Project 3 Example: Human-AI Collaboration Tester (HAICT) Exp. 7
Phase: NA
Conditions: Decision Making, Computer Aided Diagnosis
Sponsor: Brigham and Women's Hospital

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Project 3 Example: Human-AI Collaboration Tester 
(HAICT) Exp. 7 
ClinicalTrials.gov ID NCT05272189 
 
Document Date: December 3, 2025 
 
 
 

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Introduction 
Artificially intelligent (AI) systems are increasingly common and competent collaborators for 
humans performing a variety of everyday tasks. There are AI systems designed to help us park 
our cars, translate our voices into text messages, and filter out our spam email. AI collaborators 
have also been integrated into the workflows of expert humans performing socially important, 
and even potentially dangerous, jobs — e.g., assisting air traffic controllers in preventing midair 
collisions. Interestingly, some AI systems are designed to act as second observers to assist 
humans in making important perceptual decisions: Airport baggage screeners use AI support to 
decide whether there is a weapon in a bag; radiologists use AI support to decide whether a 
suspicious region on an X-ray is cancer; and law enforcement use AI support to identify suspects 
in surveillance video. What makes these examples different from AI sorting our email, or 
calculating flight paths, is that they all involve a human and a machine making a decision about 
the same perceptual stimulus (e.g., “Is that object in this suitcase a weapon or a dumbbell?”).  
 
If humans and AI are working together, even the most talented AI is only useful if its human 
collaborator can and will take advantage of its information. If the human does not trust the AI, she 
may ignore its advice (Beck et al., 2007). There are many different possible rules for combining 
the same human and AI information (e.g. Does the AI offer its information before, during, or after 
the human’s initial decision?). These rules can produce different outcomes. We frame this situation 
in terms of signal detection theory (Hautus et al., 2021; Macmillan & Creelman, 2005). In that 
context, changing interaction rules can shift d’, criterion, or both1.  Moreover, the rules interact 
 
1 Note: In the psychophysical literature,  d’ is often called “sensitivity”. However, in the 
medical literature, “sensitivity” refers to the true positive (TP) or hit rate. Accordingly, we 
will try to avoid using the term, “sensitivity” at all. 

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strongly with task variables (e.g. How prevalent are positive cases?). The interaction of task and 
rule can change the human user’s attitude / trust in the AI (Hoff & Bashir, 2015). This, in turn, 
influences the combined results for human plus AI.  
AI support for perceptual decision making is wide-spread. We will focus our discussion on 
medical images (Greenes, 2014) recognizing that the issues are similar in other domains from 
conversing with your car (Strayer et al., 2016) to automating airport screening [Hättenschwiler, 
2017 #13395]. In medical image interpretation, much of this work falls under the heading of 
Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) (Li & Nishikawa, 
2015).  
If experts were perfect at detecting and identifying clinically relevant findings in images, there 
would be no interest in CAD, but experts evaluating medical images make more mistakes that we 
would like, and CAD helps less than we would hope. Precise error rates in actual clinical practice 
are hard to establish though 20-30% appears to be a reasonable estimate in many radiologic 
domains (reviewed in Berlin, 2007). Some of these errors are, in a sense, desirable. For instance, 
a suspicious spot on a mammogram should be referred for further testing, even if it subsequently 
turns out to be a false positive. Of course, if AI could reduce these ‘desirable’ false positive errors, 
that would save unnecessary testing, expense, and worry. False negative / miss errors can have a 
higher cost in cases where early detection improves outcomes (Monticciolo et al., 2017).  
 
 
 

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Kundel and colleagues (1978) provided a useful 3-part taxonomy for false negative (“miss”) 
errors, dividing them into search, recognition, and decision errors, based on eye movements 
recordings. Search errors are those where the eyes never fixate on the target at all. In recognition 
errors, the eyes briefly (<500 msec) fixate the target but then move on, as if the observer failed to 
recognize that something interesting had been fixated. Decision errors are those where the observer 
(and the observer’s eyes) scrutinize the relevant stimulus location but fail to identify the target. 
 
