Importance and Benefits of Human Centered AI
Human-Centered AI promises a plethora of benefits. It promises more ethics, accuracy, and data to business owners so they can understand their customers better and meet individual customers’ needs with AI that can cater to them.
Most Artificial Intelligence algorithms used in businesses today don’t collect, organize and analyze data in ways that are useful for their employees. With a human-first approach, as designed in human-centered AI, the algorithm recognizes humans as the only users of the products and services and keeps them as the focal point of the design. The User experience, research, and design principles acquired in Human Centered AI help designers identify ways in which biased data may have influenced other AI agents’ behavior or response. And Human-Centered AI is important because it allows us, humans, to become more involved in how AI is implemented.
Humans are central in AI training data (the process of creating datasets to teach machine learning algorithms) because they have the natural ability to learn, adapt and perform. Because humans can learn and adapt based on their own personal experiences, they are a good source of data that can teach machine learning algorithms.
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The relationship between human-centered AI and human-computer interaction
HCI, or Human-Computer Interaction, is the field of study that focuses on optimizing how users and computers interact. This includes developing strategies to make interactions more human-centered. HCI has been around for a while, dating back to the 1980s when personal computing was on the rise. With the advent of the internet and the explosion of mobile and diversified technologies such as voice-based AI systems and the Internet of Things (IoT), computing became omnipresent and omnipotent. Consequently, the need for developing a tool that would make such man-machine interactions more human-like grew significantly. This established HCI as a technology, bringing different fields such as cognitive engineering, linguistics, neuroscience, and others under its realm. Today, Human factors engineering is the application of human sciences and engineering principles to the design and evaluation of computer systems and products. Human factors engineers work to ensure that products and systems are safe, comfortable, and easy to use.
How To Implement Human-Centered AI
Implementing human-centered AI consists of four main areas of focus to implement the technology in a way that is most beneficial to its users. They are:
- Understand the User Experience – Conduct research first to understand the user experience and data that is relevant to those interactions. This can be accomplished through administering surveys, focus groups, and conducting customer interviews. Then take this information and use it to design AI applications based on insights acquired.
- Design AI Applications – Create solutions that help users accomplish their tasks. These solutions can be built into any platform or application, such as apps, websites, ads, or mobile devices.
- Implement AI Solutions – Deploy these solutions across multiple touchpoints so users can interact with them easily when they need assistance completing tasks or getting more information about products/services they’re interested in purchasing.
- Test & Measure Results – Test how well these solutions work by building a prototype to monitor how many times users interact with them, what they use them for, and find out if they’re satisfied with their interaction experience.
Use Case Of Human Centered AI
Human-centered AI can apply to a wide variety of AI applications and forms of data. It can be used to optimize a chatbot’s language to make it more accessible to a wider range of users or used to identify data that is relevant to humans.
One example of this is using natural language processing (NLP) to improve a chatbot’s language. A chatbot is a computer program that simulates a human-to-human online conversation. And a chatbot’s language can sometimes be difficult for people to understand, especially those who have language barriers or who use assistive technologies. You can use NLP to improve a chatbot’s language by taking a sample of language that is both easy to understand and relevant to the task the chatbot is designed to complete. Then take the sampled language and train the chatbot to use a similar language. This helps make the chatbot become more accessible. You can also use NLP to let a chatbot understand natural language so the chatbot responds to questions and comments in ways that are both easy to understand and relevant to the conversation.
Human Centered AI Used In Businesses
Human-centered AI benefits businesses the most, when business owners use it to make informed decisions, become reliable, apply principled behavior, scale, and predict future aspects of products and services.
This is seen within some of the following industries:
Healthcare
In Healthcare, Human-Centered AI brings newer and more efficiency to the lives of patients, doctors, and other healthcare professionals. The algorithm evaluates healthcare data in many forms to organize data and identify insights and patterns. It can also identify new drug applications. One example of this was seen with the new drug for the treatment of COVID-19. The data was used to organize photographs, clinical research trials, and medical claims.
Human Resources
Many organizations deal with being called out for biases during their hiring process. Human centered AI can be designed to eliminate biases by using only job-related data in applications to initiate hiring decisions. This way hiring staff prioritizes diversity and extinguishes a more inclusive hiring process within the company.
Auto Industry
Since the introduction of autonomous cars, there has been a lot of improvement in how cars are driven. Artificial intelligence created self-driving cars that use sensors to acquire data about their surrounding environment. There will be a lot more of this in the years to come.
Education
AI plays a critical role in teaching and online class experiences. Way before the onset of the pandemic, remote learning was introduced but it became more popular ever since. Human-Centered AI has helped teachers and lecturers build courses, assess student performances, save time and automate tasks. It can even be tailored to meet the needs of each individual student.
Cybersecurity
Basic AI technologies can already detect network intrusion, fraud, hacks, user authentication, and spam filtration. Human Centered AI systems allow for the detection of attacks ahead of time way before they even enter the system.
