What Is Responsible AI? Principles, Benefits, and Best Practices for Ethical AI Development

The latest McKinsey Global Survey on AI found that 65% of respondents regularly use generative AI in their organizations—nearly double the percentage from 10 months ago. Given this rapid growth, it raises the question: Can oversight possibly keep up?

In October 2023, the White House published an executive order intended to make AI safer and more reliable. The order reflects a growing awareness of AI’s possible ramifications, as well as the eagerness of governing bodies to address these issues.

However, while these regulations are a step in the right direction, not everyone is convinced. In BRG’s Global AI Regulation Report, just over half of respondents (57%) expect effective AI policy in the next 3 years. However, only 36% believe these regulations will provide the necessary guidelines.

As AI evolves and transforms, leaders doubt the legislature’s ability to keep pace.

What is Responsible AI?

As governments struggle to implement inclusive AI policies, organizations are left to build their own guardrails. Responsible AI (RAI) frameworks offer a starting point for these policies. RAI includes practices that improve AI management, emphasizing principles governing AI design and usage. It outlines models to mitigate harm to individuals, the environment, and society.

“The common goal of anybody involved in responsible AI is minimizing those negative impacts while maximizing your opportunities,” says Noël Luke, the Chief Assurance Officer at TrustArc. TrustArc is a leading firm offering compliance solutions, trust-building certifications, and data governance services.

Responsible AI frameworks provide guardrails for companies and facilitate improved trust with clients and stakeholders alike. “AI is often described as a black box,” says Luke. “Adhering to these [responsible AI] principles helps you to build trust so people understand the system you're using and what factors into it.”

Why is Responsible AI Use Important? 

Responsible AI is a vital countermeasure to the ethical drawbacks of AI systems, especially concerning automated decision-making. When left unchecked, some AI models have historically displayed biases and output inaccurate or irrelevant information. Without proper checks, these problems are seldom detected until it's too late to correct them.

Air Canada's chatbot is an excellent example of AI misinformation. In 2022, Jake Moffatt contacted Air Canada with a question about bereavement fares. Moffat had lost a loved one and was informed by the chatbot that he qualified for a discount on his fare. The bot instructed him to pay full price and to apply for a partial refund within 90 days.

Unfortunately, this refund wasn't in Air Canada's policy, and the company argued that they weren’t responsible for advice from the bot. Moffatt took Air Canada to court. Though Air Canada claimed its chatbot was a “separate legal entity,”  the court ruled in the customer’s favor. The ruling establishes a clear precedent: corporations are fully accountable for misinformation generated by their AI models.

Given AI’s increased reach, however, the potential for damage is no longer limited to controversy or small claims court. When healthcare organizations leverage emerging AI technologies, the stakes become a matter of life or death. AI-powered algorithms promise profound advancements in the field, including one instance of significantly reduced false positives and false negatives in the early detection of lung cancer—but what happens when these models don’t fire correctly?

A misaligned model could generate false alarms or miss critical symptoms; consumers are keenly aware of this risk. Six in ten Americans state that they would feel uncomfortable with their healthcare provider relying on AI to help care for them. 

Responsible AI puts checks and balances in place to reduce these risks, protecting the company and its users. In doing so, companies improve their systems and strengthen their relationship with their consumers. 

The Principles of Responsible AI

Responsible AI revolves around principles meant to guide the ethical development and deployment of the service. According to IBM, these principles include empathy, bias control, transparency, and accountability, which create a framework that protects companies and their users. 

The pillars of RAI can be divided into governance, transparency, fairness, and sustainability practices.

Governance

AI governance refers to the standards, processes, and oversight businesses have in place to ensure responsible AI in their systems. “At every point in the AI life cycle, there's an opportunity to make a mistake or cause harm,” says Luke. Without proper governance procedures, harm cannot be mitigated.

Flaws and bias can always slip through the cracks, and it’s up to companies to ensure their AI models remain fair and ethical. While the level of governance a company will need depends on its size, the complexity of its AI systems, and other factors, there are a few general strategies businesses can implement.

Many businesses now have ethics boards or committees that oversee AI services, ensuring they meet legal and ethical standards. By creating internal governance frameworks with clear policies and assigning specific responsibilities to their board members, companies foster a stronger sense of accountability. These committee members can also engage stakeholders by explaining how the AI works, its applications, and its potential pros and cons.

Some companies also supplement their boards with automated monitoring and audit trails. While having a human in the loop is necessary, this continuous monitoring helps companies ensure the system functions as intended and strengthens data governance and security. 

