What Is Responsible AI?

As artificial intelligence continues to emerge as a catalyst for growth and economic output in nearly all industries, responsible AI is a developing set of principles to help our systems and organizations think critically about overcoming the challenges inherent in this technology. According to McKinsey, by 2030, 70 percent of companies will be using some form of AI technology [1]. The firm estimates that AI could add $13 trillion to the world economy by 2030 [1], while Gartner projects the whole of the AI software market could be worth $134.8 billion by 2025 [2].

Artificial intelligence is here to stay, but what are the challenges of using AI, and how can we overcome them in a way that is safe and ethical? Use this article to 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.

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 brain works. 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.

Key principles of responsible AI

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: Safe 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 problems.

• 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 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.

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.

Careers in responsible artificial intelligence

According to McKinsey’s research, the economic impact of AI is just beginning, and AI's “contribution to growth may be three or more times higher by 2030 than it is over the next five years” [1]. 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

Average annual salary in the US: $127,269 [3]

Job outlook (projected growth from 2022 to 2032): 25 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 on a team to improve existing systems, if not build programs from the ground up.

AI research scientist

Average annual salary in the US: $131,087 [5]

Job outlook (projected growth from 2022 to 2032): 23 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

Average annual salary in the US: $114,426 [7]

Job outlook (projected growth from 2022 to 2032): 25 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. 

Learn more with Coursera.

To learn more, consider Introduction to Responsible AI offered by Google Cloud Training on Coursera. This one-hour course is designed to help you understand how company decisions affect the end product of an AI algorithm and why the principles of responsible AI are important.