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.
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 Privacy
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 cybersecurity threats.
Algorithmic 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. If companies don’t take steps to root out biases in their AI models, AI bias could spread beyond corporate settings and worsen societal issues like unequal policing and housing discrimination.
“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,” Ravit Dotan, an AI ethics advisor, researcher and speaker, told Built In. “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.”
Lack of Explainability
AI algorithms operate on immensely complex mathematical patterns, which can make it difficult to understand why a model generated a particular output. This can have consequences in areas like finance and retail, where customers may rely on AI to inform their own decisions. If customers don’t trust AI-driven decisions and processes, companies in sectors like media could undermine their reputations in the face of general skepticism toward AI.
Responsible AI Principles
Developing and applying a responsible AI framework often falls on data scientists and software developers, so responsible AI varies from company to company. That said, there are guiding principles organizations can follow when they implement responsible AI:
1. Fairness
AI systems should be built to avoid bias and discrimination, treating users of all demographics fairly, regardless of race, gender, socioeconomic background or any other factor. AI developers must make sure that all AI training data is diverse and representative of the real-world population. They must also remove any discriminatory patterns or outliers that may negatively impact an AI model’s performance and regularly test and audit AI products to make sure they remain fair after their initial deployment.
2. Transparency
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. Companies should document the steps they take to build AI products. For example, an organization can create a dashboard to track the AI products it uses and any associated regulatory or financial risks.
While some AI models are too complex for even experts to understand, companies can work with models that are inherently more transparent and explainable, such as decision trees or linear regression. They can also design their user interfaces to present outputs in a more clear way.
3. Privacy and Security
When using any personal data to train AI models, companies should respect existing privacy regulations and ensure the data is safe from theft or misuse. This 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 under the right circumstances. Companies can also anonymize or aggregate sensitive data to better protect it, removing or encrypting personally identifiable information from training datasets.
4. Inclusive Collaboration
Every AI system should be designed with the oversight of a team of humans that reflects the diverse perspectives, backgrounds and experiences of the general population. 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 everyone.
A diverse team can foster creativity and encourage innovative thinking when solving complex problems associated with AI development, including 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, promoting a more responsible and socially conscious AI development process.
5. Accountability
Organizations developing and deploying AI systems should take responsibility for their actions and have mechanisms in place to address and rectify any negative consequences or harms caused by AI products they either made or used. While the California Consumer Privacy Act, the EU’s General Data Protection Regulation and several anti-discriminatory laws can apply to AI, there are not yet regulations pertaining specifically to artificial intelligence.
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.
“I think about it as a more broad, societal responsibility. Because the stakes are really high,” 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.”
Implementing Responsible AI
Implementing a responsible AI framework requires a systematic and comprehensive approach. Companies can get started by following these steps.
Establish Responsible AI Principles
Companies need to create a clear vision for how they want to approach AI responsibly, outlining principles to guide how AI will be developed and deployed. These policies can address ethical considerations, data privacy concerns, approaches to transparency and accountability measures — all of which must align with relevant legal and regulatory requirements, as well as the organization’s own values and goals.
Educate All Employees on Responsible AI
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 can organize company-wide and department-specific training sessions, so employees know how to use AI tools while avoiding common pitfalls.
Apply Responsible AI Practices Across the Development Process
Companies can execute responsible AI by following best practices at every stage of development. These include identifying biases in training data, using AI models that are more transparent than others, selecting metrics to gauge the performance of AI models and regularly testing AI models, even after they’ve been deployed. Taking these steps makes it easier to assess AI models and pinpoint errors and prejudices.
Support Extensive Human Oversight of AI Products
Problems with AI models are easier to catch when companies assemble teams with members of diverse backgrounds who are dedicated to studying AI models and addressing any issues. In addition, businesses can partner with outside organizations to adopt third-party responsible AI standards and evaluate whether their own AI products meet these expectations, as an additional layer of accountability.
Examples of Companies Practicing Responsible AI
Here’s how some of the biggest names in tech are implementing responsible AI into their everyday operations.
Microsoft
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.
IBM
Sometimes, expert insight is needed to make informed responsible AI decisions, which is where a designated AI committee or advisory board comes in. IBM leverages this idea by employing its internal AI Ethics Board, which is composed 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.
Google recognizes that ethical decisions need to be considered at every stage of the AI development process, and 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.
NVIDIA
NVIDIA lists transparency as one of the guiding principles behind its idea of “trustworthy AI,” and it’s applying this principle beyond the boundaries of its internal culture. The company helped found the Coalition for Secure AI (CoSAI), which encourages organizations to exchange responsible AI frameworks, methods and tools. As a member, NVIDIA allows itself to be held accountable to standards outside of its own framework, lending more sincerity and credibility to the company’s responsible AI efforts.
Benefits of Responsible AI
When implemented thoughtfully, responsible AI offers various advantages to companies that embrace its principles.
Ensures Compliance
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. The need for compliance has become more vital in the wake of the EU AI Act taking effect in 2024 and the White House potentially pushing for more AI legislation after announcing an AI Bill of Rights in 2022 and issuing an executive order in 2023.
There’s also an increased focus on how existing U.S. laws can be applied to AI, particularly as it relates to discrimination, defamation and copyright. Companies that don’t keep up with evolving compliance standards could face major legal repercussions later on.
Improves the Quality of the AI Product
When an AI product is unbiased and transparent, the quality of its outputs improves. 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. Businesses that live up to these principles can also stand out from companies that violate the latest data privacy laws, design problematic AI products and suffer from a resulting lack of trust and bad publicity.
“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,” Navrina Singh, the founder and CEO of AI governance software provider Credo AI, told Built In.
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, including furthering the UN’s sustainable development goals.
“Doing AI responsibly means huge environmental and societal payoffs for humanity,” 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.”
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.
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 ethics-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.”
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:
- Fairness
- Transparency
- Privacy and security
- Inclusive collaboration
