The rise of artificial intelligence (AI) in the past few decades provoked new and unprecedented opportunities to improve people’s lives in every corner of the world. It’s applied to numerous eras, from education and healthcare to marketing or even human resources, transforming how humans solve problems at scale.
Frankly, AI doesn’t always bring positive impacts. Many companies still battle over the perceived risks associated with artificial intelligence. Not only do they have to ensure fairness and interpretability, but they also stay mindful of new regulations.
That’s where responsible AI comes in handy to help figure out issues and ensure your business is compliant.
In this article, we’ll define responsible AI as well as its importance. Then, we’ll go over the principles/practices of responsible AI and how you can implement them.
What Is Responsible AI?
There is no single definition of responsible AI. At its core, responsible AI practices seek to ensure that artificial intelligence (AI) technologies are ethically sound and socially responsible.
This includes ensuring that AI systems are designed and operated to minimize harm, protect privacy and security, and respect human rights. It allows companies to build trust in both customers and shareholders, at the same time scaling AL with confidence.
As AI technologies become increasingly ubiquitous and powerful, it is important to ensure that they are developed and used responsibly. Numerous reasons motivate businesses and organizations to build and follow responsible AI for artificial intelligence systems.
Responsible AI practices prevent harmful outcomes such as biased decision-making, invasion of privacy, and amplified social inequality. They also ensure that AI technologies are ethically sound and socially beneficial.
Responsible AI Principles
Here are four principles for responsible AI you can refer to.
Data bias proves a critical consideration when it comes to responsible AI. If one of your data sources is exposed to inherent bias, it affects the decision-making process directly, resulting in unfair outcomes for individuals, groups, or the entire organization.
Responsible AI gives your business a chance to stay aware of bias and potential bias so you can mitigate these issues.
To identify patterns and predict results accurately, AI systems are required to access a huge amount of data, including personal and sensitive data. It’s the organization’s responsibility to ensure the data used in AI models stays secured.
Security appears as one of the most significant factors of AI systems. It allows you to stop malware and hackers from attacking your systems and altering their intended behavior.
Organizations should be transparent about how their AI systems work and provide explanations for the decisions they make.
What Are Responsible AI Principle Practices?
We’ve gone over the definition of responsible AI and its four main principles. So how can your organization achieve them? Below are the best practices you can follow.
Practices to achieve fairness
To minimize the data bias, you need to check to see if the dataset to represents the population accurately. Besides, examining the dataset’s subpopulations is vital to verify if the model works equally well for every group.
Bear in mind to always consider fairness when designing models. It’s highly recommended to have consulting from subject matter specialists and social scientists.
What’s more, you must monitor the model regularly after deployment to guarantee that biases won’t happen over drifting time.
Practices to protect privacy
Before safeguarding data, you should evaluate and classify for sensitivity levels.
Once done, your business or organization needs policies to access and use this private data. Not all types of users have the right to edit your data. Just grant them only the level of access necessary to complete their tasks.
Plus, you can make use of privacy-enhancing technologies (PETs) to secure your model and data.
Practices to guarantee the security
There are multiple types of data, and several of them would be potential targets of attackers. So consider the incentives of hackers, for instance, breaking down your business, stealing customer data, or simply for fun.
Prevention is always better than cure. You can have a testing team working as an adversary to try to attack the system. This helps your organization identify vulnerability holes in your model/system and fix them beforehand.
Practices to ensure transparency
It’s crucial to interpret and explain why AI models produce specific results so businesses and users can be transparent and trust AI systems. To achieve that, never put a ton of datasets into the model. You should only take a small amount of data to perform a particular action only. Not only does it help determine the correlation or causation between variables with ease.
As mentioned above, you also need consulting from domain experts to understand the level of interpretability.
In short, responsible AI plays a key role in ensuring AI technologies are applied correctly. Implementing responsible AI practices can help address some of the challenges associated with AI, such as providing privacy and security.
The future of responsible AI will likely see continued growth in the adoption of responsible practices by organizations.