What Is Responsible AI?

Key takeaways

Responsible artificial intelligence (AI) is an emerging set of principles that help build ethical and trustworthy AI systems.

• According to the Stanford AI Index, 78 percent of organizations worldwide reported using AI in 2024, underscoring the need to develop responsible and trustworthy AI systems [1].

• Responsible AI follows the principles of inclusivity and fairness, safety and reliability, data privacy, and transparency.

• You can work with responsible AI systems in roles like machine learning engineer, AI research scientist, and software engineer.

Explore responsible AI and the practices that the United States government and companies like Microsoft and Google are putting into place to safeguard AI projects. If you’re ready to start building expertise in artificial intelligence, enroll in the IBM AI Foundations for Everyone Specialization. You’ll have the opportunity to learn the fundamentals of AI systems, including deep learning, prompt engineering, and responsible AI, in as little as four weeks. Upon completion, you’ll have earned a career certificate for your resume.

What is responsible AI?

Responsible AI is using artificial intelligence in an ethical and trustworthy way while acknowledging the impact AI can have on society and our individual human lives. Artificial intelligence mimics the way our brains work. Unfortunately, certain negative characteristics of our thinking patterns, such as hidden bias and bad decision-making, are also present. For example, an AI algorithm designed to process rental applications might show a bias toward renting properties to applicants of a specific race.

Recognizing that artificial intelligence can perpetrate these aspects of our society, many companies and organizations have released statements about how they will use and develop artificial intelligence technology ethically. Although these statements are different and put into practice differently at each organization, they share a few key principles.

Read more: AI Ethics: What It Is, Why It Matters, and More

Responsible AI principles 

Many companies and organizations developing or working with artificial intelligence are publishing a set of guidelines they intend to follow to show a commitment to responsible AI. Although any responsible AI statement's language and exact contents will vary, some emerging ideas include inclusivity, safety, data privacy, and transparency.

• Inclusive and fair: Inclusive and fair artificial intelligence means AI engineers must find ways to remove unfair biases in training data. Responsibility in this area means development teams will need to actively seek out unfair biases to improve AI systems, such as those based on nationality or race, sexual orientation, income, ability, or beliefs.

• Safe and reliable: AI should strive to be safe for society. This principle asks AI developers to consider potential uses for AI that could hurt people, such as creating materials that deceive or spread falsehoods. To create safe AI, engineers and other developers will need to test AI programs before deployment to identify safety risks and find ways to mitigate those risks.

• Data privacy: Data privacy in responsible AI means being transparent with people about how their data will be used and giving users the ability to make decisions about what private data will be shared. In addition to giving consumers tools to manage their privacy, AI engineers and developers can take steps to safeguard user data to keep it safe from malicious agents.

• Transparency: For consumer protection, it’s important that companies and organizations are transparent about the ways they’re using artificial intelligence. Users and company stakeholders need to be able to understand how the AI works and how it draws the conclusions it does. Providing transparency also allows consumers to hold companies accountable for the ways they use AI.

Why is responsible AI important?

Responsible artificial intelligence benefits companies and consumers. For consumers, responsible AI can lead to a more just and fair system that operates with transparency and protects their data security. For organizations, responsible AI can help you reduce risk and protect your reputation as an ethical company.

Keeping values-based AI at the center of your operations can also help you guard against government regulations and plan for long-term success in an uncertain regulatory future. Across the world, governments are reacting to artificial intelligence by creating laws and regulations to force AI companies to adhere to ethical principles. By creating a plan for responsible AI now, you won’t have to make reactionary decisions as the laws change or tighten.

What is an example of responsible AI in real life?

An example of responsible AI use is in the finance sector, where responsible AI ensures fair and unbiased loan approval for individuals, regardless of their background. Using responsible AI to ensure that AI-recommended patient diagnoses are error-free and explainable is an example of responsible AI use in health care. Additionally, responsible AI frameworks, like IBM’s watsonx Orchestrate and H&M’s sustainable and responsible AI framework, are examples of real-life responsible AI implementations.

