Responsible AI: What It Means and Why It Matters

Artificial intelligence (AI) has been a part of our daily lives for a while now. Every time we’ve used a grammar checker or looked at streaming service recommendations, we’ve benefited from AI. As ‌technology advances, we’re seeing it play a role in areas such as disease diagnosis, self-driving cars, and automated fraud detection in banks. Its rate of growth is staggering.

In all of this advancement, one major question remains: Is this increasing reliance on AI safe and responsible?

It’s such an important issue that you’ll often hear someone use the term ‘responsible AI’ every time the technology takes another leap forward. Developers need to regularly ask themselves whether their AI applications will make things easier for people and consider how someone might misuse them. It all comes down to making responsible decisions.

In this article, we’ll explain what responsible AI is, why it matters in a business context (with some real-world applications), and how we might implement and enforce responsible AI practices.

Responsible artificial intelligence (AI) is the commitment to developing and using AI applications in a fair and ethical manner and ensuring that they benefit individuals. This is important for businesses and their customers.

Businesses are increasingly turning to AI to help them provide a positive customer experience and build a loyal following. But top-notch service isn’t the only thing customers are looking for — and the more businesses use AI, the more customers expect them to be responsible and trustworthy .

If a business isn’t prioritising building trust, a growing number of customers will think twice about engaging with it at all.

You’ll often hear people using the terms ‘responsible AI’ and ‘ethical AI’ (or ‘AI ethics’) interchangeably. However, while the two terms are related, there are important distinctions between them.

Essentially, responsible AI deals with the ‘how’ of an application, and ethical AI refers to the ‘why’. Any responsible AI system that’s intended to solve a problem will follow an ethical AI framework during its development. Basically, developers should be asking themselves whether creating a specific AI service or software complies with standard ethical principles.

Let’s look at what responsible AI and ethical AI focus on:

Ethical considerations should inform the development of any AI application, particularly now that AI is so versatile and powerful. If developers don’t take ethics into consideration and actively participate in establishing standards of use, there’s little to stop a business from using AI in underhanded ways that may help them gain an unfair advantage over competitors or exploit their customers.

Responsible AI is important for other reasons, and there are a few key areas where it will have a positive impact:

• Risk assessment and mitigation: Businesses and AI developers should proactively identify and address potential negative consequences like bias, privacy violations, security vulnerabilities, and unintended societal impacts.
• Regulatory compliance: AI developers should help organisations navigate and adhere to evolving national and international AI-related laws and regulations.
• Benefits and innovation: With safety mechanisms in place, AI developers are free to unlock AI’s transformative potential more safely and ethically, leading to sustainable competitive advantages and positive social impacts.

We’re still developing an understanding of where AI can take us, so it’s vital to establish trust in artificial intelligence now. Many people are skeptical of AI and how organisations and individuals are using it. If it seems businesses are looking to use AI in an unethical manner, it will only breed distrust.

Establishing what's right and what is wrong when it comes to AI can get a little messy. After all, this is a relatively new technology that’s still in the process of development and refinement.

However, while there’s no universal, definitive list of core principles of AI, it’s safe to assume that they would focus on these categories:

• Fairness and inclusiveness
• Transparency and explainability
• Accountability
• Reliability and safety
• Privacy and security
• Sustainability
• Lawfulness

Let’s take a closer look at each.

AI should be accessible to everyone. While people can develop biases or learn to discriminate against others, there’s no reason a computer would unless a developer actively gives it this ability. This type of conduct should be strictly prohibited.

Example: An automated loan application system should only make a decision based on factual financial data rather than on other demographic data such as race, gender, or age.

Businesses can uphold this principle by making sure that a team with diverse backgrounds carries out the initial development and training of the AI system. This can help ensure that ‌ datasets are interpreted in a fair and inclusive manner.

AI development is complex. Most of us probably aren’t very interested in understanding how Netflix generates our recommendations. We just want the suggestions to be personalised to our tastes.

Nevertheless, it’s vital that developers provide transparent information about how an AI application works, how it was trained, what its intended purpose is, and where any potential areas of misuse may exist. Members of the public should have full access to how an AI model comes to its decision.

Example: A person who gets turned down for a job by a company that uses an AI-screening system should be entitled to know the internal mechanisms and algorithms that the system used to decide that they weren’t a suitable candidate.

AI developers should not only be responsible for their work during the development phase of an application, but they should also be held accountable if anything goes wrong with the application once it goes live or is misused in any way.

While one of the main goals of AI is to automate certain decisions and actions, maintaining human oversight of the application is necessary.

Example: If an AI-image generation tool is used to create offensive imagery, the developer should be expected to make the necessary adjustments to keep this from happening again.

AI tools are increasingly performing manual tasks like data entry, largely because they’re expected to do the work faster and without mistakes. It’s essential that these applications have a high level of reliability, as errors can have a negative impact on the businesses using them. An AI application should never be made available until it can consistently demonstrate this reliability.

AI-driven tools should also be able to demonstrate the ability to achieve guaranteed safety standards, which is equally important for accountability.

