Oct 13 / Martha James

AI and Decision-Making: Can We Trust Artificial Intelligence to Make Important Choices for Us?

By: Martha James   |   14 March, 2024
AI and Decision-Making
Ever wondered if you could trust a machine to make life-changing decisions for you? Maybe it's deciding on the best treatment for an illness or approving a loan for your dream home. AI is already playing a role in these areas, and as it gets smarter, it’s starting to make some big calls. But the question remains—can we really trust AI to handle important decisions that affect our lives?

In this blog, we’ll dive into how AI decision-making works, the different types of AI-driven choices, what drives these decisions, and the areas where AI excels. We’ll also look at the places where it falls short and explore what the future holds for AI in decision-making.

Table of Contents

  • 1. So, How Does AI Make Decisions?
  • 2. Different Kinds of AI Decision-Making
  • 3. What’s Driving AI’s Decisions?
  • 4. Where We Can Trust AI
  • 5. But Here’s Where We Should Be Careful
  • 6. The Good, the Bad, and the AI
  • 7. Looking Ahead: What’s Next for AI Decision-Making?
  • 8. Conclusion

1. So, How Does AI Make Decisions?

AI basically makes decisions by crunching a ton of data and spotting patterns in it. It’s programmed to go through all that information and come up with suggestions or predictions based on what it’s learned from past data. This process is driven by algorithms that help AI evaluate options and choose the best one according to the parameters it's been given.

Take healthcare, for example—AI looks at things like patient records, test results, and other medical data to help doctors figure out the best treatment options. It’s all about making data-driven choices quickly and efficiently. But here’s the thing: AI isn’t perfect. It depends entirely on the quality of the data it’s fed and the way its algorithms are set up.
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2. Different Kinds of AI Decision-Making

Not all AI is created equal. There are two main types of AI decision-making we usually talk about:

  • Rule-Based AI: This is the type that follows strict instructions. It works well when there are clear rules to follow, like processing credit card applications. If you meet the criteria, you're approved.

  • Learning-Based AI: This is the more advanced AI—the kind that doesn’t just follow rules but actually learns from data. For example, self-driving cars use this type of AI to learn from their surroundings and make real-time decisions.
Generative AI Applications

3. What’s Driving AI’s Decisions?

You might be wondering, “How does AI make these decisions?” It all boils down to a few key things:

  • Data Quality: AI relies on the data it’s fed. Good data means better decisions. If an AI is trained on biased data (say, historical hiring practices that favored one group over another), it might continue to make biased decisions. 

  • The Algorithms: The way AI processes that data is through algorithms. Some algorithms are better for specific tasks. For example, some are great at recognizing faces in photos, while others are better at predicting stock market trends.

  • Ethics and Values: Here's where it gets a little tricky. AI doesn't have morals or values—at least not in the way we do. So, when it makes a decision, it’s based purely on data, not whether it’s ethically right or wrong. That’s why humans need to intervene wherever necessary. 

  • Human Oversight: Speaking of humans, having people involved in overseeing AI decisions is still super important. In industries like healthcare, doctors use AI to help diagnose diseases, but they don’t just rely on the AI’s recommendation alone. They review it and use their expertise to make the final call.

4. Where We Can Trust AI

AI is already handling some important tasks. And in certain areas, it’s actually doing a good job.

  • Healthcare: AI is used to help doctors identify patterns in medical data that might be hard for a human to see. IBM’s Watson, for example, assists doctors by analyzing tons of medical studies and patient records to recommend treatment options for cancer patients. This doesn’t mean doctors are obsolete—it just means they have a smart tool to help them.

  • Finance: You know those fraud alerts you get from your bank? That’s AI working behind the scenes, identifying anything that looks fishy. It’s also used to assess credit risks and even manage investment portfolios.

  • Manufacturing and Logistics: AI is great at optimizing processes—things like making sure factories run smoothly or predicting when a machine might break down. This kind of decision-making is where AI really shines because it's all about numbers and efficiency.

5. But Here’s Where We Should Be Careful

Not all decisions are right, and there are some areas where we might want to think twice before we blindly trust the decisions made by AI.

  • Criminal Justice: This one’s a bit controversial. Some police departments have started using AI to predict crime. But these systems have been criticized for being biased, especially if the data they were trained on is biased. 
Limitations of Generative AI
  • Hiring: AI tools that help with recruitment sound great—they can sift through thousands of resumes quickly. But here's the thing: if the AI is trained on biased data (maybe a company’s past hiring data favored certain demographics), it can end up preserving those biases.
Limitations of Generative AI
  • Autonomous Vehicles: Self-driving cars are great, but we’re not quite ready to let them make every decision. While they’re great for most driving situations, there are still some ethical and unpredictable decisions that they might not handle as well as a human driver.
Limitations of Generative AI

6. The Good, the Bad, and the AI:

AI decision-making can be a powerful tool for speeding up processes and improving accuracy in areas like finance, healthcare, and logistics. But there’s also a dark side. When AI gets it wrong—especially in areas like criminal justice or hiring—the consequences can be serious. And since some AI decisions are based on complex algorithms that are difficult to explain, it can be hard to hold anyone accountable when something goes wrong.

7. Looking Ahead: What’s Next for AI Decision-Making?

AI is here to stay, and its role in decision-making will only grow. But that doesn’t mean we should trust it blindly. Moving forward, it’s all about balance. We need better transparency in how AI makes decisions, better data to train these systems, and more collaboration between humans and AI to make sure those decisions are fair and just.

8. Conclusion

If you're curious about how AI works and want to dive deeper into its decision-making processes, check out the AI courses at Syntax Academy. These courses are designed to give you a strong foundation and hands-on experience with the latest AI tools and techniques.

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