Artificial intelligence or AI, as it's better known, is finding an application in many industries and areas of life. AI systems have come a long way in the past few decades, and are still being improved every day. However, there are many different types of AI systems, all designed for specific uses.
All of those AI types share the same roots and have to be created using the four principles. They are providing guides, AI development, training, testing, and deployment. This article will explain why these four principles are a must during AI development.
Building a Responsible AI System
AI solutions are being developed everywhere in the world because they offer some incredible benefits that primarily focus on improving existing systems to make them more productive and efficient. However, the road to creating a responsible AI system is very long and there are all kinds of challenges along the way. Even though companies pour billions of dollars into AI development, only a small portion of those projects get to see the light of day. Most AI developers drop projects during development because of massive costs or low data quality.
Keep in mind that AI technology is still in its early stages, which is why it's still a distant dream for most small and medium-sized companies out there. However, the world is waking up to the incredible potential AI has to offer, so it's expected to find its way into most industries and areas of life in the following years.
With that said, AI development is still extremely hard to manage, as AI solutions must be free of bias and discrimination. They also have to be fair and provide explanations for their choices. While all of this might seem logical, creating such an AI in practice is far harder than you think.
There are a lot of considerations along the way, so most AI development is done in-house by large enterprises and international corporations. If the AI solution is built according to the rules of responsible AI, it's much more scalable and provides more accurate results. But why do AI solutions have a hard time integrating with existing systems and putting the four principles into practice? Let's take a closer look.
Responsible AI Principles
Most successful AI solutions are built using responsible AI principles as they provide safe, ethical, responsible, acceptable results. These principles can be broken down into five different areas: fairness and bias, trust and transparency, accountability, social benefit, and privacy and security. You might find different names for these principles online, but they all mean the same. Here are the principles and why they matter:
1. Fairness and Bias
First, and foremost, artificial intelligence has to be harmless to people. That means that the first principle must ensure that AI systems are unbiased in every way. Most people don't know that AI can also be biased as it's built using guidance provided by humans.
The good thing is that most AI solutions are built on an unbiased toolkit provided by special technology vendors. Instead of building everything from scratch, engineers can start their work on an unbiased framework designed to train AI systems the right way. Even though all building blocks are widely available right now, putting everything together the right way takes a lot of hard work and time.
2. Trust and Transparency
Most AI systems work in a closed environment, and their inner workings stay a mystery for most humans. However, since these systems work together with humans, there is a need for explainability. AI solutions are trained with the use of machine learning that can't tell the difference between poor and high-quality data. It's essential to train the ML model to prioritize incoming data correctly.
As AI solutions are often applied to existing software and hardware, it's extremely important to test them out to ensure that the result is true to the responsible AI principles. The developers have to test the software with different types of data to ensure that it's still unbiased and accurate.
3. Accountability
AI training requires a steady stream of high-quality data. However, organizing data pipelines isn't a job left only to the developers. There are many different systems connected to one supply chain. That includes data providers, labelers, technology providers, and system integrators. Although AI solutions provide insights that can transform a company from the ground up, sometimes they generate the wrong results. In an unorganized supply chain, it's hard to say who is to blame for the bad data.
The key here is to create a streamlined data pipeline and a clear chain of command. That will increase governance and accountability, reduce unnecessary conflicts, and create a competing environment that motivates team members into doing a better job overall.
4. Social Benefit
Responsible AI should be used for the greater good of society. It can help create a better tomorrow by analyzing historical data and finding solutions to unforeseen problems. For example, some companies have been using AI to help develop a better COVID-19 vaccine.
Moreover, new technologies are redefining customers' expectations. People want better quality products that provide more value, and AI is the element that delivers acceptable results. There is no doubt that AI will play an ever more important role in creating a better society in the future.
5. Privacy and Security
Lastly, AI systems must be able to differentiate private data and public data. It has to understand its limits and only use public data that doesn't hurt users' privacy. Moreover, since AI systems have to be connected to the internet, they have to feature state-of-the-art cybersecurity measures such as facial recognition, role recognition, etc.
Conclusion
As we said earlier, AI solutions will soon find their way into every aspect of life. In a few years' time, people will interact more with AI than live agents, so it's imperative that the solution follows the guidelines of responsible AI. One thing is for sure, AI alignment is done in the earliest stages of development. Everything that is done today will have an impact on how AI behaves in the future.