Challenges of responsible AI

While responsible AI is an important way for companies to consider their AI policies, you may also encounter challenges while implementing a responsible AI plan. Let’s take a look at some common responsible AI challenges and what you can do to overcome them.

• Difficult to measure: If you begin an ethical AI program at your organization, you’ll be watching a metric based on principles rather than solid, easy-to-access numbers like sales or clicks on a website. You will need to develop a system for understanding what changes you want to pursue and how you will measure whether your program is working.

• Transparently complex: For most end users, transparency isn’t why artificial intelligence is hard to understand; rather, it is because of computer science illiteracy. One challenge for using AI responsibly is balancing transparency in your process with the fact that the process is complicated to start with. Companies and government organizations will need to look for new ways to educate the wider audience about how algorithms work to cultivate a more informed consumer base.

• Reaching a wider audience: When all of the decision-makers in the room come from a similar perspective, such as from a technical background or only consulting senior-level employees, it can be difficult to spot problems that aren’t relevant to those individuals. You can overcome this challenge by developing a committee of individuals that represent a wider range of perspectives.

Responsible AI jobs

According to the Stanford AI Index, 78 percent of organizations worldwide reported using AI in 2024, with global private investment in genAI reaching $33.9 billion [1]. Additionally, a 2025 McKinsey report found that 92 percent of companies plan to increase their investments in genAI over the next three years [2]. If you want to position yourself to take advantage of this growing field, a few careers you could consider are machine learning engineer, AI research scientist, and software engineer.

Machine learning engineer

Median annual total salary in the US: $159,000 [3]

Job outlook (projected growth from 2024 to 2034): 15 percent [4]

Education requirements: You can become a machine learning engineer through a few different paths, including non-degree certification or a bachelor’s degree in computer science or a related field.

As a machine learning engineer, you will develop and create machine learning algorithms and programs designed to solve complex problems. You will build and train artificial intelligence to work with large volumes of data and learn on its own, as well as test software and correct bugs. In this role, you may work in a team to improve existing systems, if not build programs from the ground up.

AI research scientist

Median annual total salary in the US: $203,000 [5]

Job outlook (projected growth from 2024 to 2034): 20 percent [6]

Education requirements: You will typically need to earn a master’s degree in computer science or a related field to become an AI research scientist, although in some instances, you can enter the field with a bachelor’s degree, and in others, you may need to earn a doctorate.

As an artificial intelligence scientist, you will work to create AI-based solutions to complex problems. You can specialize in creating artificial intelligence algorithms and applying and adapting existing AI tech for specialized use, or you can specialize in collecting and understanding the data used to train AI. In this role, you may collaborate with other researchers and publish your findings in peer-reviewed journals.

Software engineer

Median annual total salary in the US: $148,000 [7]

Job outlook (projected growth from 2024 to 2034): 15 percent [4]

Education requirements: You may find that many software engineer positions ask for a bachelor’s degree in computer science or a related field. However, it is possible to enter the field with non-degree training programs. 

As a software engineer, you will work to create, design, and maintain software systems. You may develop software for specific, specialized purposes for your company or organization, or you may work on projects designed for broader consumer use. You may work with a team of other professionals, and you may choose from various projects, such as video games, network systems, software designed for business applications, or software for personal computers, among others. 

Resources to help you build career confidence

Want to keep your skills future-ready? Get the latest career insights by joining our LinkedIn newsletter, Career Chat! And if you want to keep exploring the field of artificial intelligence, check out these free resources: 

• Take the quiz: AI Career Quiz: Is It Right for You? Find Your Role

• Watch on YouTube: How Generative AI is Transforming Business Operations | Real Examples 

• Hear from an industry expert: How to Use GenAI in Your Job: Insight from Coursera’s Former CEO 

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