pursuing incompatible prospects.

Once hired by the client, the wealth manager often selects an investment strategy for the client in the form of a model portfolio based on the clients characteristics. Using AI for this selection allows the wealth manager to be more confident that the decision is backed by quantitative reasoning. Many unsupervised learning methods, such as clustering or expectation-maximizing (EM) algorithms, can find patterns in data and group similar data points. By applying such methods to a group of clients, wealth managers would not only be able to better understand similar clients, but also quickly and confidently categorize a new client. By selecting model portfolios using unsupervised learning, wealth managers would reduce the time analyzing client portfolio, as well as be confident in the quantitative validity of the result.

Combatting challenges

AI implementation in the wealth management business is not without challenges. The requirement for data accuracy is a continued pain point in statement parsing. Tuning the confidence level required for OOD data (Out-of-Distribution; that is, data or formats never seen before) would be challenging and may pose the risk of leaking confidential client information.

Privacy and security become a top priority when utilizing AI, and wealth management firms should develop their internal framework and preemptively address client concerns about the use of sensitive information. Additionally, even with significant inroad, decisions made by AI are still relatively poorly explainable, sometimes even unreliable when facing new data, and the regulatory landscape is constantly evolving. Practitioners are best served using a mosaic approach, retain a healthy suspicion for AI-provided recommendations as to any other source of information, and be ready to intervene.

Heightened efficiency with AI

AI has already positively affected many industries from automating tedious and manual tasks to bringing to light new information in formerly collected data. As AI solutions continue to be adopted by wealth managers, it will only become clearer just how much more efficient the process can be.

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Wealth Management Is a Tech Late Bloomer, But AI Will Change That - Global Banking And Finance Review

Organised crime that involves indentured labour remains big business, amassing an estimated $50-150 billion annually. And the problem is far closer to home than many realise.

Modern AI approaches, NLP and data fusion have a role to play in tackling criminal activity

Despite Britain having banned slavery outright in 1833, as recently as 2020, the Centre for Social Justice estimated that there were 100,000 or more modern slaves in the UK alone.

To profit from this exploitation of human rights, criminal gangs use various sophisticated psychological, financial and physical techniques to maintain control over their workforce. Often targeting vulnerable people who have nowhere to turn such as homeless or substance-dependent individuals and those who may not even be aware they are being exploited. An example is debt bondage where people end up working for little or no wages in order to repay debt. This is one of the most common forms of modern slavery, according to the UN.

This all makes detecting and disrupting modern slavery extremely problematic. Cases reported to the police arerising, but perhaps more than 90% of modern slavery still goes undetected. This makes the crime as hidden as it is pervasive, deepening consequences for business, government and society which must all work together to spot the red flags.

Laws such as The Modern Slavery Act (2015) and the EUs SixthAnti-Money Laundering Directive are raising the topic of modern slavery to board-level, but questions remain surrounding implementation and accountability.

It is not easy, but as a global community, we have the capacity to end slavery. Bold management, social policies and control frameworks will all be essential. Unfortunately, legacy technology and institutional structures both complicate the path ahead.

Organisations have a regulatory duty to identify anti-money laundering (AML) and enforce sanctions through strong customer due diligence. While successful detection of money laundering will ultimately identify many involved in human trafficking and modern slavery, we know that detection is limited. The UN estimates that $1.6 trillion is laundered through the global financial system annually and only 1% of illicit financial flows are intercepted globally.

The types of payments used to commit human trafficking can be particularly hard to detect. Low-value payments made to a few dozen migrant workers all sharing a few addresses is unlikely to trigger suspicion.

Fortunately, there is significantly enhanced awareness of the issues, and technology can now provide some vital support to companies, banks and governments.

Modern artificial intelligence (AI) approaches, such as utilising machine learning to grapple with huge quantities of data to identify patterns, provide a new opportunity to tackle slavery and trafficking which was simply not practical manually. Even with an army of compliance staff.

Advanced data analytics help companies automatically detect risk in their supply chain and among their customers. It can scale their understanding of available data, joining the dots automatically to detect and thwart human trafficking and modern slavery.

We are now seeing a step change in how entity resolution and natural language processing (NLP) are helping suppliers and partners across the entire supply chain network make substantial leaps forward in tackling this crime. Using AI to sift through vast amounts of data, technology is now being deployed on the front lines of the fight against modern slavery in the following ways:

Enhancing due diligence Traffickers and criminal gangs have adopted stratagems to avoid detection such as acquiring legitimate enabling assets such as bank accounts, national insurance numbers and tax details. These allow funds to be deposited, withdrawn and laundered, and all the while avoiding scrutiny by the police and employers.

Critically, it allows criminals to put victims in well-paying jobs in the supply chain. Criminal gangs also maintain control of victims earnings by opening bank accounts in their names but not in their control, providing the bank with the required onboarding documents, such as proof of address and utility statements.

Analytics can spot hidden connections between otherwise seemingly disconnected individuals, meaning that next generation KYC checks can more reliably monitor for deception and violations and involve the law, if necessary.

Screening can also identify those perpetrating slavery and trafficking. Banks and other organisations can get early indication of clients who have been associated with illicit activity before either through exposure in the news media or through specialist watchlists.

At the root of all criminal activity is profit. Making these crimes difficult or impossible to commit profitably will ultimately benefit the potential victims.

Employment vetting It is a widespread misconception to think that victims are mostly paid cash or off-the-books. With legitimate assets and tax codes, victims can unwittingly earn tens of thousands of pounds a year while only receiving a small stipend on top of their food and accommodation.

But times are a changing. Prompted by the Modern Slavery Act and Anti-Money Laundering Directives, employment agencies and supply chain partners such as factories and warehouses are increasingly looking at the details provided by workers, conducting their own due diligence.

Sophisticated entity resolution that can uniquely identify individuals from ambiguous and sparse datasets can detect the tell-tale red flags of exploitation such as unusual numbers of employees sharing the same address or bank details.

Complex investigations Data fusion technologies are now helping resource-constrained teams of intelligence analysts to more easily extract data from any source whether structured or unstructured. Natural language processing (NLP) and entity resolution combined with flexible link-analysis help investigators build up a single, centralised knowledge graph for a case or network of criminal gangs. The graph helps connect the dots automatically between victims, suspects phone numbers, bank accounts, transactions, flight records or any other evidence collected during an investigation.

The application of AI, data fusion and other techniques is a key development in the fight against modern slavery and human trafficking. It can automatically isolate risks at any point in the client or employee lifecycle and help the entire ecosystem of businesses, agencies, financial institutions and authorities understand the tell-tale signs of human trafficking and exploitation.

Entity resolution, automatic prioritisation, NLP and data fusion all play a role in ensuring that pertinent data is not missed. Enforcement analysts can then more easily uncover these hidden crimes bringing about a step-change in the way that modern slavery is detected and prevented.

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How AI and data fusion are aiding the battle against modern slavery - FinTech Futures