Renewable Energy
Human centered artificial intelligence is needed to utilize renewable energy. It helps manage the decentralized grids, as well as balance, store, and optimize electricity demands and supply in real time. For example, gas turbines have sensors attached that are linked to AI data processing systems that gather information to adjust fuel valves and ensure low emissions levels. Human-Centered AI even has the ability to make better decisions on reducing environmental impact.
Pros of Human Centered AI
Human-centered AI is designed to improve human user experience by taking into account factors such as personality, context, and expectations. Automated AI is designed to solve specific tasks without human intervention or oversight. That is why the goal of human-centered AI is not to replace humans entirely but to enhance our capabilities with intelligent, human-informed technology.
This is demonstrated in a variety of ways, including:
Human Impact
Human Centered AI can make a big impact on us improving our health and well-being by having systems that monitor our physical activity and stress levels, allowing us to better manage our health by making better food and exercise decisions on a daily basis. It also reduces our work stress by relieving us of repetitive or mundane tasks.
Improve Operational Efficiency
Human centered AI can assist in streamlining business operations and increasing efficiency. One example is AI predicting the future and forecasting events based on historical data. Google, for example, uses AI to forecast flu outbreaks and traffic jams by analyzing search queries and traffic patterns in various countries.
This type of advanced human-designed technology enables us to expand processes and information on a larger scale without compromising data integrity or increasing spending on human resources.
Cons of Human Centered AI
Human-centered AI advanced technology is beneficial but it does pose some risks. The most concerning risks are misuse of technology, loss of jobs, and a negative impact on human capabilities.
Misuse
A recent survey found that half of Americans don’t trust companies with their personal information including credit card numbers and social security numbers. They believe that these companies will use this information for marketing purposes without considering their privacy rights in return for free services or discounts on products or services.
Another form of misuse is from hackers and criminals who want to steal money or break into systems such as those of banks or hospitals. In 2017 an AI virus called WannaCry attacked computers around the world and locked down their data while requesting a ransom of $300 in Bitcoin cryptocurrency.
Job Loss
There are a few experts that claim that AI will take over most of the tasks that humans perform in their day-to-day lives, which will result in a massive unemployment rate.
Human Impact
Algorithms that are designed without consideration for the human nature of their users, algorithms can lead to poor user behavior. People can find themselves dependent on a machine that fails them when they need it the most if they allow their abilities to deteriorate or don’t actively seek out learning opportunities.
You can see this with online shopping. When someone shop online they spend more money than they need to because the system recommends products that replicate past purchases, instead of showing them deals or coupons they would find if they were shopping in a physical store. This is why people believe that advanced AI technologies have the potential to make people lazy and less productive.
Another addictive issue stems from cell phone usage. Social media platforms are designed and called out to have an addictive nature. This has been linked to people experiencing feelings of unhappiness or “fear of missing out” in compulsive users, which largely affects a person’s work, home life, health, and mental well-being.
To avoid these issues, you should be caref
\]ul when designing AI functionality based on human needs, wants, and desires.
History of Human Centered AI
Artificial Intelligence can be traced back to the 1950s when computer scientists were inspired by the work of Alan Turing, who proposed a test that would evaluate whether a machine could exhibit some level of intelligence. The Turing Test is still used today to evaluate whether machines can be considered sentient. Whilst some people believe that Large Language Models have already passed the Turing test, the consensus at the time of writing is that LLMs such as GPT-4 are very close, but have not yet consistently reached a human level of ‘apearing to be human’.
One of the earliest applications of AI was when computers were used to help doctors diagnose patients. IBM developed an AI system called Watson that could perform medical diagnoses by analyzing patient information and comparing it to a large database of medical records. Watson was able to outperform experienced doctors by correctly diagnosing approximately 80% of patients with certain illnesses. It was so successful that it eventually became the inspiration for other AI systems in healthcare.
Another application was IBM’s Deep Blue, which beat chess champion, Garry Kasparov, in 1997. Deep Blue was able to navigate through a maze and identify patterns more quickly than humans could by using neural networks and sophisticated algorithms.
However, it wasn’t until the 1980s that AI really took off. This was largely due to the advent of expert systems, which mimicked human behavior by learning from examples and making decisions on the fly without human intervention. And in 1995, digital assistants like Siri arose and their goal was to improve our ability to interact with computers and help us accomplish tasks that automated systems couldn’t handle on their own.
Today, AI is all around us. We have self-driving vehicles, smart home appliances, and chatbots that provide customer service on social media platforms like Facebook Messenger.
Future Outlook On Human Centered AI
Human-Centered AI technology has the ability to learn, adapt and improve itself based on data. Its applications have an impact on society because it’s designed to be aware of its surroundings, and make decisions better than humans. This allows many people communication-wise, including the elderly, to stay in touch with friends, family, and people with disabilities, such as those on the autism spectrum, who are unable to get humans to recognize and respond to their emotions and needs. AI improves communication by simulating human interaction with technology.
