What is Ethical Artificial Intelligence?

Published on
January 25, 2022
Jenna Gomez
Contributing Writer
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Artificial intelligence (AI) is one of the most interesting fields of technology today. We often hear stories of AI systems and machines performing human tasks such as designing buildings, drafting legal documents, and even making medical diagnoses. However, the future of artificial intelligence is something that has many people concerned. Some experts believe that there’s a 50% chance that high-level machine intelligence systems will arrive within 45 years, ready to outperform human beings at virtually every task.

Aside from job security, the ethical implications of artificial intelligence are also of concern to many. Pressing issues like discriminatory bias in facial recognition software have come to light already, while voice-controlled smart assistants are consistently under fire for privacy and data protection.

Enter ethical artificial intelligence.

What is Ethical Artificial Intelligence?

Ethical AI considers the full impact of AI usage on all stakeholders, such as customers, suppliers, employees, and society as a whole. It aims to be people-centric, fair, and responsible. Even if technology seems neutral, AI is only as equitable as the humans that program it, and the data that feeds into it.


As some of the earliest adopters, healthcare practitioners found that AI interfered with care management through racial and gender biases, failure to secure patient consent, and lack of protection towards patient privacy. With ethical AI, we can limit the impact of biases like those by hiring diverse AI teams, ensuring adequate representation for all users, and improving privacy and security measures around data usage.

Principles of Ethical AI

Given the potential impact of some of the coming artificial intelligence trends — including more robotics and neural networks — ethical AI must draw from a set of principles and moral codes to avert potential risks and damages. Technology companies need to emphasize fairness, transparency, and accountability to face growing pressure from regulators and the public. Integrating ethical AI into their processes from the start will help them attain greater success.

Admittedly this is a concept that's still being worked out to some degree. But it will largely come down to the human element of design. In the near future, we would expect education in software development to progress beyond study in programming, databases, cloud development, and the like to include creative strategy and ethical problem-solving. Through this mix of focuses and skills, designers working in AI in years to come will be trusted to program algorithms and design intelligence programs that do away with biases and take both broad and specific ethical concerns into account.

Regulatory Bodies Promoting Ethical AI

In one study on the public perception of AI, people were found to be generally distrustful of the technology — largely due to the way it has been portrayed over the years, and from the growing awareness that there is little oversight for it. Implementation of a transparent regulatory ecosystem, then, may be the only way that AI can be meaningfully held accountable to the public.

Globally, we are on the road toward AI regulation. All 194 member states of the United Nations’ Educational, Scientific, and Cultural Organization (UNESCO) have unanimously adopted a series of recommendations on ethical AI. These recommendations aim to realize the advantages of the technology, while reducing the human rights risks associated with its use. The European Commission likewise published a set of guidelines for the ethical development of AI, highlighting the need for consistent human oversight. Additionally, countries are beginning to establish institutions that better regulate AI technologies.

Using Synthetic Data Creation

One way to mitigate ethical concerns is to utilize synthetic data creation processes. Synthetic data is created manually or artificially apart from data generated by real-world events. Using various algorithms and tools, you can create vast amounts of well-balanced, alternative identities for a machine learning model. Aside from minimizing privacy risks, synthetic data creation also saves organizations from the costly and error-prone process of stripping personal information from collected data.

Viana™, a vision analytics solution by meldCX, creates ethical synthetic data by using a digital building block that cuts time and lowers costs, all while adhering to the aforementioned ethical AI principles. 

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