Figure 1: Two-alternative forced-choice decisions as signal detection problems. 
 In their simplest form, decision errors can arise when the observer is faced with a two-alternative 
forced-choice decision (e.g. Is this cancer or not?). In the terms of signal detection theory, as 
cartooned in Figure 1, truly negative and positive cases can be thought to produce overlapping 
distributions of internal response values within the observer. To make a decision, the observer 
must establish some ‘criterion’, ‘c’; declaring all values above criterion to be positive and those 
below to be negative. The separation between the positive and negative distributions determines 
the difficulty of the decision and can be quantified by the parameter, D’ (though the situation is 

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more complex and other metrics are more useful if, for example, the variance of the two 
distributions is not the same). When the distributions overlap, the observer must make errors: False 
positive (FP) or False alarm errors, shaded red, and False Negative (FN) or miss errors, shaded 
green. Shifting criterion can change the mix of errors but cannot eliminate them. A manipulation 
that increases D’ reduces errors.  
Two observers will typically perform better than either alone, especially if the ‘noise’ that 
degrades their performance is not identical. The relationship of two observers in SDT terms has 
been formalized by Sebastian and Geisler (2018).  A CAD AI that offers its own assessment of the 
stimulus can be thought of as a second observer. The focus of our interest is in the interaction of 
that second observer with a human who is making the ultimate decision. 
 
Search and recognition errors can arise when the perceptual task involves spatial uncertainty. If 
the task involves a search for possible targets (e.g. signs of breast cancer), it is possible to fail 
because you never looked at the target (search errors) or because, when you looked at the target, 
its importance was not registered (recognition errors). The use of AI to reduce these errors is 
important but not considered in this experiment. 
 
As we are framing the Human-AI interaction in perceptual decision making, there is an error-
prone human observer and an AI that might be useful.  The resulting human-AI interaction can be 
formalized as a signal detection problem with two observers (Bartlett & McCarley, 2017; Bechar 
et al., 2009). For example, take a human and an AI, each with d’ of 2.5 (comparable to performance 
in screening mammography). If the two observers are uncorrelated, the optimal joint d’ would be 
about 3.5. As Bartlett and McCarley (2017) note, however, “In practice, unfortunately, people 

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often interact with automated aids in a suboptimal way”. They may overweight or underweight the 
AI advice. The core problem is that there is no one optimal solution that covers all Human-AI 
interactions. To offer a trivial illustration, if the human is simply guessing and the AI is perfect, 
the obvious rule is to use the AI. If the situation is reversed, the obvious rule is to ignore the AI. 
The optimal use of the AI, therefore, depends on the details of the specific Human-AI interaction. 
Unfortunately, it is not practical to test a range of modes of interaction in real-world clinical 
situations. Expert observers are a limited resource and interventions (e.g. don’t use AI on the next 
N patients) are often unethical or impractical in the real world. Our goal is to develop a “Human-
AI Collaboration Test (HAICT) that can be used in the lab to identify candidate interventions that 
could be practically tried in real-world settings. 
 
We can identify a non-exhaustive set of factors that will influence the AI-human interaction.  
1) Absolute and relative skill of the human and AI: This defines a 2D space of conditions where 
the human and AI might both be very skilled or not or where one is more skilled than the 
other.  
2) Signal & noise variability: The physical signal, presented to the human and/or the AI has 
some intrinsic variability. That variability could be different for signals (Do all masses look 
alike and are they all imaged with equal fidelity?) and for ‘noise’ (Do other possible masses 
all look alike or do they come in a wide range of forms?).  
3) Correlation of AI and human responses; Are the human and AI responding in the same way 
to the same thing? The potential benefit of an AI declines as its correlation with the human 
increases. Thus, the situation is different if both parties are limited by the same physical 
signal/noise ratio as opposed to a condition where the human is looking at an X-ray while 

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the AI is basing its response on some entirely different measurement. Sebastian and Geisler 
(2018) have provided an SDT-based method for estimating the correlation between two 
observers. 
4) Feedback: In many real-world settings, it is not available or available only incompletely, in 
aggregate, and/or after a significant delay. Consider screening mammography where some 
AI true positives will provide an immediate form of feedback when the radiologist clearly 
sees that CAD has marked something they found or missed. Feedback about other CAD 
responses would have to wait for subsequent follow-up (e.g. Was it a hit or a false alarm?). 
In the case of miss errors, by either the human or the AI, it is possible there is no feedback.  
5) Costs and benefits of different outcomes: In mammography, for example, FP errors carry 
modest risk and some real psychological and financial costs. The costs of FN errors are much 
greater. A similar situation applies to airport security screening. The could also be 
operational costs if, for example, adding AI doubled the time or the cost of an exam. 
6) Target Prevalence:  Particularly in screening situations, target prevalence is an important 
variable. Breast cancer is rare in a screening population. Real threats are even rarer at the 
airport checkpoint. This obviously interacts with costs and benefits. Are 100 or 1000 FP 
errors more or less expensive than one FN error? Prevalence is known to influence human 
behavior (Levari et al., 2018; Wolfe et al., 2005). 
 
This partial list describes a vast dataspace. It would be neither practical or fruitful to systematically 
cover it with experiments. One possible solution would be to model the effects. A valiant effort 
was made by Bechar et al (2009) who proposed to compute “An Objective Function to Evaluate 
Performance of Human-Robot Collaboration in Target Recognition Tasks” (Bechar et al., 2009). 

