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6 PRINCIPLES OF DESIGN: UX & AI GUIDE ⇣

Intro



As technology advances, users expect more personalized and intuitive
experiences, and AI is rapidly becoming an essential tool for designers to meet
this demand. This article will explore six key design principles that can assist
you in creating superior user experiences for digital products utilizing
Artificial Intelligence technology.

⌐■_■ To add more fun, some well-known UX advocates present these principles
instead.

pt 1

1. Align AI with Human Needs - presented by Michael Scott:





AI products must be designed to solve real-world problems by addressing genuine
human needs, rather than concentrating on developing systems that are similar
yet have limited or even nonexistent applications.

To be successful, machine learning needs to adopt a multi-disciplinary
perspective, encompassing not just technical aspects but also tackling social
systems challenges. An essential step in this process involves clarifying user
needs in advance. While machine learning models are designed to make predictions
based on patterns and relationships found in data, achieving accurate results
relies on numerous human-driven factors. Addressing user needs beforehand
ensures that these factors are appropriately accounted for, ultimately leading
to improved outcomes.

pt 2

2. Consider Ethical Implications - presented by SpongeBob:





It is crucial for designers and developers to consider the ethical implications
when creating AI products. Addressing issues such as bias, fairness, and
potential misuse is paramount for creating products that positively contribute
to society. By understanding AI's impact on human behavior and society,
designers can create products that respect core human values, avoid reinforcing
negative stereotypes or biases, and prevent undesirable consequences

pt 3

3. Clearly Establish Expectations - presented by David Attenborough:





Due to the vast range of AI capabilities within various products, users may have
unrealistic or misplaced expectations. It is crucial to clearly communicate the
AI's functionality and limitations in plain language. Adopting an approach of
under-promising and over-delivering can help build trust, and over time, users
will learn how to seamlessly integrate the AI into their workflows.

pt 4

4. Personalization with Consent - presented by Eric Cartman:





Personalization is a powerful tool in enhancing user experiences. AI products
often rely on personal data to deliver tailored recommendations and experiences.
However, it is vital to respect user privacy and comply with relevant privacy
and data protection laws. To achieve personalization with consent, AI products
should provide clear information on the data collection process, explain its
purpose, and offer users the ability to manage their data effectively. This
ensures higher trust in the AI product.

pt 5

5. Explainability - presented by Morgan Freeman:





Users need to be aware of AI-generated content as well as comprehend the
decision-making process behind it. Providing clear distinctions between
AI-generated content and offering explanations for AI decisions can help foster
user trust with the system. Explainability may be achieved through informative
indicators or brief descriptions that clarify AI-generated content and
illustrate how the AI arrived at specific conclusions or recommendations. This
approach empowers users with a sense of control and confidence while interacting
with AI products.

pt 6

6. Iterate with Real Data - presented by Eminem:





Working with real user data during early prototyping phases can help form
accurate assumptions for developing a well-functioning machine learning model.
It ensures that the resulting model accurately reflects the needs and behaviors
of the target users. In doing so, time, resources, and effort are saved,
resulting in a more efficient and effective development process for AI products.

Sources





Contact



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educational purposes only. Edgaras✌

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