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Bridging the Gap: Evaluating Technology Companies’ Efforts in AI Consumer Education

Agata Karkosz, Leader at FPAcademy at Future Processing

AI is becoming increasingly influential across various sectors and industries. And its impact is only expected to grow in the future. In fact, AI adoption has more than doubled since 2017, with businesses making larger investments to scale and fast-track development.

With such rapid advancements taking place, it is important to educate consumers about navigating the complexities of the technology.

The Current Landscape of AI Education

There are several companies actively providing education and training in the field of AI. Google AI has been offering AI and machine learning courses to consumers for a few years now, while lesser-known companies, like Coursera, edX and Fast.ai, have also entered the space.

Nevertheless, these offerings are often catered to people interested in learning and developing skills in the technology, rather than the everyday consumer. In fact, research released last year by leading software development company, Future Processing, found that more than two-thirds of UK consumers aged 50 and above felt technology companies needed to do more to help them understand AI.

Respondents were asked what AI applications and tools they are currently using, with AI voice assistants and customer service chatbots coming out on top. But, because the technology’s capabilities are wide-reaching, companies need to up their game to ensure the average consumer is in the know.

Educational Initiatives: Are They Enough?

Companies must work harder to help consumers of varying levels of technical expertise understand AI. Doing so will help demystify AI and build consumer confidence and trust.

Understanding this need, Google AI offers resources such as the “Machine Learning Crash Course”, a free online course designed to introduce individuals to machine learning concepts. Microsoft, too, has its AI School, an online platform supplying free courses and resources covering various AI topics, while other major firms, including Facebook, Intel, Amazon and NVIDIA, have sparked similar education initiatives.

Still, due to fresh technological advancements and research breakthroughs, as well as increased data availability, open source and collaboration efforts, rapid industry adoption, and greater funding and investment, the challenge for companies comes in maintaining up-to-date and readily available education materials.

The Challenge of Simplifying Complexity

The inherent complexity of AI arises from the interdisciplinary nature of the field, combining elements of computer science, mathematics, statistics, neuroscience, and engineering. AI involves complex algorithms, sophisticated mathematical models, and intricate programming structures that are hard to rationalise.

Balancing the need to simplify AI concepts for broader understanding while retaining the essential information poses a significant communication challenge for businesses. Oversimplifying may lead to misconceptions or a lack of appreciation for the complexities involved while providing too much detail can result in confusion.

Successful communication often involves using relatable metaphors, real-world examples, and interactive experiences to engage consumers and help them grasp the fundamental principles without getting lost in technical intricacies. Effective communication about AI requires collaboration between experts, educators, communicators, and the general public to ensure a more informed and inclusive understanding of this rapidly advancing field.

Transparency and Explainability

Enhancing transparency and explainability in AI systems has become a key focus for technology companies to address concerns related to bias, accountability, and trust. Several efforts have been made to make AI systems more understandable and interpretable. Some common strategies and initiatives include:

Interpretable Models

Many technology companies are working on developing AI models that are inherently more interpretable. This involves using algorithms and architectures that produce results that can be easily explained and understood. For example, decision trees, rule-based systems, and linear models are often more interpretable than complex deep neural networks.

Explainable AI (XAI) Techniques

Explainable AI is a research area focused on developing techniques and tools that help users understand the decisions made by AI models. Techniques such as feature importance analysis, saliency maps, and attention mechanisms aim to highlight the factors influencing the model’s predictions.

Ethical AI Guidelines

Many companies have established ethical AI guidelines and principles prioritising transparency and fairness. These guidelines may include commitments to avoiding biased data, providing clear explanations for decisions, and involving diverse perspectives in the development process.

User-Friendly Interfaces

Technology companies are investing in user-friendly interfaces that allow users to interact with AI systems more intuitively to enhance transparency. Dashboards, visualisations, and plain-language explanations help users understand the model’s behaviour and outputs.

Addressing Challenges and Gaps

AI education faces several challenges that stem from the diverse nature of the field, the varied backgrounds of learners, and the evolving landscape of information dissemination.

The rapid evolution of AI is a primary challenge, but a lack of standardisation, diverse audience backgrounds, and the spread of misinformation have also hampered the public’s ability to understand the technology.

The highest concerns surrounding AI are privacy, transparency and security. This is supported by Future Processing’s research, with respondents aged 50 and above ranking security and data privacy as their highest concerns when using AI, followed closely by misinformation and question misinterpretation.

