Responsible AI is a set of practices used to make sure artificial intelligence is developed and applied in an ethical and legal way. It involves considering the potential effects AI systems may have on users, society and the environment, taking steps to minimize any harms and prioritizing transparency and fairness when it comes to the ways AI is made and used.
What Is Responsible AI?
Responsible AI is a set of practices that ensures AI systems are designed, deployed and used in an ethical and legal way. When companies implement responsible AI, they minimize the potential for artificial intelligence to cause harm and make sure it benefits individuals, communities and society.
“The entire AI lifecycle — from the time you’re procuring data to the time you’re designing, developing, testing, to the time you’re putting it out in the market — there’s this entire layer of responsibility and accountability that is needed by businesses,” Navrina Singh, the founder and CEO of AI governance software provider Credo AI, told Built In. “And they need to hold themselves accountable.”
Accountability is a crucial component of responsible AI, as it ensures that developers, businesses and other stakeholders are held to certain ethical standards when it comes to the design, development and use of AI artificial intelligence. It removes any ambiguity about where responsibility lies if something goes wrong with an AI system, and incentivizes the ethical and fair use of AI in society.
Why Is Responsible AI Important?
Responsible AI is meant to address data privacy, bias and lack of explainability, which represent the “big three” concerns of ethical AI, according to Reid Blackman, AI consultant and author of Ethical Machines.
Data, which AI models rely on, is sometimes scraped from the internet with no permission or attribution. Other times it is the proprietary information of a specific company. Either way, it is important that AI systems gather, store and use this data in a way that is both compliant with existing data privacy laws, and safe from any kind of cybersecurity threat.
Then there’s the issue of bias. AI models are built on a foundation of data, and if that foundation has prejudiced, distorted or incomplete information, the outputs generated will reflect that and even magnify it.
And there may not even be a clear explanation how or why an AI model is working in a particular way. These algorithms operate on immensely complex mathematical patterns — too complex for even experts to understand — which can make it difficult to understand why a model generated a particular output.
“This technology is very, very, very powerful,” Ravit Dotan, an AI ethics advisor, researcher and speaker, told Built In. It works faster and at a much larger scale than any human is capable of working on their own. So, “when something goes wrong, it goes wrong at scale,” she continued. “With all of this power does come all of this responsibility.”
“With all of this power does come all of this responsibility.”
And now, as automation continues to disrupt virtually every business across all industries — affecting the way we live, work and create — the stakes are even higher. If an AI recruiting tool is consistently biased against women, people of color or people with disabilities, that could affect the livelihood of thousands or even millions of people. Or if a company somehow violates a data privacy law, people’s personal information is in danger, not to mention all the fines the company will have to deal with.
“The kind of damages that can happen societally are really extensive. And they can happen inadvertently, which is why it’s really important for everyone who’s involved with AI to be careful,” Dotan said. “It really requires a lot of attention to what people are doing when they’re developing these tools, investing in them, buying them, using them.”
Responsible AI can help to mitigate those damages. It provides a framework on which companies can build and use more safe, trustworthy and fair AI products — allowing them to take advantage of all the benefits of artificial intelligence, responsibly.
Responsible AI vs. Ethical AI
Responsible AI is an overarching approach that guides well-intentioned AI development. Ethical AI, on the other hand, is a “subset of responsible AI,” Blackman told Built In. Ethical AI falls under the greater umbrella of responsible AI practices.
To specify further, responsible AI focuses on the development and use of artificial intelligence in a way that considers its potential impact on individuals, communities and society as a whole. This involves not just ethics, but also fairness, transparency and accountability as a way to minimize harm.
Ethical AI, by contrast, focuses specifically on the ethics, moral implications and considerations of artificial intelligence. It addresses ethic-based aspects of AI development and use, including bias, discrimination and its impact on human rights, ensuring that it is used in responsible ways.
A responsible AI framework essentially breaks down how to “not ethically fuck up using AI,” Blackman said. “You also throw in regulatory compliance, cybersecurity, engineering excellence. Responsible AI is just all of those things.”
How to Implement Responsible AI
Implementing a responsible AI framework requires a systematic and comprehensive approach, starting with education. Everyone in the organization, from the C-suite to the HR department, needs to understand the basics of how AI works, how their company uses it and the risks that come with it.
