Humans vs. AI: Who should make the decision?

Summary

The video script explores the dynamic between human and artificial intelligence in decision-making, particularly in fraud detection. It illustrates how AI excels with high confidence predictions, while humans shine when AI is uncertain. The script advocates for augmented intelligence, combining human judgment with AI insights, but cautions about human biases like automation bias and the reluctance to trust AI when it admits fallibility. The key takeaway is that the most effective decision-making often lies in the collaboration between humans and AI, when carefully managed to minimize cognitive biases.

Takeaways

• 🤖 The decision-making process can be a combination of human intuition and AI analysis, each with their own strengths and weaknesses.
• 📊 AI excels in high-confidence predictions, providing high success rates when it is certain about an outcome, such as in fraud detection systems.
• 🧐 Humans tend to outperform AI when the AI's confidence is low, often due to their ability to bring in additional context and information.
• 📉 Performance curves for AI and humans differ, with AI showing a steeper curve correlating high confidence with high accuracy.
• 🤝 Augmented intelligence, which combines human and AI decision-making, can offer the highest success rates for certain confidence levels.
• 💡 The effectiveness of augmented intelligence is influenced by how AI recommendations are presented to human decision-makers.
• 👀 Forced display of AI recommendations can lead to automation bias, where humans may overly rely on AI suggestions.
• 🙌 Optional display, where AI recommendations are only shown upon request, can help mitigate automation bias and encourage independent human judgment.
• 🔢 Providing an accuracy percentage with AI recommendations can affect human trust and acceptance of the AI's advice.
• 🧐 Humans may be less likely to incorporate AI recommendations if they are explicitly told there is a chance of being wrong.
• 🔑 Understanding the strengths of both AI and human decision-making can lead to more effective outcomes when combined in an augmented intelligence approach.
• 📚 The script emphasizes the importance of considering human cognitive biases in the design of AI-assisted decision-making systems.

Q & A

What is the main topic of the video script?

The main topic of the video script is the decision-making process, particularly the comparison between human decision-making and artificial intelligence (AI), and how they can be combined for optimal results in tasks such as fraud detection.

Why are financial analysts overwhelmed with alerts in a fraud detection system?

Financial analysts are overwhelmed because 90 percent of the thousands of alerts generated each day are false positives, making it difficult to focus on the actual fraudulent transactions.

How does the video script describe the typical AI performance curve in terms of success rate and confidence score?

The script describes the AI performance curve as having high success rates at very low and very high confidence scores, indicating the AI is certain about its predictions. However, at moderate confidence levels, the success rate drops, showing the AI is unsure.

How does human performance compare to AI performance in the script?

Human performance curves are typically flatter than AI, meaning humans may not be as accurate as a confident AI but can outperform AI when the AI is unsure, especially in complex or statistically rare cases.

What is the term used to describe the combination of human decision-making aided by AI?

The term used to describe the combination of human decision-making aided by AI is 'Augmented Intelligence'.

Why is augmented intelligence considered to have the highest success rate for some confidence scores?

Augmented intelligence has the highest success rate for some confidence scores because it leverages both human judgment and AI analysis, particularly in cases where the AI's confidence is not very high or very low.

What cognitive bias is mentioned in the script that can affect the effectiveness of AI recommendations?

The script mentions 'automation bias', which is the tendency for humans to favor suggestions from automated systems and ignore contradictory information.

What are the two display methods for AI recommendations mentioned in the script, and how do they differ?

The two display methods are 'forced display', which shows the AI recommendation simultaneously with the decision case, and 'optional display', which only shows the AI recommendation when requested by the human decision maker.

How does the accuracy percentage of an AI recommendation affect human decision-making?

When an AI recommendation is accompanied by an accuracy percentage, humans are less likely to incorporate the recommendation into their decision, as they may not trust or like the idea that the AI might be wrong.

What does the script suggest as the best approach to decision-making in complex tasks?

The script suggests that the best approach to decision-making in complex tasks is a combination of AI and human input, known as augmented intelligence, while being mindful of human cognitive biases.

What is the final message of the video script regarding the collaboration between humans and AI?

The final message is that humans and AI algorithms can form a powerful team to improve decision-making outcomes, provided that we understand and leverage their respective strengths and account for potential biases.

Outlines

00:00

🤖 AI vs. Human Decision Making in Fraud Detection

The script discusses the dilemma of whether a decision should be made by a human or an artificial intelligence (AI). It uses the example of a fraud detection system to illustrate the strengths and weaknesses of both. The AI's performance is typically high when it is confident but lower when unsure, while humans may outperform AI when the AI's confidence is at a 50 percent level, especially in complex or rare cases. The script introduces the concept of 'augmented intelligence,' which combines human decision-making with AI assistance, potentially leading to the highest success rate for certain confidence scores.

