The Ethics of Image Analysis Martin Peterson,TU/e

Outline of Lecture 1)What is ethics? And why should you care? 1)False positives vs. False negatives 2) The ROC analysis 3) Ethicals aspects 4) Ethics assignment

What is ethics 1)Descriptive ethics 2) Normative ethics 3) Metaethics My claim: Algorithms for image analysis contain hidden ethical assumptions. Example: False positive vs. False negatives

The Tiger Example In order to survive in the jungle, you build a detector that warns for everything that is yellow, including tigers. - But everything edible is also yellow. - How sensistive should the warning device be?

The Analogy Is Obvious lightness location T1T1 T2T2 V1V1 V2V2 Segmentation in a noisy image. For a larger threshold value, T 1, the estimated area V 1 of the ‘blood’ segment will be lower than the estimated area V 2 which is found with a lower threshold T 2. It is not a priori clear, however, which of the two threshold values is the ‘correct’ one. The software engineer chooses a threshold without real argument – thereby biasing the outcome of the algorithm when it is used on patient data. This may statistically influence the change of false positive diagnostic errors in favour of false negatives, or vice versa.

False positives vs. False negatives True positve = Device says it is a tiger, and it is a tiger. (You survive!) False positve = Device says it is a tiger, but it is not a tiger. (You starve!) False negative = Device says it is not a tiger, but it is a tiger. (You die!) True negative = Device says it is not a tiger, and it is not a tiger. (You survive!)

Trade-off between FP and FN Unfortunately, we cannot build a device that warns for all tigers and nothing but tigers – the more sensitive the device is for yellow things, the more bananas it will detect.

The ROC analysis 1(2) ROC = receiver operating characteristic True positive rate (TPR) = TP / P = TP / (TP + FN) False positive rate (FPR) = FP / N = FP / (FP + TN) P = actual number of postives (tigers) N = actual number of negatives (bananas etc.)

The ROC analysis 2(2) P = TP + FNN = FP + TN True positive rate (TPR) = TP / P = TP / (TP + FN) False positive rate (FPR) = FP / N = FP / (FP + TN) TPFP FN TN

A Numerical Example: Detector A TP= 63FP= FN= 37TN= TPR= 0.63 FPR= / / 28+72

A Numerical Example: Detector B TP=77FP= FN=23TN= TPR=0.77 FPR= /

The area under the ROC curve is sometimes used as a score

The radar on my sailing boat….

Ethical Aspects of the ROC analysis Algorithms, (i) sometimes comprise ethical assumptions, and (ii) influence our decisions, (iii) but algorithms are not moral agents.

Why Do Alogorithms Comprise Ethical Assumptions? VALUE-JUDGEMENT: A proposition is a value-judgement if and only if it expresses a view on how things ought to be, or what is good or bad, or desirable or undesirable. Do all algorithms comprise value-judgments? No. But some do. DEFINITION An algorithm comprises a value-judgment if (but possibly not only if) two or more persons who accept different value-judgments would have a rational reason to design or use the model differently.

The limits of using area under the ROC curve an ethical criterion

The Goal of Science vs. The Goal of Medicine Science: Seek the truth. Build upon accumulated knowledge. Be conservative – a false negative is better than a false positive? Medicine: Protect the patient. Better to be safe than sorry – a false positive is better than a false negative?.

A Moral Principle: Transparency Transparency

Another Moral Principle: The Precautionary Principle ”Where there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost-effective measures to prevent environmental degradation.” (UNCED) “When an activity raises threats of harm to the environment or human health, precautionary measures should be taken even if some cause and effect relationships are not fully established scientifically.” (Wingspread Conference)

What kind of principle is PP? A legal principle A normative principle An epistemic principle

. Normative interpretations of PP: What should we do? Epistemic interpretations of PP: What should we believe? - Reversed burden of proof? - If experts disagree, trust the pessimistic ones? - False positive/negative errors

An objection to the epistemic version of PP: You live in the jungle. Tigers are yellow and black. Everything edible in the jungle is also yellow (Bananas) To protect yourself against tigers you build a device that detects and warns for everything that is yellow. The detector is very sensitive  Many false positives Good news: Because of the detector you will not be killed by a tiger. Bad news: You will starve to death, because you will never find anything to eat Conclusion: It is far from clear that it is in general better to prefer false positives over false negatives.

Assignment Include 2 A4 pages in the final report, in which you: 1)Explain what an ROC analysis is and why it is important to your project. 2)Discuss the ethical aspects of the choice between a high TPR and a low FPR. What would a reasonable choice be for your project? 3)Motivate why the TPR and FPR of your software is at it is.