Example: Many factories rely on automated tools instead of manual labour, so they need assurances that these tools are safe to operate at all times.

AI programs rely on vast sets of data during the training process. Once a developer or programmer is in possession of these datasets, they must be able to provide high levels of protection and security measures, such as data governance, encryption, and anonymisation.

Example: There have been times when someone has obtained and used this data without the subject’s consent. This is a violation of their privacy. Data should only be used if an individual has already given their consent.

AI already uses a vast amount of resources in terms of data centres and chipsets, which consume large quantities of energy. To ensure alignment with environmental expectations and obligations, AI infrastructure needs to be ethically sourced and used in a sustainable manner.

Example: Sustainability is important for our future, which is why Salesforce has made an active commitment to initiatives that champion this undertaking. Our Sustainable AI Policy Principles are designed to guide AI regulation and climate innovation, and we’re proud to offer support to five nonprofits who are intent on developing climate-friendly, AI-driven solutions.

Above all, responsible AI applications must adhere to existing laws, both national and international. And if an AI application is delving into new areas that aren’t currently governed or don’t have statutory frameworks or standards, then governments and policymakers must work together to make sure any new implementation is strictly regulated.

Example: AI companies are already enmeshed (and have a tarnished reputation) for using copyrighted material in the training of AI models without consent or compensation. Going forward, it is essential to rebuild trust by establishing and adhering to fair, legal guidelines that not only protect copyrighted material and intellectual property but also prevent the creation of ‘substantially similar’ outputs.

As we mentioned earlier, there’s no official list of responsible AI principles, and there are no hard and fast rules about when they should be implemented. For now, businesses must determine how to apply these principles themselves.

So, if you’re in the process of implementing or developing responsible AI tools and applications, consider asking yourself the following questions. They’ll help you decide whether the principles we’ve discussed will apply to your situation.

• Will I use the AI system that I’m developing or implementing to make decisions?
• Will these decisions have an impact on people, society, or the environment?
• Am I unsure about what the impacts might be?
• Am I unsure whether it uses copyrighted material or intellectual property?

If you answered ‘yes’ to any of these questions, you might need to take a look at the follow-up questions:

• Is the AI system I’m using or developing designed to be fair and inclusive to everyone?
• Can I explain in detail how it works?
• Is there accountability in the event of its misuse?
• Can I prove it’s reliable?
• Do I have consent to use first-party data?
• Are there robust security measures for its use in place?
• Is it being used in a sustainable manner?
• Does it comply with existing AI laws and regulations?
• Does the AI system I’m using or developing protect against the misuse or theft of copyrighted material or intellectual property?

If your answer is ‘no’ to any of the above, you’ll have a clearer understanding of which area (or principle) you might need to work on further.

Now that you have a good understanding of the principles of responsible AI, let’s take a look at some of the real-world practical examples where businesses can apply these principles:

At Salesforce, we’ve made responsible AI a core part of our functionality. Whenever you use one of our AI-driven tools or features, you can feel confident that your data is securely protected at all times. If your business requires AI generative content, all LLMs (either our own or one that’s part of our Shared Trust Boundary) forget the prompt and output as soon as the output is processed for maximum security.

AI is constantly changing and evolving, and legislation and guidance on how to make sure your use of AI is ethically sound will continue to evolve over the next few years. However, there are five steps you can take to implement responsible AI into your business:

1. Establish a robust governance framework. Everything is more official and transparent when it’s in writing. Ensure all relevant stakeholders collaborate to create a set of ethical AI principles that will guide the future application of AI. Be sure to assign clear roles and responsibilities.
2. Embed responsible AI research into the AI lifecycle. Whenever you have a recommendation for a new AI application for the business, consider conducting an ethical impact assessment to make sure it adheres to your principles. Ensure that you maintain a human-centred design in its initial training and creation.
3. Apply consistent monitoring and feedback: Maintain accountability for your AI applications with regular feedback and evaluation intervals. Collect user feedback forms to refine performance.
4. Conduct in-depth training: A policy only works if all parties in a business understand it. As part of your responsible AI framework development, make sure that your entire workforce is trained in the implementation and expectations of your AI applications.
5. Align with relevant legal compliance: Although Australia doesn’t currently have statutory AI regulations, you should still check to see whether your practices and applications need to comply with broader international legislation such as the EU AI Act or General Data Protection Regulations (GDPR) .

There’s no question that AI is here to stay, and its application is only going to reach deeper across all sectors. Nearly all businesses, no matter the industry, will soon be using AI tools and software to streamline their business operations, increase revenue and drive lead generation.

With that in mind, it’s impossible to overestimate the importance of responsible AI. AI is a phenomenon, but it has to be used appropriately if we want to get to a place where all businesses can reap the benefits.

At Salesforce, we’re already helping hundreds of customers maximise the impact of responsible AI, thanks largely to our Einstein trust layer. It’s our main defence against negative AI practices. It delivers top-of-the-range privacy and security features and can help you build an AI ecosystem for your business in an ethical and sustainable manner.

Ready to begin your responsible AI journey? Contact us today, and we’ll get in touch with you quickly.