05:05

🧠 Overcoming Cognitive Bias in Augmented Intelligence

This paragraph delves into the importance of considering human cognitive bias when implementing augmented intelligence. It contrasts 'forced display' and 'optional display' of AI recommendations and explains how they influence human decision-making. Forced display can lead to automation bias, where humans may overly rely on AI suggestions, while optional display allows humans to form their own impressions before considering AI input. The paragraph also touches on the impact of trust and accuracy percentages on human acceptance of AI recommendations, emphasizing the need to present AI augmentation effectively to enhance decision-making outcomes.

Keywords

💡Decision Making

Decision making is the cognitive process of selecting a course of action from among multiple alternatives. In the video's context, it refers to the choice between a human or an AI making a decision. The script discusses how different tasks may be better suited for either a human or an AI, highlighting the importance of understanding the strengths and limitations of each in the decision-making process.

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video script discusses AI's role in decision-making tasks, particularly in the example of a fraud detection system where AI can handle a high volume of alerts more efficiently than humans.

💡Fraud Detection

Fraud detection involves the identification of potentially fraudulent activities, often using algorithms and data analysis. The script uses fraud detection as a practical example where AI can assist in reducing the workload of financial analysts by identifying false positives among numerous alerts.

💡Confidence Score

A confidence score in the context of AI refers to the level of certainty the system has in its predictions or decisions. The video explains how different confidence scores on a scale from 0 to 100 percent can affect the success rate of identifying real alerts versus false positives in a fraud detection system.

💡Human Bias

Human bias refers to the systematic errors in human judgment and decision-making caused by our cognitive inclinations. The script mentions that the way AI recommendations are presented to humans can influence their decisions, potentially leading to automation bias where humans overly trust AI suggestions.

💡Performance Curve

A performance curve in the video represents the relationship between the AI's confidence score and its success rate in predicting the correctness of an alert. It illustrates how AI performance varies with different levels of confidence, showing higher success rates at very high or very low confidence scores.

💡Augmented Intelligence

Augmented intelligence is a concept where human intelligence is enhanced by AI. The video suggests that combining human decision-making with AI assistance can lead to the highest success rates, especially in cases where the AI's confidence score is moderate.

💡Forced Display

Forced display is a method where AI recommendations are automatically shown alongside decision cases for immediate consideration. The script warns that this can lead to automation bias, where human decision-makers might favor the AI's suggestions over their own judgment.

💡Optional Display

Optional display is an approach where AI recommendations are only provided upon request by the human decision-maker. The video script explains that this can help overcome automation bias by allowing humans to form their own initial impressions before considering AI input.

💡Accuracy Percentage

Accuracy percentage in the context of AI refers to the estimated likelihood that a prediction will be correct. The script notes that when AI recommendations include an accuracy percentage, humans may be less likely to incorporate the AI's advice into their decisions, reflecting a distrust of imperfect recommendations.

💡Cognitive Bias

Cognitive bias is a pattern of deviation in judgment that occurs as a result of the way our minds process information. The video discusses how human cognitive biases, such as automation bias and distrust of imperfect recommendations, can affect the effectiveness of augmented intelligence in decision-making.

Highlights

The debate on whether a human or AI should make a decision is explored, emphasizing the strengths and limitations of both.

AI outperforms humans in tasks with high statistical certainty, while humans excel in complex or rare cases.

Fraud detection is used as a case study to illustrate the decision-making process involving AI and human analysts.

The concept of a performance curve is introduced to visualize AI and human success rates in decision-making.

AI algorithms are highly performant when confident but less so when uncertain, unlike humans who may outperform AI in unsure situations.

Humans can bring additional context and information to decisions, unlike AI which sticks to its decision logic.

Augmented intelligence, a combination of human and AI decision-making, is proposed as optimal for certain scenarios.

The success rate of augmented intelligence is highest for moderate confidence scores in predictions.

Human cognitive bias, such as automation bias, can affect the effectiveness of AI-assisted decision-making.

Forced display of AI recommendations can lead to automation bias, where humans favor AI suggestions over their own judgment.

Optional display of AI recommendations allows humans to form their own impressions before considering AI input.

The presentation of AI recommendations, including accuracy percentages, influences human trust and decision-making.

The importance of minimizing human cognitive bias in the decision-making process when using augmented intelligence is emphasized.

AI and human collaboration can lead to improved decision-making outcomes when the right balance is achieved.

The transcript concludes by suggesting that understanding who to ask is key to leveraging the strengths of both AI and humans.

The video invites viewers to engage with the content through questions and subscriptions for more informative content.

