There is no doubt that artificial intelligence and every tool that has AI in its roots, is taking us by storm. Social media posts, tips and tricks, blogs, articles, movies-you name it! AI discussions everywhere you turn. We know that AI has been a hot topic for quite some time now. There was always a bit of skepticism and fearfulness surrounding AI, but it was more on the sci-fi side of robots taking over the world. So, there was never that big of a panic. When ChatGPT popped up, things started to change! People were surprised and amazed by what it can do and how powerful it is. But with that also came a fair bit of fear for their own jobs. Now that ChatGPT proved that it can write copy, write code other AI tools could design, do voice-overs, there’s also deep fake and so much more. This left people asking the question of ‘What now?” and “How much more advanced can it get?”.
AI solution development has become a sensation. By the end of 2027, the AI market is expected to be $267 Billion. And by the end of this decade i.e. 2030, it is forecasted artificial intelligence will be contributing as much as $15 trillion dollars to the global economy. Even today, as many as 37% of businesses are already using AI in their day-to-day operations.
Since AI solution development has become a huge sensation more and more people are jumping on the trending bandwagon and trying to do as much as they can with it. There are a few important things to highlight when talking about developing AI solutions like:
- Design AI systems with fairness and transparency in mind.
- Ensure AI systems are secure and protect user data.
- Test AI systems thoroughly before deployment.
- Monitor AI systems regularly to ensure accuracy.
- Provide users with clear explanations of how AI systems work.
Let’s get into what this means!
Transparency in mind
Imagine a facial recognition system is being used for security purposes in the airport. Usually it works perfectly, but one day it starts to miscategorize individuals as potentially dangerous. As a result, several innocent people are arrested. Would it be important to know why the system made all these mistakes? Should we be able to explain why it made mistakes? And why would this matter? Some contemporary machine learning systems are so-called “black box” systems, meaning we can’t really see how they work. This “opacity”, or lack of visibility, can be a problem if we use these systems to make decisions that have an effect on individuals. Individuals have a right to know how critical decisions – such as who gets accepted for a loan application, who gets paroled, and who gets hired – are made. This has led many to call for “more transparent AI”. Transparency itself is ethically neutral and is not an ethical concept. Instead, it constitutes an ideal. Transparency is something that can manifest in many different ways, and something that can present a solution for underlying ethical questions. In this sense, transparency is relevant at least to the three following issues:
1.Justifying decisions – Good governance in public or private sectors involves non-arbitrariness of decisions. All decisions that are made must be ethical, justifiable and objectively and collectively good.
2.A right to know – According to human rights, people are entitled to have explanations on how decisions were made so that they can maintain genuine agency, freedom and privacy. Freedom entails the right to get answers to questions such as “How am I being tracked? What kind of inferences are being made about me? And how, exactly, have the inferences about me been made?”
3.Understand consequences of actions – There is a moral obligation, up to some reasonable level, to understand and predict the consequences of the kinds of technologies one brings into the world. That is, saying “we can’t understand now what it will do” is not a valid argument for unleashing a system that causes harm. Instead, it is our moral duty to explore the possible risks.
Secure AI systems
When AI begins to “think” as humans do, or even in place of humans, it could threaten three central privacy principles—data accuracy, protection, and control:
- Data accuracy: For AI to produce accurate outputs, algorithms must contain large and representative data sets. Underrepresentation of certain groups in data sets can result in inaccurate outcomes and even harmful decisions. This algorithmic bias is often created unintentionally. For example, researchers have found that smart speakers fail to understand female or minority voices because the algorithms are built from databases containing primarily white male voices. With this in mind, what would happen if we trusted AI to take our 911 calls?
- Data protection: Although large data sets produce more accurate and representative results, they run a higher privacy risk if they are breached. Even seemingly anonymized personal data can easily be de-anonymized by AI. Specifically, researchers have found there is minimal anonymity in even coarse data sets, resulting in up to 95 percent reidentification. Together, this means that you could run the risk of being easily identified and have your data leaked if privacy considerations are not taken into account. Using AI also can lead to red flags when utilized to process taxes and analyze federal benefits eligibility.
- Data control: When AI starts to see and define patterns, it draws conclusions and can make decisions about you to make your online experience easier or more robust. However, when AI yields false or unfavorable results, it raises questions whether the decisions were made fairly. For example, AI used to score credit risks can unintentionally cut the credit lines of individuals who fit certain profiles. These decisions can happen without your knowledge, consent, or choice, especially if the data driving these decisions is collected without your knowledge. What’s more, AI can infer further details about you, such as your political leanings, race, and religion, even if you never broadcast these details online.
Testing AI systems
It’s very important that before deploying an AI software that it has gone through a test period in which the team that created it can put it through various tests and see how it performs. If the software fails and shows evidence of bad performance, that would be an indicator of where the development team needs to focus their efforts before sending it out into the world. Putting out AI systems that are faulty can be risky and damaging not only to the company standing behind it but also to the users.
Monitoring AI systems
Even though the system goes through a beta testing phase and passes, this is not where all efforts stop. Over time the system can encounter problems that may mess up the normal functionality of the system or put it at risk. Monitoring makes it easy for companies to detect malicious intruders or discover an attack-exposed vulnerability in their infrastructure and quickly take measures to prevent both. Monitoring the AI system also ensures a good experience for the user, which is what is important in user retention.
Clear usage instructions
Usage instructions means directions regarding conditions for use and the process for obtaining permission. All users must be given clear and understandable instructions in order to understand what that means for them and their privacy. In the process of developing responsible AI solutions it is extremely important to not purposefully omit information that may sway the decision of the user.
So, if you are a business looking to create an AI solution of your own, make sure to follow these basic guidelines in order to create an enjoyable for the user and a successful product. If you’re looking for other tech related solutions and/or IT support feel free to reach out to our team at admin@colbygroup.net or visit our website for more information.