But, through ongoing collaboration and dialogue, governments can establish standards which foster diversity and inclusivity, provide up-to-date resources, and emphasise practical application to deliver more effective and equitable AI education.

The Role of Ethical Guidelines

With AI frequently coming under the spotlight, technology companies are increasingly recognising the importance of ethical guidelines and policies in AI development and usage.

Many have published official documents outlining their ethical principles and values concerning AI. These documents typically cover commitments to fairness, transparency, accountability, privacy, and avoiding biases in AI systems. For example, Google and Microsoft’s respective AI Principles are publicly available documents that articulate the companies’ ethical commitments.

Learning to Embrace

Continuous efforts in AI consumer education are imperative to navigate the evolving landscape of AI, bridge educational gaps, address ethical considerations, and ensure that individuals are equipped with the knowledge and skills needed in an increasingly AI-driven world. The commitment to transparency, inclusivity, and ethical practices will contribute to building a more informed and responsible AI community, meaning we can embrace all it can bring to the table, rather than dwelling on its shortcomings.

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Business

Conflicting with compliance: How the finance sector is struggling to implement GenAI

By James Sherlow, Systems Engineering Director, EMEA, for Cequence Security

GenerativeAI has multiple applications in the finance sector from product development to customer relations to marketing and sales. In fact, McKinsey estimates that GenAI has the potential to improve operating profits in the finance sector by between 9-15% and in the banking sector, productivity gains could be between 3-5% of annual revenues. It suggests AI tools could be used to boost customer liaison with AI integrated through APIs to give real-time recommendations either autonomously or via CSRs, to inform decision making and expedite day-to-day tasks for employees, and to decrease risk by monitoring for fraud or elevated instances of risk.

However, McKinsey also warns of inhibitors to adoption in the sector. These include the level of regulation applicable to different processes, which is fairly low with respect to customer relations but high for credit risk scoring, for example, and the data used, some of is in the public domain but some of which comprises personally identifiable information (PII) which is highly sensitive. If these issues can be overcome, the analyst estimates GenAI could more than double the application of expertise to decision making, planning and creative tasks from 25% without to 56%.

Hamstrung by regulations

Clearly the business use cases are there but unlike other sectors, finance is currently being hamstrung by regulations that have yet to catch up with the AI revolution. Unlike in the EU which approved the AI Act in March, the UK has no plans to regulate the technology. Instead, it intends to promote guidelines. The UK Financial Authorities comprising the Bank of England, PRA, and FCA have been canvassing the market on what these should look like since October 2022, publishing the results (FS2/23 – AI and Machine Learning) a year later which showed a strong demand for harmonisation with the likes of the AI Act as well as NIST’s AI Risk Management Framework.

Right now, this means financial providers find themselves in regulatory limbo. If we look at cyber security, for instance, firms are being presented with GenAI-enabled solutions that can assist them with incident detection and response but they’re not able to utilise that functionality because it contravenes compliance requirements. Decision-making processes are a key example as these must be made by a human, tracked and audited and, while the decision-making capabilities of GenAI may be on a par, accountability in remains a grey area. Consequently, many firms are erring on the side of caution and are choosing to deactivate AI functionality within their security solutions.

In fact, a recent EY report found one in five financial services leaders did not think their organisation was well-positioned to take advantage of the potential benefits. Much will depend on how easily the technology can be integrated into existing frameworks, although the GenAI and the Banking on AI: Financial Services Harnesses Generative AI for Security and Service report cautions this may take three to five years. That’s a long time in the world of GenAI, which has already come a long way since it burst on to the market 18 months ago.

Malicious AI

The danger is that while the sector drags its heels, threat actors will show no such qualms and will be quick to capitalise on the technology to launch attacks. FS2/23 makes the point that GenAI could see an increase in money laundering and fraud through the use of deep fakes, for instance, and sophisticated phishing campaigns. We’re still in the learning phase but as the months tick by the expectation is that we can expect to see high-volume self-learning attacks by the end of the year. These will be on an unprecedented scale because GenAI will lower the technological barrier to entry, enabling new threat actors to enter the fray.

