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HUMAN

CENTERED

AI

Human-centered AI focus on designing effective experiences between humans and algorithms.

I bring focus to the human factors when engineers are distracted by technological achievements.
HCAI = Technology + Human + Ethics

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TO AI OR NOT TO AI?

WHY YOUR BUSINESS

NEEDS HCAI

While AI has improved our lives and work, it may also harm us if not appropriately designed. Bypassing human-centered design factors can bring big risks to your end-users and business.
What is

Human-centered AI (HCAI) refers to the development of AI systems that are designed to enhance human capabilities and empower people to achieve their goals. This approach focuses on delivering human-centered, reliable, safe, and trustworthy AI systems. HCAI rises from the implications produced by the enhanced complexity of designing AI-based services and it process is becoming increasingly important as AI systems are being used in a wide range of applications, from healthcare to finance to transportation, and as the potential impacts of these systems on society become more apparent.

How it works

There are several key principles that should be considered in the design of any AI system. The first principle leverages the interdisciplinary nature of HCD and applies them to AI-based services to understand and guide how designers identify opportunities to enhance and support their work while maintaining a human-centered approach aligned with user needs and values. Another principle is explainability, which refers to the ability of the system to provide clear explanations for its decisions and actions. Explainable AI (XAI) is becoming an important area of research, as it helps to build trust and accountability for the system.

Human-centered Design
// One-way - Users towards machines.
// Deterministic and expected outputs by following a fixed set of rules and parameters.
// Limited to a user-machine collaboration tool.
// Users are who initiate actions, the machine is passive.
Human-centered AI
// Two-ways - Between users and machines.
// Non-deterministic and unexpected outputs.
// Can evolve and play a dual role of an assistive tool plus
collaborative relationship.
// Machines can initiate actions depending on the intelligence level of the AI.
My approach

The design process for AI-based solutions requires a much deeper focus on research to meet users' needs and expectations that adapt to technology. The HCAI framework promotes the strategic approach that is to leverages the strength of both human and machine intelligence so that we can build a hybrid augmented intelligence to maximize the intelligence level of AI. Therefore, I follow a human-centered approach, I design digital solutions that bring value e to people and support business growth with a focus on AI product design.

The process

HCAI is rooted in the HCD approach, although, it's an enhanced version of its baseline principles to accommodate AI challenges and issues introduced by this technology.

The Double Diamond that we commonly use in our design process illustrates the nature of converging and diverging stages throughout the design process. But when it comes to design for AI, the current process often falls apart because user research has limitations in recognizing resources or opportunities for AI; synthesis may not identify actionable insights for AI; ideation methods they lack the capabilities to seek AI solutions; and evaluation may not effectively reduce the risk associated with AI-driven solutions as prototype may not simulate performance or the system's evolution.

Therefore, I follow a framework that highlights a mix of the traditional HCD approach informed by an AI design process:

Design Process & Skills

HCAI gives a competitive advantage to designers who are invested in emerging technologies. Designers because more active as QA in monitoring and prioritizing errors obtained from user testing. HCAI is going to be a valuable resource for businesses with mature UX research, with a well-coordinated & interdisciplinary product development lifecycle. Businesses cannot neglect HCAI otherwise the unintended consequences of AI features will creep their service quality and business growth.

1

User Research

HCAI investigates human interactions with intelligent machines. Designers are in a privileged place to advocate for strong research, and critical and analytical thinking to operationalize ethical principles around AI while delivering value to organizations.

2

Dataset management

It's important to have a good understanding of the data that will be used to train and evaluate the system and to pay attention to issues such as bias, accuracy, and fairness in the data. It's also important to have a rigorous testing and evaluation process in place to ensure that the system performs well and meets the intended goals. In summary, the best design process for AI systems is one that is human-centered, explainable, ethical, and data-driven, with a rigorous testing and evaluation process in place.

3

HUMAN-CENTERED AI

the HCAI approach places humans at the center of the development, ensuring that humans are the ultimate decision makers with AI systems.

01
02

HUMANS

We need to start from user needs and apply human-centered process and methods to develop AI-based solutions that are useful and explainable.

Technology

We need to integrate human roles and build more scalable and powerful intelligent automated solutions by taking the complementary advantages of human and machine intelligence collaboration. The design goals are to develop human controllable AI and augment human abilities instead of replacing them.

03

ETHICS

We must guarantee fairness, justice, privacy, and accountability through interdisciplinary approaches to design and develop ethical and responsible AI-based solutions.

04

Human + Technology + Design

The success of AI is ground on user engagement. Which in its turn relies on seamless cognitive augmentation that meets its goals and their trust in the system.
Trust is a relatively new design component in interactive intelligent systems and is especially important in building AI and ML solutions.

When it comes to the adoption and reliance on these technologies, trust is essential to support the relationship between the user and the system — even one breach of trust can highly influence user perception of that technology.

How to design for trust →

THE IMPACT

Problem definition and goal setting

Testing and Evaluation

Involves clearly defining the problem that the AI system is intended to solve, and setting specific goals and objectives for the project.

Align testing and evaluating of the AI system with design techniques such as user testing, simulation, and benchmarking, to ensure that it performs well and meets the intended goals.

Stakeholders engagement

Continual improvement

One important principle of human-centered design is to involve and engage with stakeholders, including end-users, to gather information about their needs and preferences, and to ensure that the system is aligned with their values and goals.

AI systems are non-deterministic by nature, so we must continuously improve the AI system and its experience based on user feedback, new research, and new data, to ensure that it remains aligned with human values and goals.

Ethical and societal impact assessment

Design and development

To do no harm, we have to assess the potential ethical and societal impacts of the AI system, such as potential biases and discrimination, and identify ways to mitigate these impacts by designing safety mechanisms.

Bring an HCD approach to the design and development of AI systems. It is important to ensure that the system is transparent, explainable, and accountable.

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