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.