An increasingly hot topic in Artificial Intelligence (AI) is human-centered AI.
What is Human-Centered AI?
As the name suggests, human-centered AI focuses on understanding the language, behavior, and emotions of people to bridge the gap between machines and humans, thereby shifting attention to the human aspects of technology.
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Principles of Human-Centered AI
Human-centered AI reflects an ethical approach to computing with a long-term goal of improving, augmenting, and enhancing humans, rather than replacing them with machines. A human-centered approach supports the creativity and innovation of people, while also reflecting human values in software applications.
Organizations use human-centered AI for planning, analyzing, and evaluating algorithms. Because a human-centered approach keeps humans in the loop while building AI, it’s easier to monitor for bias in computational decisions. This AI approach enables a checks and balances system wherein neither the human nor machine are fully autonomous, therefore making it easier to identify ways to make outcomes more inclusive.
It’s believed that this outlook in Artificial Intelligence development can lead to a safer future with human-centric machine learning. Among other things, this framework reduces privacy and security fears about AI technology, as well as concerns about increased unemployment due to automation and robotics.
Benefits of Human-Centered AI
Businesses are embracing this technology because of its ability to offer insight into customer behaviors. Understanding a customer’s habits and expectations can inform business strategies that build upon personal needs and aspirations. By applying analytics to human choices, AI technology identifies patterns. In turn, this data improves a company’s ability to create a personalized customer experience.
Human-centered AI augments humans by offering better strategies and solutions to overcome challenges through the combination of analytics and informed decision-making. It also can enable people to scale ideas by removing some of the computational work while still using the emotional and cognitive aspects of human behavior.
The end result is more successful products and services, as well as more rewarding user experiences. This AI approach can improve product design, as well as enable better experiences with gaming, social media, or other software applications.
What Is a Human in the Loop Approach?
A human-in-the-loop (HITL) approach combines human and machine intelligence to create machine learning modules. It’s a continuous process where humans are training, turning, and testing an algorithm, and then repeating as necessary. By providing a continuous feedback loop, HITL enables an algorithm to become more accurate.
If you’re interested in learning more about human-centered AI and how it can help your organization, contact InApp to learn more about our ground-breaking work in this field.
Frequently Asked Questions
Why is human-centered AI important?
A human-centered approach improves the ability to monitor for bias in algorithmic decisions. It essentially provides a checks and balances system wherein neither the human nor machine are fully autonomous, therefore making it easier to identify ways to make outcomes more inclusive.
What is human-centered machine learning?
Human-centered machine learning (HCML) is the development of adaptable and usable machine-learning systems based on human needs. Because a human-centered approach keeps humans in the loop, it’s easier to monitor for bias in algorithmic decisions.
Who is the human in human-centered artificial intelligence?
The human in human-centered artificial intelligence is the user or users. These systems are continuously improving because a human provides input and feedback to design applications that are reliable, safe, and trustworthy.
What does a human-centered machine learning designer do?
A human-centered machine learning designer works to create technology-based programs, applications, and devices that ultimately solve the issues experienced by people using the technology.