Transparency

Data privacy is a sensitive subject, with many AI users growing increasingly anxious about how it handles their personal information. 70% of Americans claim they have little to no trust in companies to make responsible decisions about how they use AI in their products, and 81% say the information companies collect will be used in ways they aren't comfortable with.

Although users can protect themselves and their data by reviewing terms and conditions, responsible AI necessitates company transparency about their models.

Businesses have options around transparency. A popular avenue is labeling all AI-generated products and having a well-defined privacy policy. This privacy policy should illustrate how the company uses user data, whether the AI is trained on user interactions, and, if so, allow users the choice to opt-out. 

For a company to be truly transparent, however, it must understand what information its AI draws from, why it makes specific outcomes, and how the algorithms work. Deep learning models often struggle with a “black box problem,” which adds a layer of difficulty in explaining why AI makes certain decisions.

By building a custom AI solution, companies can decide what information their model pulls from, providing insight that avoids the black box. 

Fairness

AI is a helpful tool for streamlining tasks and picking up on patterns humans might have missed. However, models trained on biased and inaccurate information offer biased and inaccurate results, leading to disastrous outcomes.

An extreme example of this is Rite Aid’s facial recognition controversy. From 2012 to 2020, Rite Aid used an AI-based facial recognition technology to identify potential shoplifters. Unfortunately, instead of flagging customers with a criminal background, the technology falsely identified many women and people of color as “likely” shoplifters. These false positives caused Rite Aid workers to approach innocent customers, demand they leave the store, and even call the police. 

Other examples of AI’s bias throughout the last decade include Google’s advertising system showing high-paying job ads to men instead of women or Amazon’s ML-based hiring algorithm penalizing CVs that included the word “women.” These cases show a troubling pattern of discrimination in AI’s data and algorithms. If not addressed, such flaws foster mistrust and cause serious harm to marginalized groups.

Tackling this bias is easier said than done. The National Institute of Standards and Technology (NIST) mentions that “institutional and societal factors are significant sources of AI bias” and “successfully meeting this challenge will require taking all forms of bias into account.” Section 508 suggests that companies counter this by training their models on diverse data sources, creating development teams with diverse backgrounds, and conducting thorough user research, among other strategies. 

Sustainability

A significant concern regarding AI is its environmental impact. A single request made through ChatGPT uses 10 times the electricity of a Google Search, and AI-related infrastructure may soon consume 6 times more water than the entire population of Denmark. These and other consequences could quickly add up in an age where climate change is already a concern. 

Responsible AI, fortunately, takes this threat into account. Companies can combine two key approaches for sustainable AI use: AI for sustainability and sustainable AI. 

AI for sustainability entails the use of AI tools to cut down on carbon emissions and better implement climate adaptation strategies. For instance, a company could use AI to track emissions across the supply chain, optimize fleet efficiency to reduce fuel usage, and determine how best to maximize revenue while reducing emissions. AI can even forecast impending climate and environmental threats, allowing companies to predict and plan around ecological dangers. 

Sustainable AI, meanwhile, seeks to reduce the impact AI systems have on the environment. While the United Nations has taken the initiative on this with the launch of the AI Innovation Grand Challenge at the 2023 climate summit, there are ways that individual companies can also reduce their ecological impact. These include using smaller, more specialized models that can be fine-tuned for specific tasks, and selecting hardware optimized for their project’s demands. 

Examples of Responsible AI Use

Many large corporations recognizing AI's potential are also aware of its risks. IBM, for example, launched an AI Ethics Council that reviews products and services to ensure they meet the brand’s Principles of Trust. The company is highly outspoken about responsible AI, having written extensively on the subject. 

Microsoft is another strong RAI supporter. They released a responsible AI transparency report earlier this year, revealing that 99% of their employees have undergone mandatory training for responsible AI practices. They also offer various tools to support responsible AI, such as a responsible AI dashboard and an impact assessment guide. ‍

Google, meanwhile, has implemented the “4Ms” to keep their machine learning under 15% of their total energy use: model, machine, mechanization, and map optimization. These practices involve heavy optimization and selecting efficient models and systems, compensating for the increased energy load Google sees each year.

Going Forward with Ethical AI

As more companies embrace and implement AI in their systems, more policies will be enacted to counter its possible damage. By factoring these principles into their systems, companies avoid potential legal troubles, build trust among their clients, and create stronger AI models that stay ahead of their competitors. 

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