Today, there are several thoughts on the growth of AI in society. If you actively follow the new advancements in the tech space, you might have come across a lot of different viewpoints on AI.

Popular beliefs are both positive and negative. On one hand, some influencers like Elon Musk believe that advancement in AI can lead to devastating scenarios for humans, while some think AI can really help the human race reach its true potential if used properly. Well, we believe it too! 

An excess of anything, no matter how good, can lead to dramatic consequences. AI holds a lot of potential to accelerate the pace of technological advancement, but it can also have a negative impact on society if used unethically. It’s our duty to make sure we abide by rules that ensure the safe usage of AI for the good of society. And Responsible AI can help us do just that! Sounds great right? Yes! But before we get into how you can work on responsible AI, let’s first understand what is Responsible AI.

Responsible AI

Responsible AI is a relatively new field that came into the light only a few years ago. It is the practice of recognizing, and preparing for the harmful effects associated with the development of AI. It deals with developing and scaling AI initiatives within an organization without compromising its business ethics, consumer trust and employee trust. Responsible AI is the key to scale business and AI with conviction.

Here are 8 ways machine learning practitioners can help ensure the AIs we create are responsible, just, and fair:

Removing the bias

ML models are created by humans and are always biased in some manner. Think about it that way: what we call biases are in fact patterns in the data that Machine Learning algorithms are trying to extract to make predictions. Whether those patterns are representative of a real phenomenon or not, is what decides whether this bias is fruitful (and makes Machine Learning work!) or harmful. “Real” bias will give you the desired results, while harmful bias might mislead you and cause a lot of damage at the receiving end. Obviously, nobody wants to perpetuate harmful biases in AI, but that’s often easier said than done. From data collection to training to rollout, it is vital we take responsibility for removing our biases from our models. Diverse teams, robust testing, and understanding the potential biases in our training sets are a great place to start. To be precise on approach, an ML model should be:

• Respectful of all the laws and regulations pertaining to securing basic human rights.
• Ethical in moral values and principles.
• Robust and aware of its social environment.

Focusing on sustainability

A state-of-the-art model today requires 300,000 times the compute resources as one did just six years ago. AI projects are suddenly becoming a significant generator of carbon dioxide. We can fix this by addressing some of our worst habits. Using DataPrepOps tools can help us find the most helpful and harmful data records in your dataset, so we don’t waste resources on the latter. Past that, we need to stop retraining when it isn’t necessary and build models that fit our core use case instead of giant ones that try to solve too much at once. Building sustainable AI practices will ensure that we move forward in a direction that’s not only beneficial for the advancement of the human race, but is also less harmful to the environment we’re living in!

Democratizing costs

Those state-of-the-art models we just mentioned require a ton of resources that simply aren’t available to most businesses today. Only big corporations with deep pockets are experimenting with all these latest contraptions of technology. This could lead to research monopolies and keep the gains from machine learning out of the hands of regular people at non-fortune 500 businesses. We need to support and fund initiatives that open up compute resources and datasets to universities and small organizations, so we can all benefit, not just the big guys. 

Forging ethical partnerships

AI is already a global industry. That means you have a lot of choices for the kinds of partners you seek out. For example, if you need your data augmented or labeled, consider smaller shops that employ underserved communities instead of the larger ones that have less control of the work that ultimately gets done. Forging partnerships that are fair and equal in terms of opportunities are also vital to responsible AI. You employ people who need it and get incredibly accurate labels on top of it. It’s a win-win.

Hiring by work product, not diploma

With the preponderance of online machine learning classes available to anyone with the patience and talents to complete them, you don’t need to limit your hiring process to the usual top-tier universities. There is so much good talent available these days, which can be found anywhere, in particular thanks to the recent shift to remote work. Look at the work product, not the diploma, when you’re making your next hire. Increasing diversity in your team never hurts, it will only benefit your AI initiatives by keeping them unbiased from a diverse perspective.

Keep open sourcing 

The machine learning community has always been good about publishing datasets and research. Continuing this trend means more smart people with the same resources tackling the next generation of tricky ML problems. After all, you never know where the next big breakthrough will come from. But the more different viewpoints we have and the more disparate tactics we try, the faster we’ll end up there. Benefits of open sourcing – 

• Scrutiny – Open sourcing will help in getting better scrutiny from experienced researchers and developers from around the world, and their expertise will help in the holistic development of the technology.
• For the community by the community – Instead of big companies investing in private research projects that are not available to the ML community, focusing on building for the community and using its expertise is the way to go forward.
• Open for all – With open sourcing, all the new initiatives for AI will be available to everyone rather than companies with deep pockets.

Protect the users

Even though AI is still a fairly young field, the concerns both about the way it is being implemented and the applications that it serves have already made top news many times. Data privacy, for example, has been the subject of many controversies. The consumers of this new technology should not have to sacrifice their privacy & safety for the sake of accessing it. It is our responsibility to hold the builders of AI applications accountable (especially with the dawning of Generative AI), and establish rules and legislation to make sure basic human rights aren’t forgotten for the sake of building the future.

Just do more good

It’s up to the community to make this a reality. We can partner with nonprofits, mentor new practitioners, donate our time and expertise to solving problems outside our day-to-day jobs, publish tutorials, and so, so much more.

It’s not that difficult to advance our technology and do good for the community as well. All we have to do is keep in mind the larger picture, instead of being narrow-minded and focusing on short term benefits that often come at a price.

Why Responsible AI is important

What we have today was almost unimaginable a few decades ago. Technology has advanced a lot in the last few decades, and we have made strides in AI. Though AI shows a lot of promise, it can be fatal if you don’t handle it properly. Today, bias in AI-related tech projects has become a common talking point in the tech industry. This will only grow with time if there are no proper measures to ensure such biases do not occur in the first place. Avoiding the misuse of technology due to lack of attention to biases is essential to setting norms, and following those norms is key for Responsible AI.

Some brownie points on best practices for AI: 

We discussed a few ways on how we can usher in an era of Responsible AI.

• Design a framework for responsible AI from the start. Make sure to review it regularly, so as it remains relevant with time.
• Ensure all the efforts towards AI are transparent, so that the decisions that originate from AI are explainable.
• Make sure the team that decides on Responsible AI norms and reviews it is cross-functional & diverse in nature. This could avoid any bias and would allow everyone to speak their minds about ethics in AI operations.
• Implement the best practices for ML and improvise over time!

Responsible AI is ours to deal with. The way we all go about working on AI is our call each time. Choosing the responsible way will pave the path to a future that harvests all the good fruits that AI can yield for our business and society. All it takes is for all of us to chip in what we can and be responsible for the actions and paths we choose to take AI forward!