Companies also need to establish a clear vision for how they want to approach AI responsibly, outlining their own principles to guide how AI will be developed and deployed. Though responsible AI implementation will look different for every company, these policies should generally address ethical considerations, data privacy concerns, approaches to transparency and accountability measures — all of which should align with relevant legal and regulatory requirements, as well as the organization’s own values and goals.
As examples, here’s how some of the biggest names in tech are implementing responsible AI into their everyday operations.
Create Company AI Principles and Goals
To ensure that its AI systems are built responsibly, Microsoft follows a self-developed playbook known as the Microsoft Responsible AI Standard. This document outlines the company’s AI principles and goals, plus provides guidance for how and when to apply them. Goals spanning across the principles of accountability, transparency and more are written in detail and help steer responsible AI development at Microsoft.
Establish an AI Ethics Committee
Sometimes, expert insight is needed to make informed responsible AI decisions, which is where a designated AI committee or advisory board can be helpful for an organization. IBM leverages this idea by employing its internal AI Ethics Board, which is comprised of various stakeholders across the company. On the board, members participate in review and decision-making processes related to IBM’s policies, practices and services, all to make sure they align with the company’s ethical values and support a culture of responsible AI.
Consider Ethics at Every Step of Development
Google recognizes that ethical decisions need to be considered at every stage of the AI development process, from product ideation to launch. As such, this ideology is reflected in the company’s responsible AI practices. These practices emphasize the importance of human-centered design from the start, raw data examination before system input as well as continuous testing and monitoring of AI software even past deployment, especially for machine learning systems.
Responsible AI Principles
For now, the implementation of any kind of responsible AI framework is entirely up to the discretion of the data scientists and software developers who make it. As a result, the steps required to prevent discrimination, ensure compliance, foster transparency and instill accountability vary from company to company.
That said, there are guiding principles organizations can follow when they implement responsible AI:
AI systems should be built to avoid bias and discrimination. And they should not perpetuate or exacerbate any existing equity issues in the world. Instead, they should treat users of all demographics fairly, regardless of race, gender, socioeconomic background or any other factor.
Accomplishing this requires AI developers to make certain that all the data used to train algorithms is diverse and representative of the real-world population. It also entails that developers remove any discriminatory patterns or outliers that may negatively impact an AI model's performance. They should also regularly test and audit their AI products to make sure they remain fair after their initial deployment.
AI systems should be understandable and explainable to both the people who make them and the people who are affected by them. The inner-workings of how and why they came to a particular decision or generated a particular output should be transparent, including how the data used to train an AI system is collected, stored and used.
Of course, this isn’t always possible. Sometimes AI models are just too big and complex for even experts to fully understand. But companies can choose to work with models that are inherently more transparent and explainable, such as decision trees or linear regression, which provide clear rules or logic that can be easily understood by humans.
They can also design their user interfaces to present outputs in a more clear way. The use of visualizations, like saliency maps, can help users understand which parts of the input data influenced the model’s output the most. And natural language can help break down the model’s results in a more digestible way.
Transparency means documentation too. It can be spelled out in disclosures, which are specifically meant to illustrate the exact steps a company took to build its AI. Companies can also create their own dashboards to keep track of the AI products they use and any regulatory or financial risk that they come with.
3. Privacy and Security
Protecting the privacy of individuals is good practice, and in many cases it’s the law. Companies should handle any personal data they use to train their models appropriately, respecting any existing privacy regulations and ensuring that it is safe from theft or misuse.
This typically requires a data governance framework, or a set of internal standards that an organization follows to ensure its data is accurate, usable, secure and available to the right people and under the right circumstances. Companies can also anonymize or aggregate any sensitive data to better protect it, which involves removing or encrypting personally identifiable information from the datasets used for training.
4. Inclusive Collaboration
Every AI system should be designed with the oversight of a team of humans that are just as diverse as the general population — with varied perspectives, backgrounds and experiences. Business leaders and experts in ethics, social sciences and other subject matters should be included in the process just as much as data scientists and AI engineers to ensure the product is inclusive and responsive to the needs of all everyone.
A diverse team can foster creativity and encourage innovative thinking when solving all the complex problems associated with AI development. A diverse team also has a higher likelihood of identifying and addressing any biases in a model that may have otherwise gone unnoticed. And it can also encourage more conversations about the ethical implications and social impact of a given AI product, thus promoting a more responsible and socially conscious AI development process.
Organizations developing and deploying AI systems should take responsibility for their actions, and they should have mechanisms in place to address and rectify any negative consequences or harms caused by AI products they either made or used.
