The saying, “With great power comes great responsibility” is at least as old as the first century BC, and yet it still applies to today’s emerging technologies like AI.
While AI has the power to revolutionize how businesses operate, from creating efficiencies across the supply chain to reducing waste on the shop floor, if these capabilities go unchecked, the consequences could be devastating. So, how do we ensure AI and ML are exercised properly?
Enter: Responsible AI, a set of guidelines for organizations to utilize AI fairly.
What Is Responsible AI?
Responsible AI is a framework for a business to develop and implement AI ethically, so that it empowers your organization without displaying bias (whether intentional or not), negatively impacting customers or society, or breaking any laws.
Responsible AI involves:
- Using human-centric AI: Human-centric AI centers around augmenting human intelligence rather than replacing it. One of the major goals of human-centric AI is to improve workers’ understanding of how AI systems operate, which is key to creating responsible AI systems as well.
- Directly examining raw data: Examining raw data as part of a responsible AI initiative involves making sure you draw data samples from all users. For example, when doing market research, you wouldn’t want to only pull data from consumers between 35 and 50 years old when your product is supposed to “serve all customers.”
- Openly communicating limitations to your audience: Suppose you’ve designed an app that uses a machine learning algorithm to pick cars users may prefer over others, but the ML model was trained only using data from drivers in New York and Los Angeles. To use the model responsibly, the business should use a disclaimer informing consumers of this detail.
- Detecting bias: By detecting biases in your AI, such as those stemming from narrow or skewed data sets, you can reduce the possibility of your system producing discriminatory insights.
- Having explainable models: You should be able to candidly explain how your AI system works, the data it takes into consideration, and how it processes data to produce insights.
Why Is Responsible AI Important Now?
AI is becoming increasingly prominent, but many organizations who implement AI systems aren’t held accountable for inherent bias in their datasets. According to Accenture’s Tech Vision research, in 2022 only 35% of consumers around the world trust how organizations are implementing AI. By investing in ensuring your organization uses AI responsibly, you can not only shine a positive light on your company, you can also create systems that are explainable to the public and other stakeholders—as well as tools for generating profits and streamlining core processes.
Responsible AI in the Gartner Hype Cycle
Gartner’s 2022 Hype Cycle for Artificial Intelligence is a graphical representation of cutting-edge techniques used to analyze which technologies stand to offer the greatest impact.
In the report, Gartner listed Responsible AI as a technology expected to have transformational impact (their highest rating) over the next five to 10 years, meaning it should be on every AI user’s radar.
Common Challenges Responsible AI Helps Solve
Responsible AI can result in benefits that spread throughout both your organization and members of the general public whose lives intersect with your technology. Some of the key challenges it helps solve include:
Biased Systems
Some AI systems have bias baked into their algorithms. By examining your models and mitigating or (even better) completely eliminating unintentional bias, you make your organization a fairer, safer place to work for people from all backgrounds—most of whom are more likely to support working for a company that supports equality.
Lack of Transparency
At times, a company may conceal the way an AI system works, perhaps to protect its intellectual property. However, responsible AI also involves letting more people see how your AI works. This doesn’t mean posting your IP on Twitter for anyone to see—but having accessible documentation for your stakeholders is a good place to start.
This transparency reinforces the fact that you have nothing to hide and build ethical, fair AI systems.
Systems That Prioritize Business Success at the Expense of Employees
With responsible AI, you can provide tools for workers instead of eliminating their jobs. With human-centric AI, for example, you accomplish two objectives: You declare to the world that you’re using AI to support your employees, and you empower your workforce with tools that make them more productive.
Benefits of Responsible AI
The emergence of AI comes with inherent accountability and trust problems. However, designing and deploying it ethically can help mitigate these issues from the start. While it’s hard to say there’s a use case that wouldn’t benefit from responsible AI, we’ve highlighted a few key ones.
Improving Data Governance
One of the most important responsible AI use cases is accelerating governance. Organizations need both their internal governance standards and legislative requirements to be naturally integrated into their systems. Responsible AI can improve corporate governance, thereby reducing errors and the possibility of accidentally falling out of compliance with data privacy legislation.
Promoting Fairness and Eliminating Bias
Even though AI has been accused of supporting biased decisions, it can also be used to uncover and eliminate unfair workplace biases. Responsible AI can reveal bias in other AI systems by studying data about how people are promoted, hired, fired, and compensated. Using responsible AI principles, organizations can design AI systems to eliminate undesirable bias and arrive at fair decisions under specific and precisely defined criteria.
For example, you can mitigate bias in an AI algorithm used to review resumes by programming it to not account for gender or race when it determines whether to recommend a candidate.
Enabling More Ethical Practices
The main objective of taking ethics into account when designing AI systems is to help organizations develop AI that is both morally and legally acceptable. Meeting legal obligations is sometimes more linear than fulfilling moral obligations. But, with responsible AI, your organization can prioritize ethics in a way that:
- Ensures that your systems conform to all applicable laws and regulations.
- Meets the needs of vulnerable populations and neighborhoods, perhaps by leaving more sensitive decisions up to humans instead of a bot.
- Makes sure models are built with diverse datasets that ensure the insights the AI system produces are based on a wide selection of samples. For example, suppose you design a machine learning system that helps HR choose the best candidates for a job. If the system was only trained on 2,000 companies, 98% of which had male CEOs, the dataset may lack diversity that’s represented in a larger sample size.
To Wrap Up
If you’ve ever watched a science fiction movie, you know that trust and technology don’t always go hand in hand. But, by implementing a responsible AI framework, organizations can work to create trust in their AI systems while mitigating any potential harm before it occurs.
Still searching for ways to build more trust in your data science projects? Check out our checklist for increasing buy-in in your work while staying true to ethical AI practices.