Transcripts

A decision needs to be made.

But who should make it?

Me, a human, ... or an artificial intelligence, an AI?

We've discussed before that humans can outperform AI at some tasks,

but that, statistically, AI will make a better job of deciding for other tasks.

So for one single decision, who should decide?

Well, the answer is a fascinating combination of holistic curves and human bias.

Let's get into it.

So, consider a fraud detection system.

Fraud detection.

The system generates the alerts of potentially fraudulent transactions.

Financial analysts review each alert.

Now, there's thousands of events generated each day,

and the analysts are overwhelmed with 90 percent of those alerts being false positives.

An AI system could help alleviate the workload.

But which alerts should the AI handle, and which should be processed by a skilled financial analyst?

Well, let's draw a graph to answer the question, "Is this a real alert?"

So, let's draw a graph with an X and Y axis.

The Y axis tracks the success rate.

So an alert comes in, we make a prediction as to if it is real or not,

and we track if that prediction turned out to be right.

Along the X axis is the confidence score.

So a confidence score of zero percent

says a prediction thinks that this is definitely not a real alert, it's a false positive.

A confidence score of 100 percent

means that a prediction is certain that it is a real alert.

Now a typical AI performance curve will look something like this.

So we've got very low confidence scores, this is not a real alert,

and very high confidence scores, this is a real alert.

They're correlated to a high success rate.

That's these areas up here.

When the AI is not sure about a given prediction, then it's not such a case.

Lower success rate when the AI is not sure.

And so effectively the AI algorithm is saying, "I don't know".

Now, human performance curves are typically a little bit flatter than that.

So the human's performance curve might look something like this.

Often not quite as accurate as a very confident AI algorithm,

but a little better at making the right decision when the AI is unsure.

At a 50 percent confidence level, a human is likely to do a better job than an AI.

Now why is that?

Well, when an AI is certain of itself,

it's highly performant and beats out humans who can lose consistency and focus and attention.

AIs, they don't get distracted.

But on the other hand, when an AI is unsure,

often for cases that are complex or statistically rare,

humans can outperform an AI prediction by bringing in additional information and context.

They can look stuff up or ask a colleague,

whereas the AI sticks to its same old decision logic and information.

So when a new alert comes in, if the AI assigns a high or low confidence level,

then chances are that statistically speaking, it will do a better job of deriving if that alert is real

or a false positive, than a given financial analyst.

But this is not a zero sum game.

It doesn't have to be AI or human.

We have one more option.

Augmented.

Augmented intelligence combines both a human decision, aided by AI,

and this performance curve falls somewhere between the two.

And for somewhat low and for somewhat high confidence scores,

which make up a significant number of predictions,

it's augmented intelligence that will have the highest success rate.

Except ...

... for augmented intelligence to be most effective, we need to account

for the messy business of human cognitive bias.

We're not always great at doing what we're told.

It turns out that how we present information from an AI algorithm to a human decision maker

has a significant influence on how effectively that information is used.

So, to illustrate that, let's consider forced display vs. optional display.

A forced display simultaneously displays an AI recommendation along with a given decision case.

So, for every fraud decision alert that I need to make a decision about,

I, as the analyst, also see the AI's recommendation.

And this can lead to something called automation bias,

which is the propensity for humans to favor suggestions from automated decision making systems

and to ignore contradictory information.

Effectively, the human decision maker is saying the AI knows best

and going with the AI prediction at the expense of their own judgment.

Optional display means the AI recommendation is only shown to the human decision maker when they request it.

So, a person sees a decision case and can then ask the AI to reveal its recommendation.

This overcomes automation bias

by giving a person time to consider the case for themselves before consulting an AI recommendation.

The human is not overwhelmingly influenced by what the AI thinks

because they've had a chance to make up their own first impression.

And then there's the whole issue of trust, too.

When an AI recommendation is accompanied by an accuracy percentage,

which indicates how likely this prediction is to be correct,

humans are less likely to incorporate the AI recommendation into their decision,

regardless of the accuracy percentage being displayed.

Basically, we don't like recommendations that openly tell us that they might be wrong.

So, we've seen that who should make a decision, a human, an AI,

or a human assisted by an AI recommendation, is something that we can derive.

We can move from subjective decisions to the quantifiable.

That for a given decision who the most effective decision maker is likely to be.

And when the most effective decision maker is a combination of AI and human, that's augmented intelligence,

we must consider a presentation of that augmentation to minimize human cognitive bias in the decision making process.

Brought together, us humans and AI algorithms make a pretty powerful team.

We can improve decision making outcomes - if we just know who to ask.

If you have any questions, please drop us a line below,

and if you want to see more videos like this in the future, please like and subscribe.

Thanks for watching.