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Summary endterm | Lecture 1-6 | RDS 2024 | Responsible Data Science (INFOB3RDS)

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Universiteit Utrecht (UU)

This summary contains everything from the lectures that you need to know for the endterm exam of RDS. It is made specifically for RDS in 2024. For the summary, I have used my own lecture notes and the lecture slides.

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Summary Responsible Data Science
(INFOB3RDS) Lecture 1-6 Endterm
Glossary
Act utilitarianism: choose action that maximizes utility
Algorithmic bias: bias due to wrong algorithm, does not work well with your data.
Ambiguity effect (cognitive bias): always prefer known risks over unknown risks
Analytical bias (data bias): results from how results are evaluated
Anchoring effect (cognitive bias): people rely too much on first piece of information (which
can be irrelevant)
Appropriate details (XAI property): (the amount of) details in explanation should be relevant
Association bias: data reinforces cultural bias, (mostly ‘gender bias’), result of training data.
Attraction effect (decoy effect): chooser tends to choose items that have more (slightly
worse) competitors, instead of just considering logical alternatives (choice is influenced)
Automation bias (human bias: blindly trusting technology. Very psychological.
Backward chaining: explain results, get facts from results
Bandwagon effect (cognitive bias): basing your decision on what the crowd does
Base rate fallacy (cognitive bias): people ignore statistical base rates of incidents
Black-box algorithm: model representation shaped by algorithm, not understandable to
humans.
Categorical imperatives: things you should follow regardless your desires
Clarity (XAI property): explanations should be clearly understandable
Cognitive bias: tricks our thinking instead of vision.
Completeness (XAI property): explanations should explain everything you need to know
Confirmation bias (cognitive bias): we tend to seek information confirming our beliefs
Confirmation bias (human bias): “see what you want to see”, results of interpretations
Contractarianism: we make up morality with an agreed-upon contract, by cooperation.
Decoy effect: (compare to attraction effect): 3rd (inferior alternative) changes preference
Demographic parity (algorithmic fairness measure): low difference between proportion of
privileged subjects in both groups.
Dimensions of variation (XAI property): explanation should reveal where boundaries are
(what is needed to change outcome)
Disparate impact (algorithmic fairness measure): ratio of certain group in privileged &
unprivileged groups should be equal
Dunning-kruger effect (cognitive bias): low-ability people overestimate themselves, while
high-ability people underestimate themselves
Equalized odds (algorithmic fairness measure): low difference between TPR & FPR of both
groups
Equal opportunity (algorithmic fairness measure): TPR (almost) equal groups are treated
similarly.
Exclusion bias (human bias): systematically excluding certain data
Feature relevance: measure relevance of each feature. Loal: feature contributions for
specific prediction; Global: take all predictions into account (explain entire model overall)
Formula of humanity: humans are the end, not the means.
Forward chaining: derive results from facts

, Framing effect (cognitive bias): caused by perspective
Fundamental attribution error (cognitive bias): credit success to yourself but failure to others
Funding bias (human bias): favor interest group / sponsors in study
General (“strong”) AI: AI that can learn and use intelligence autonomously.
Hasty generalization (cognitive bias): generalize too fast, while your experiences are limited
Hindsight bias (cognitive bias): saying “i knew it all along” afterwards
Human bias: human psychological disposition to oversee things.
Hypothetical imperatives: if you desire X, you should do Y
Identifiable victim effect (cognitive bias): people ignore impersonal representations of e.g.
poverty, but will help a specific person
Inertia (status quo bias) (cognitive bias): stick to current situation, fearing change
Jens Gulden: main lecturer of RDS.
Kantianism: morality is constant. morality depends on intentions, not on outcome.
Layer-wise Relevance Propagation (LRP): understand ANN in the background, by
propagating the prediction backwards. Understand which parameters have highest influence.
Local explanations: model agnostic explanation: does not care about internal model, but only
about a specific input instance (local)
Measurement bias (data bias): data inaccurately reflects population, badly recorded.
Metaethics: foundation / origins of ethical principles. Grounding problem: looking for clear
unmoving moral belief.
Model simplification (XAI): simplifying a model, e.g. constructing a DT from ANN.
Moral absolutism (in moral realism: most extreme version of moral realism, moral facts are
unchangeable and universal.
Moral anti-realism (metaethical belief): there are no objective moral facts, they are
expressions of our emotional attitude rather than of the state of the world.
Moral realism (metaethical belief): objective morals exist, independent of human beliefs.
Moral relativism (in moral realism): moral facts are true / false only relative to specific
contexts
Moral subjectivism (in moral anti-realism): morals can be true or false, it is a matter of
preference
Narrow (“weak”) AI: statistically trained AI, works for predefined problems
Observability (XAI property): explanation should explain internal mechanism
Observer bias (human bias): researcher projects his expectations in the research (in
decisions)
Omitted variable bias (data bias): data lacks relevant features
Outgroup homogeneity bias (cognitive bias): people out of your own group seem more alike
Overgeneralization (human bias): generalize too quickly (I have seen 10 swans and they
were all white, therefore black swans do not exist)
Perceptual bias: distorts our perceptions / sensory experience.
Pseudonymization by encryption: pseudonymize data by encrypting sensitive data.
Pseudonymization by tokenization: pseudonymize data by giving each entry an anonymous
ID (token) based on name
Racial / gender bias: resulting from selection bias of training data.
Recall bias: Similar types are labeled consistently, due to human interpretation differences
Rule utilitarianism: maximize utility in the long run
Selection bias (data bias): some data points have more chance to be included, no proper
randomization is used
Soundness (XAI property): explanations should be consistent, reasonable, plausible

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