While human-centered AI is often seen as the future of AI there are still significant challenges to be overcome before this type of AI can become mainstream. One major challenge for human-centered AI is understanding natural language. Some people are better at understanding language than others, so there’s no way for an AI system to know whether you’re being sarcastic or making a serious point without context.
Another challenge is dealing with situations where humans need to override an automated assistant and take control of it. Cases, where this can happen, are with self-driving cars or auto-performing surgery. As we get closer to human-centered AI becoming a reality, these challenges will likely become fewer and easier to overcome.
Detailed Historical Context
The history of Artificial Intelligence (AI) is a rich tapestry of innovation and exploration, spanning decades of research and development. From the early musings of Alan Turing to the sophisticated machine learning models of today, AI has evolved significantly over the years.
Early Beginnings
In 1950, Alan Turing proposed the Turing Test, a method to assess a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. This foundational concept sparked a wave of interest in AI, leading to the development of the first AI programs in the 1950s and 1960s. These early systems were designed to solve problems by simulating human reasoning and learning.
The Rise of Expert Systems
The 1980s saw the emergence of expert systems, which mimicked human behavior by learning from examples and making decisions on the fly without human intervention. These systems were pivotal in demonstrating AI’s potential to perform complex tasks, laying the groundwork for future advancements.
The Advent of Machine Learning
The 1990s and 2000s witnessed the rise of machine learning, a subset of AI that focuses on developing algorithms capable of learning from data. This period saw significant breakthroughs, including the development of neural networks and the application of AI in various fields such as healthcare and finance.
Modern AI
Today, AI is ubiquitous, influencing every aspect of our lives. From virtual assistants like Siri and Alexa to self-driving cars and personalized recommendation systems, AI has become an integral part of our daily experiences. The advent of deep learning, a subset of machine learning, has further accelerated AI’s capabilities, enabling it to tackle complex tasks with unprecedented accuracy.
Ethics and AI Governance
As AI continues to permeate every aspect of our lives, ethical considerations and governance challenges have become increasingly important. Ensuring that AI systems are developed and deployed responsibly is crucial to preventing unintended consequences and fostering trust in AI technologies.
Ethical Frameworks
Several ethical frameworks have been proposed to guide AI development, including principles such as transparency, fairness, and accountability. These frameworks aim to ensure that AI systems are designed and used in ways that respect human values and rights.
Global Initiatives
International organizations and governments have launched various initiatives to address AI’s societal impacts. For example, the European Union’s AI Act aims to establish a regulatory framework for AI, while the United Nations has launched efforts to explore AI’s potential to achieve the Sustainable Development Goals.
Corporate Responsibility
Companies are also taking steps to address AI ethics, establishing internal guidelines and ethics boards to ensure that AI systems are developed and used responsibly. These efforts are crucial to building trust in AI technologies and ensuring that they benefit society as a whole.
Real World Examples
AI has been applied in various real-world contexts, transforming industries and improving lives.
Healthcare
AI has been used in healthcare to analyze medical images, predict patient outcomes, and develop personalized treatment plans. For example, AI-powered diagnostic tools have been shown to detect diseases such as cancer more accurately and earlier than human doctors.
Finance
AI has been applied in finance to detect fraud, predict market trends, and automate trading. AI-powered systems have also been used to provide personalized financial advice and manage investment portfolios.
Education
AI has been used in education to develop personalized learning plans, automate grading, and create virtual teaching assistants. AI-powered systems have also been used to analyze student data and identify areas where students need additional support.
Future of AI – Expert Opinions and Predictions
As AI continues to evolve, experts predict significant advancements in various fields.
Increased Automation
Experts predict that AI will continue to automate routine tasks, freeing humans to focus on more complex and creative tasks.
Improved Decision-Making
AI is expected to improve decision-making in various fields, including healthcare, finance, and education, by providing more accurate and data-driven insights.
Enhanced Human-AI Collaboration
Experts predict that AI will become increasingly integrated into human workflows, enabling humans and AI systems to collaborate more effectively and efficiently.
Career Opportunities and Education in AI
As AI continues to transform industries, new career opportunities are emerging.
Emerging Job Roles
New job roles such as AI engineer, data scientist, and AI ethicist are becoming increasingly important. These roles require a deep understanding of AI technologies and their applications.
Staying Relevant
To stay relevant in a job market increasingly shaped by AI, professionals need to develop skills that complement AI capabilities. This includes skills such as critical thinking, creativity, and emotional intelligence.
AI Education
Educational institutions are launching AI-focused programs to prepare students for careers in AI. These programs cover topics such as machine learning, natural language processing, and AI ethics.
Conclusion
Human-centered artificial intelligence is a branch of artificial intelligence that focuses on making machines learn from human behavior, rather than forcing humans to follow the instructions of pre-designed algorithms. Its use of human-centered principles is used to create AI functionality that reflects how humans prefer to interact with technology and this improves the experience overall of humans interacting with AI.
If you want to learn more about HCI, also read this interesting blogpost about Human Intelligence vs AI.
