The importance of Human-Centered AI
Artificial intelligence has become a daily companion in our lives, but relying solely on traditional technology-driven approaches will not be enough to develop and implement this technology to have a positive impact on humanity truly.
Moving towards new concepts and approaches will be the key to improving the human experience, and this involves the concept of Human-Centered AI (HCAI). We explain what it is and how to apply it in your company.
What is Human-Centered AI?
Despite the increasing levels of automation that AI enables, the common thread running through all of its use cases is the human factor. Therefore, the long-term success of AI depends on recognizing that people are central to its design, operation, and use.
According to IBM, Human-Centered AI (HCAI) is an emerging discipline that seeks to create AI systems that amplify and augment, rather than displace, human capabilities. It seeks to preserve human control in a way that ensures AI meets our needs while operating transparently, delivering equitable outcomes, and respecting privacy.
This type of AI learns from human input and collaboration, allowing it to continuously improve while providing an efficient experience for both parties.
By developing AI that aims to understand human language, emotions, and behavior, HCAI ultimately aims to bridge the gap between machines and humans.
HCAI for companies
Looking at this approach from a business perspective, these solutions leverage human science and qualitatively coarse data to understand the needs, aspirations, and drivers that underlie customer behaviors in your market.
These advanced contextual analytics combine data and human science to provide specific behavioral insights. When this happens, patterns emerge that can be used to create personalized and enhanced customer experiences.
This can lead to the development of clear and informed business strategies from which companies can derive numerous benefits, such as:
- Informed decision-making: HCAI aims to improve our capabilities through informed intelligent technology, enabling businesses to make more informed decisions and develop clear strategies and solutions to challenges.
- Reliability and scalability: this AI takes our thinking skills and allows our ideas to scale to meet larger data needs. Cognitive input enables the expansion of processes and information on a larger scale without compromising data integrity or increasing human resource expenditure.
- Creating more successful software and products: by applying the principles of behavioral science to technology through HCAI, developers and product designers can leverage user behavior and subconscious patterns to build services and solutions that follow a more satisfying, informed, and enriching user journey.
Foundations for reliable and ethical AI
Creating functional and reliable AI requires professionals in the sector, as well as an approach that pays special attention to responsible and ethical AI. It is very important to apply a perspective that puts education and awareness of ethical aspects and responsible use of technology at the core.
At this point it is important to promote a deep understanding of the challenges and best practices, with a focus on legal issues and responsibility, as well as advice to customers, providing them with clear and accurate information on ethical issues and responsible use, guiding them in making decisions in their projects.
Therefore, to maintain human-centered AI, customers must be added to the feedback loop of the process, allowing the technology to be continuously optimized and adapted to new challenges.
The post-launch period of a project can be divided into 5 phases that support effective AI:
- Improvement: AI performance in the real world should be proactively monitored. Meanwhile, customers can provide feedback and recommendations on potential problems and opportunities for improvement.
- Analysis: Potential issues are analyzed to identify the causes of potential anomalies, assessing the impact on AI performance, accuracy of recommendations, etc.
- Review: The results of the analysis are reviewed and an implementation plan is agreed, where feedback is also reviewed and incorporated into the plan.
- Implementation: Updates are made to the model and new ones are implemented every few months to keep it up to date.
Monitoring: Once updates are made, AI performance is monitored to ensure that the changes are effective.
In short, the key will be to proactively involve people in the development and maintenance of AI so that it can be truly useful and concerns around it are minimized.
How to apply people-centred AI
Following the fundamental value that human+AI is a much better combination than either one individually, visualizations and user experiences can be developed that foster collaboration between the two parties.
Another option is to create frameworks for designing or evaluating models of human-AI interaction and to conduct theoretical work that develops and extends theories of human-AI collaboration or co-creation.
One of the biggest challenges for data scientists is to identify and analyze large and disparate datasets in ways that yield new insights to help solve complex problems. Using a human-centered approach, the first aim is to understand the attitudes that data scientists have when automating tasks. This will enable the creation of higher quality, faster, and less error-prone ML models.
At Plain Concepts we seek new forms of research that rigorously design ways of human-AI interactions and experiences that enhance and expand human capabilities. This will result in better products, happier customers, and a society with an improved quality of life.
We take an interdisciplinary approach that includes researchers specializing in this human-computer interaction, data visualization, design in the context of AI, security analysis, and more.
If you want to know more about what we do, feel free to contact us!