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Bechar et al created a linear model that incorporates many (but not all) of the variables described 
above and they could draw some interesting conclusions from the output. For instance, for their 
conditions, the utility of the AI increases as target prevalence increases; a potentially depressing 
conclusion for CAD systems deployed to help with the detection of rare targets. Their conclusions 
include the statement, “Since the number of parameters is large and, in addition, there are 
interactions between the parameters, it is difficult to predict the system performance”.  
How, then, can progress be made on deciding on the best use of an AI system? In this project, 
we propose a testbed that captures critical aspects of human-AI interaction in a signal detection 
paradigm. The idea is that a simple, 2-alternative forced-choice (2AFC) task can be tailored to 
have the properties of the real-world situation. Stimuli can be created to constrain the d’ of the 
observer and the AI. The prevalence of targets can be set along with a reward structure for different 
types of correct and incorrect responses. Feedback can approximate the real-world situation. 
Having built a task with critical similarities to the real-world task, it is possible to test non-expert 
observers in relatively short experiment that manipulate factors like the timing of presentation of 
AI information or the criterion used by the AI. Such experiments will not eliminate the need to test 
hypotheses with real experts doing the real task. However, results from this Human-AI 
Collaboration Test (HAICT) can guide clinical testing to the more promising possibilities. 
 
In the experiment described here, we take a single, hypothetical AI and use it as a second reader 
– presenting the AI opinion after the human has offered an initial reading. 
 
HAICT methodology 

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HAICT treats the human-AI interaction as a signal detection problem with two observers. We 
generated a set of visual stimuli that both our human and “AI” observer must evaluate. This is 
analogous to a CADx situation where a human reader might use a CAD system to help determine 
if a mass is cancerous.  
 
At the heart of the HAICT method is the use of artificial stimuli that can be precisely controlled 
in ways that real medical images cannot be. Thus, as shown in Figure 2, the stimuli were 20x20 
square grids in which each square was a shade of red or green. 
 
Figure 2: Construction of HAICT stimuli  
Whether a case is “positive” or “negative” determines the numbers of red and green cells in the 
stimulus. For a negative trial, the number of red cells is derived from one normal distribution. 
For positive trials, the number is derived from another distribution with a higher mean. The 
difference in those means, converted to d’ puts an upper limit on performance. That is, if the 
distributions are separated by d’=2.5, as they are in this case, even the most ‘expert’ observer 
cannot produce results with d’ better than 2.5, except by chance. This method means that some 
positive cases will look negative and vice versa. It is important to explain this to participants, 
especially if the experiment involves feedback in order to avoid confusion and frustration. 

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Human and AI stimuli can be the same or they could be generated separately so that the 
maximum performance of the two observers can be independently varied. In this experiment, 
both the AI and human have a maximum performance of a d’ of 2.5. 
 
Specific Method 
We tested 13 naïve observers (avg age 31, 8 female, 5 male). All had normal color vision as 
assessed by Ishihara color plates and acuity of 20/25 or better.  
 
Two conditions were be tested:  
1. 
Baseline - No AI input. Observer classifies each case as "bad" or "not" bad on their own.  
2. 
Second Reader - The observer makes an initial decision about every case. The simulated 
AI silently classifies stimuli using a “conservative criterion” (c = 0.5). The logic for the 
conservative criterion is that the second reader is being used to cut down on false positive 
responses and so it is intended to raise questions about positive human responses that might be 
marginal. If the observer and AI disagree, then the AI informs the human observer. The observer 
is then given a chance to either change their response or go with their first opinion.  
 
In each of these conditions, the prevalence of “positive cases” is varied in separate blocks. The 
percentage of targets could be 10%, 33%, 67% or 90%. Humans are known to become more 
conservative at low prevalence, missing more targets (Horowitz, 2017). Thus, there were 8 
blocks per Observer (2 conditions X 4 levels of prevalence). Each Observer ran 200 trials in each 
block. Block order was randomized.  
 

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Stimuli were present in a darkened room on Macintosh desktop computers with Os at an 
approximate 60 cm viewing distance. 
 
Data analysis 
One observer was removed for poor performance. For the remaining 12 Os, we performed 
standard signal detection analysis. For each condition, we computed the probability of a “hit” or 
true positive response (correct positive responses / total positive trials) and “false alarms” or 
false positive response (false positive responses / total negative trials). These values are 
converted to “z-scores” (=inverse of the standard normal cumulative distribution), zHit & zFA. 
From those values, d’ = zHit-zFA and criterion, ‘c’, = (zHit-zFA)/-2. Response time can also be 
looked at but is not of particular interest in this experiment.  
 
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