As of now, there are very few formal avenues for accountability when an AI system goes wrong. Violating data privacy legislation like the California Consumer Privacy Act and the EU’s General Data Protection Regulation can lead to some hefty fines. And there are several anti-discriminatory laws already on the books that the U.S. Federal Trade Commission has said apply to AI. But there are no regulations pertaining specifically to artificial intelligence yet.
Accountability doesn’t only happen in a courtroom though. Companies are also beholden to their investors and customers, who can play a crucial role in upholding responsible AI.
“When people talk about AI responsibility, typically they will talk about the responsibility of the tech companies that develop the AI. But I think about it as a more broad, societal responsibility. Because the stakes are really high,” AI ethics advisor Dotan said. “Companies that develop AI, yes, they’re responsible. But everyone else who supports them also shares in the responsibility. Investors, insurance companies, buyers and also end users. It’s really something that involves everyone.”
Benefits of Responsible AI
Building a responsible AI framework is a lot of work, and it can be difficult to measure and demonstrate whether an AI model is performing well from a responsibility standpoint. But when it’s done well, responsible AI has a lot of benefits.
Responsible AI fosters privacy and security, which can help ensure that companies stay within the bounds of the law when it comes to the collection, storage and usage of data.
And with politicians, human rights organizations and tech innovators alike calling for even more explicit AI regulations, there are likely more laws to come. In 2022, the EU proposed a bill that would give private citizens and companies the right to sue for financial damages if they were harmed by an AI system, holding developers legally accountable for their AI models. In the United States, the White House announced an AI Bill of Rights in 2022 and issued an executive order in 2023, sounding the alarm for potentially more federal oversight over the making of AI products in the future.
Improves the Quality of the AI Product
When an AI product is unbiased, the quality of its outputs are often better. And when it is developed in a transparent way, its outputs can only continue to get better. For example, if a company implements explainability into its hiring algorithm to tell applicants why the model made a decision about them, the company now also understands why the algorithm made that decision — meaning it can make the necessary changes and adjustments to ensure the algorithm is as fair as it can be.
“It’s a competitive advantage to do AI responsibly,” Dotan said. “You just know your product better, so you’re able to fix it, or improve it.”
Good for Brand Reputation
When a company’s brand and AI products are tied to words like “responsible,” “transparent” and “ethical,” it can do wonders for their reputation. Those words elicit trust from users, investors and employees alike.
“The word ‘responsibility’ is very grounding because you’re saying ‘I’m going to do something, I’m responsible for it.’ And then, whether they do it or not, it will determine if I trust you,” Credo AI’s Singh said.
“The word ‘responsibility’ is very grounding because you’re saying, ‘I’m going to do something, I’m responsible for it.”
This is particularly important in a world full of AI-related scandals. Companies like Meta and Clearview AI have been hit with massive fines for violating data privacy laws around the world. And tech giants like Google, Amazon and Microsoft have caught public ire for developing AI products shown to be racist and sexist. Now, consumers are demanding more transparent and equitable artificial intelligence, and companies are following suit.
“Everyone is reaching this ‘Holy shit!’ moment of ‘Oh my god, we better get our act together.’ Singh said. “I truly believe CEO and C-level executives now need to have this new line item around responsibility.”
Good for Society
Artificial intelligence made and used responsibly could actually be good for society too. AI facilitates efficiency, adaptation and augmentation, all with the click of a button. And while that power can have heavy ethical and legal implications, it can also be harnessed to do real good in the world.
A 2020 research paper determined that, of the 134 targets the United Nations laid out in its Agenda for Sustainable Development to solve issues like world hunger and climate change, 79 percent could be significantly aided by the use of AI — particularly as it relates to the economy and the environment.
Done right, AI can solve some of society’s problems, instead of just magnifying them.
“Doing AI responsibly means huge environmental and societal payoffs for humanity, without exaggerating,” Dotan said. “If we actually take those tools and think about the good we can do with them, we can actually seriously address some of the biggest problems we’re facing as humanity. So there’s a lot of promise in [AI] for society at large.”
Frequently Asked Questions
What is responsible AI?
Responsible AI is the practice of developing and applying AI in an ethical, legal and well-intentioned manner.
What are the 4 principles of responsible AI?
Four principles used to build and apply responsible AI include:
- Privacy and security
- Inclusive collaboration