Simply blocking attacks will no longer be a sufficient form of defence because GenAI will quickly regroup or pivot the attack automatically without the need to employ additional resource. If we look at how APIs, which are intrinsic to customer services and open banking for instance, are currently protected, the emphasis has been on detection and blocking but going forward we can expect deceptive response to play a far greater role. This frustrates and exhausts the resources of the attacker, making the attacks cost-prohibitive to sustain.

So how should the sector look to embrace AI given the current state of regulatory flux? As with any digital transformation project, there needs to be oversight of how AI will be used within the business, with a working group tasked to develop an AI framework. In addition to NIST, there are a number of security standards that can help here such as ISO 22989, ISO 23053, ISO 23984 and ISO 42001 and the oversight framework set out in DORA (Digital Operational Resilience Act) for third party providers. The framework should encompass the tools the firm has with AI functionality, their possible application in terms of use cases, and the risks associated with these, as well as how it will mitigate any areas of high risk.

Taking a proactive approach makes far more sense than suspending the use of AI which effectively places firms at the mercy of adversaries who will be quick to take advantage of the technology. These are tumultuous times and we can certainly expect AI to rewrite the rulebook when it comes to attack and defence. But firms must get to grips with how they can integrate the technology rather than electing to switch it off and continue as usual.

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Recognising the value of protecting intellectual property early builds strong foundation for innovators

Innovation Manager at InnoScot Health, Fiona Schaefer analyses an essential facet of developing ideas into innovations

Helping the NHS to innovate remains a key priority during this period of recovery and reform. Even within the current cash-strapped climate, there is the opportunity to maximise the first-hand experience of the healthcare workforce and its knowledge of where new ideas are needed most.

Entrepreneurial-minded, creative staff from any discipline or activity are often best placed to recognise areas for improvement – the reason why a significant number of solutions come from, and are best developed with, health and social care staff.

NHS Scotland is a powerful driver of innovation, but to truly harness the opportunities which new ideas offer for development and commercialisation, the knowledge and intellectual property (IP) underpinning them needs to be protected. That vital know-how and other intangible assets – holding appropriate contracts for example – are key from an early stage.

Medical devices can take years to develop and gain regulatory approval, so from the outset of an idea’s development – and before revenue is generated – filing for IP protection and having confidentiality agreements in place are ways to start creating valuable assets. This is especially important when applying for patent protection because that option is only available when ideas have not been discussed or presented to external parties prior to application.

Without taking that critical initial step to protect IP, anyone – without your permission – could copy the idea, so anything of worth should be protected as soon as possible, making for a clear competitive advantage and ownership in the same sense as possessing physical property.

The common theme is that to be successful – and ultimately support the commercialisation of ideas that will improve patient care and outcomes – the idea must be novel, better, quicker, or more efficient than existing options. Furthermore, to turn it into a sound proposition worth investing in, it must also be technically and financially feasible. It isn’t enough to just be new and novel – the best innovations offer tangible benefits to patient outcomes and staff working practices.

Of course, even more so in the current climate of financial constraints, the key question of ‘Who will pay for your new product or service?’ needs to be considered up front as well.

Whilst development of a strong IP portfolio requires investment and dedicated expertise, when done well and at the appropriate time, then it is resource well spent, offering a level of security whilst developing an asset which can be built upon and traded. There are various ways commercialisation can progress and whilst not all efforts will be successful, intellectual property is an asset which can be licensed or sold to others offering a range of opportunities to secure a good return.

In my experience, however, many organisations including the NHS are still missing the opportunity to recognise and protect their knowledge assets and intellectual property early in the innovation pathway. This is partly due to lack of understanding – sometimes one aspect is carefully protected, whilst another is entirely neglected. In other cases, the desire to accelerate to the next stage of product development means such important foundational steps are not given the attention required for long-term success.

Good IP management goes beyond formally protecting the knowledge assets associated with a project, e.g. by patenting or design registration, however. When considered with other intangible assets such as access to datasets, clinical trial results, standard operating procedures, quality management systems, and regulatory approvals, it is the combination which will be key to success.

Early securing of IP protection or recognition of IP rights in a collaboration agreement, demonstrates foresight and business acumen. Later on, it can significantly boost negotiating power with a licensing partner or build investor confidence.

Conversely, omissions in IP protection or suitable contracts can be damaging, potentially derailing years of product development and exposing organisations to legal challenges and other risks. Failing to protect a promising idea can also mean commercial opportunities are missed, thus leading to your IP being undervalued.

