What Is Human-Centered AI?

Human-centered AI (HCAI) is artificial intelligence that aims to augment and enhance existing human capabilities in business workflows. For enterprise AI success, AI systems should co-exist within a larger, human-based system. Human-centered AI learns from human input and collaboration, continuously improving while providing an effective experience. Instead of replacing humans, it preserves human control while respecting privacy. Human-centered AI operates transparently and delivers equitable outcomes to all users.

Traditional machine learning focuses on enhancing efficiency by automating tasks to a high degree. By using an ML algorithm, AI tools can automate specific tasks, reducing the need for human intervention.

Human-centered AI differs from this as it aims to enhance human processes by augmenting human capabilities. Instead of replacing humans in a process, human-centered AI aims to complement humans by improving what they can achieve within a set amount of time.

Human-centered AI follows four central principles.

Human-first development

Human-centered AI focuses on delivering utility to humans. It is a tool that’s developed by humans for humans. It prioritizes benefits to individual users and their rights and liberties over corporate profits. Where possible, human-centered AI seeks to help humans reach new levels of productivity by enhancing or simplifying tasks.

Accessibility

Human-centered AI applications are fully accessible to all, especially to those in society that need them most. For example, human-centered AI makes tasks easier to perform for people with disabilities or marginalized communities.

Transparency

Human-centered AI is fully transparent, with clear documentation showing how to use the AI, what it offers, and how it functions. This level of transparency creates a heightened sense of trust.

Security

Human-centered AI includes end-to-end privacy and data security protocols to keep human data safe. It prioritizes confidentiality and data safety. The end user can choose the level of access third parties have to their data.

Several benefits of human-centered AI exist for individuals and businesses.

Scalable workflows

AI and humans working together can use human creativity while drawing on AI's speed and accuracy. For example, a human writes three examples of ideas for a blog post they like. The human-centered AI generates ten other similar ideas, expanding the options humans can choose from without taking more effort.

Better decision-making

Humans and artificial intelligence models excel in different domains. Relying on each party for different tasks can create a highly efficient system. For example, humans have high empathy and can provide comprehensive insight and context to a problem. Artificial intelligence can pull on huge volumes of data to give rational input. Together, human-centered AI can help businesses make better decisions, as it equally uses AI rationale and human judgment.

Improved productivity

Human-centered AI collaboration radically improves the efficiency of certain processes. As these AI systems automate certain tasks and simplify others with intelligent assistance, they can increase the speed with which a human can complete a task. On one hand, this assistance allows humans to get more done in the same space of time. On the other, the simplification of tasks also means that humans can spend more time and cognitive effort on higher-level tasks that generate more profit.

Inclusion

Human-centered artificial intelligence creates a more inclusive society as AI systems accommodate people with different abilities. These models provide a simple and accessible way of interacting with systems and performing certain tasks. By implementing these models, businesses can create a sense of inclusion as people with disabilities can more easily complete certain tasks.

The principles of human-centered artificial intelligence define how it operates and what it offers humans.

Inclusivity and accessibility

At its core, human-centered AI aims to create systems that everyone can use, regardless of their abilities. These systems are intuitive and will help people with varying abilities to achieve more with AI assistance.

Equal use of automation and human input

Human-AI collaboration is at the center of HCAI systems. Instead of replacing humans, this AI system aims to create a balanced outcome by combining ML automation and human input.

Continuous feedback

Human-centric design utilizes continuous feedback to gather information on how to improve. Researchers supplement AI training data with human feedback to improve what the models can achieve alongside humans.

Understanding user needs

Human-centered AI understands human behavior, and human needs to create AI solutions. These models use human-computer interaction to help humans understand exact requirements and how AI systems can reach these goals.

Mitigating bias and ethics

Human-centric AI emphasizes ethical AI use, creating a model free from bias and following human ethics. By taking input from humans, these systems can center human values, creating better AI models in the future.

AI explainability

The transparency of an AI system and full explainability ensure that humans stay in the loop about how a model works. For example, when using a human-centric AI chatbot that uses natural language processing, you should be able to ask it how it got to a certain answer. If the model can respond and demonstrate its logical pathway, it shows AI explainability and fits into this system.

Human involvement in the design process

As a form of AI model that researchers build to help humans out, humans must always be a central part of the design process. Human resources like creativity and design knowledge will be central to the development process. At no point in the development cycle should human beings take a back seat.

There are four steps that you can follow to implement human-centered AI.

Identify user needs

The first step in creating any human-informed technology is to identify the pain points or issues that humans face. Ask for user feedback or conduct interviews with customers to identify problems. Be sure to consider a diverse range of opinions, surveying people from different backgrounds to understand the problems that your users currently face.

Identify the value AI brings

After identifying a potential problem humans experience, you can discuss whether or not there is an applicable solution. While jumping to an AI solution may seem natural, consider other non-ML options first. Other solutions may be more cost-effective and easier to maintain. You can also do a pros and cons balance sheet, working out whether the potential benefits outweigh the drawbacks.

Your team may believe that the problem is solvable with AI or has identified a similar problem that AI has previously solved. If this is the case, then question whether the AI system could cause harm. For example, it could create privacy issues or compromise a project's data integrity. If you identify any potential damage that AI could cause, it may not be the best solution.

Prototype solutions

After identifying a problem that is best solved with human-centered AI, you can begin prototyping solutions. Give users access to the prototypes so they can give you direct feedback and help you enhance the system for future launches.

Ask users to identify how they would improve or change the system to enhance its benefits. Collect feedback from different groups, creating an inclusive and diverse feedback collection system.

Test and improve

Extensively test and improve your system before launching it to the public or to its intended audience. Humans should oversee the improvement and refinement process, enhancing the likelihood of identifying problems and improving them. Continual improvement creates a secure, useful, and collaborative human-centric AI system.

At AWS, we are committed to developing AI responsibly and taking a human-centric approach that prioritizes education, science, and our customers. We offer a range of responsible AI tools and resources for human-centered AI development. For example,

• Model Evaluation on Amazon Bedrock helps customers evaluate, compare, and select the best FMs for their specific use case based on custom metrics, such as accuracy and safety.

• Amazon Augmented AI lets you implement a human review of ML predictions when human oversight is needed.

• Amazon SageMaker Ground Truth offers the most comprehensive set of human-in-the-loop capabilities for incorporating human feedback across the ML lifecycle to improve model accuracy and relevancy.

You can complete various human-in-the-loop tasks, from data generation and annotation to reward model generation, model review, and customization through a self-service or AWS managed offering.

Get started with human-centered AI on AWS by creating a free account today!