Ideas are evaluated by formal NHS Scotland partner InnoScot Health in the same way whether they are big or small, a product, service, or new, innovative approach to a care pathway.

We encourage and enable all 160,000 NHS Scotland staff, regardless of role or location, to come forward with their ideas, giving them the advice and support they need to maximise their potential benefits.

Protecting the IP rights of the health service is one of the cornerstones of InnoScot Health’s service offering. In fact, to date we have protected over 255 NHS Scotland innovations. Recently these have included design registration and trademarks for the SARUS® hood and trademarks for SCRAM®, building and protecting a recognised range of bags with innovative, intuitive layouts. Spin outs such as Aurum Biosciences meanwhile have patents underpinning their novel therapeutics and diagnostics.

We assist in managing this IP to ensure a return on investment for the health service. Any revenue generated from commercialising ideas and innovations from healthcare professionals is shared with the innovators and the health board through our agreements with them and the revenue sharing scheme detailed in health board IP and innovation policies.

Fundamentally, we believe that it is vital to harness the value of expertise and creativity of staff with a well-considered approach to protecting IP and knowledge input to projects from the start.

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Time is running out: NHS and their digital evolution journey

By Nej Gakenyi, CEO and Founder of GRM Digital

Many businesses have embarked on their digital evolution journey, transforming their technology offerings to upgrade their digital services in an effective and user-friendly way. Whilst this might be very successful for smaller and newer businesses, but for large corporations with long-standing legacy infrastructure, what does this mean? Recently the UK government pledged £6bn of new funding for the NHS, and the impact this funding and investment could have if executed properly, could revolutionise the UK public healthcare sector.

The NHS has always been a leader in terms of technology for medical purposes but where it has fallen down is in the streamlining of patient data, information and needs, which can lead to a breakdown in trust and the faith that the healthcare system is not a robust one. Therefore, the primary objective of additional funding must be to implement advanced data and digital technologies, to improve the digital health of the NHS and the overall health of the UK population, as well as revitalise both management efficiency and working practices.

Providing digital care

Digitalisation falls into two categories when it comes to the NHS – digitising traditionally ‘physical’ services like offering remote appointments and keeping electronic paper records, and a greater reliance on more innovative approaches driven by advances in technology. It is common knowledge that electronic services differ in GP practices across the country; and to have a drastically good or bad experience which is solely dependent on a geographical lottery contradicts the very purpose of offering an overarching healthcare provision to society at large.

By streamlining services and investing in proper infrastructure, a level playing field can be created which is vital when it comes to patients accessing both the care they need and their own personal history of appointments, GP interactions, diagnoses and medications. Through this approach, the NHS focus on creating world-leading care, provision of that care and potentially see waiting lists decrease due to the effective diagnosis and management enabled by slick and efficient technology.

This is especially important when looking at personalisedhealth support and developing a system that enables patients to receive care wherever they are and helps them monitor and manage long-term health conditions independently. This, alongside ensuring that technology and data collection supports improvements in both individual and population-level patient care, can only serve to streamline NHS efforts and create positive outcomes for both the patient and workforce.

Revolutionising patient experiences

A robust level of trust is critical to guaranteeing the success of any business or provision. If technology fails, so does the faith the customer or consumer has in the technology being designed to improve outcomes for them. An individual will always have some semblance of responsibility and ownership over their lives, well-being and health. Still, all of these key pillars can only stand strong when there is infrastructure in place to help drive positive results. Whilst there may be risks of excluding some groups of individuals with a digital-first approach, technology solutions can empower people to take control of their healthcare enabling the patient and NHS to work together. Tandem efforts between humans and technology

Technology must work in tandem with a workforce for it to be effective. This means the NHS workforce must be digitally savvy and have patient-centred care at the front and centre of all operations. Alongside any digital transformation the NHS adopts to improve patient outcomes, comes the need to assess current and future capability and capacity challenges, and build a workforce with the right skills to help shape an NHS that is fit for purpose.

This is just the beginning. With more invtesement and funding being allocated for the NHS this is the starting point, but for NHS decision-makers to ensure real benefits for patients, more still needs to be done. Effective digital evolution holds the key. Once the NHS has fully harnessed the poer of new and evolving technologies to change patient experiences throught the UK, with consistent communication and care, this will set the UK apart and will mark the NHS has a diriving example for accessible, digital healthcare.

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