Applied Machine Learning, Lecture 11: Ethical and legal considerations; domain effects and domain adaptation

Transcription

1 Applied Machine Learning, Lecture 11: Ethical and legal considerations; domain effects and domain adaptation Richard Johansson including some slides borrowed from Barbara Plank

2 overview introduction bias and fairness explainability domain effects and domain adaptation

3 automatic prediction systems are becoming more common [source]

4 example: wild claims and sloppy science Eriksson and Lacerda (2007) Charlatanry in forensic speech science: A problem to be taken seriously The LVA uses a patented and unique technology to detect Brain activity finger prints using the voice as a medium to the brain and analyzes the complete emotional structure of your subject. [source] New Scientist, 2016 Controversial software claims to tell personality from your face

5 automatic prediction systems in society

6 take-home message from this lecture your ethical position depends on your ideology the purpose here is not to teach you what is right or wrong the idea is to raise your awareness of how machine learning models can affect people in unintended (?) ways and what your company or the law may require of you

7 this lecture bias, discrimination, fairness interpretability, explanations domain effects, domain adaptation

8 overview introduction bias and fairness explainability domain effects and domain adaptation

9 can predictive systems discriminate unfairly? when predictive systems are more widely applied in situations where they affect people s lives, they may run into troubles concerning anti-discrimination laws there is no consensus about how to define and even less how to deal with the problem

10 the legal situation anti-discrimination laws in several countries prohibit unfair treatment of people based on protected attributes (e.g. gender, race) these laws often evaluate the fairness of a decision making process by means of two distinct notions: disparate treatment: if its decisions are (partly) based on the subject s protected attribute information disparate impact: if its outcomes disproportionately hurt (or, benefit) people with certain sensitive attribute values are these ideas in conflict? how can they be operationalized?

11 a sample of attempts to define fairness assume we are trying to predict a variable y, we have a predictor ŷ; there is a protected attribute a attempt 1: ŷ and a should be independent doesn t work! leads to inverse discrimination attempt 2: Hardt et al. (2016) Equality of Opportunity in Supervised Learning define the equality of opportunity as that the recall should be equal for all groups P(ŷ = positive y = positive) attempt 3: Zafar et al. (2017) Fairness Constraints: Mechanisms for Fair Classification encode disparate impact by setting a threshold on the covariance between ŷ and a

12 another example: the COMPAS system [source] [data]

13 COMPAS (continued) [source]

14 COMPAS (continued) non-offending blacks get higher scores NB: race is not a feature in the model so the recall for the positive class isn t equal for the groups

15 can we achive fairness in ML models? Kamiran and Calders (2009) Classifying without discriminating try to find the least intrusive modification of the training set to achieve fairness Kamiran and Calders (2010) Classification with No Discrimination by Preferential Sampling uses a preferential sampling scheme

16 tailoring learning algorithms (example) Zafar et al. (2017) Fairness Constraints: Mechanisms for Fair Classification encode disparate impact by setting a threshold on the covariance between ŷ and a they propose a modified logistic regression and SVC objectives:

17 summary: state of the art no well-established solution at the moment no consensus about how to measure discrimintation Žliobaitė (2015) A survey on measuring indirect discrimination in machine learning fairness in classifiers is a very active research area e.g. the FATML workshop

18 collecting training data [source]

19 collecting training data [source]

20 feedback loops in data gathering? Goel et al. (2017) Combatting Police Discrimination in the Age of Big Data argue that automatic classification can reduce the number of unnecessary searches by police ( stop and frisk ) but how does that affect the collection of training data?

21 when machine learning reinforces stereotypes ML model can pick up subtle biases from the training set including various stereotypes images, text,...

22 bias in word embeddings (1) see how-to-make-a-racist-ai-without-really-trying/ see also Bolukbasi et al. (2016) Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

23 bias in word embeddings (2) [source]

24 another example

25 overview introduction bias and fairness explainability domain effects and domain adaptation

26 can we trust the output of a machine learning model? [source]

27 can we understand what the model is doing? [source]

28 what types of models are interpretable? linear regression (least squares, ridge,... )? linear classifier (LR, SVC, perceptron,... )? decision tree classifier? neural network?

29 does it help if the model is interpretable? what do you think?

30 understanding when the predictor makes no sense [source]

31 Motivation: Predicting Pneumonia Risk Study (mid-90 s) LOW Risk: outpatient: antibiotics, call if not feeling better HIGH Risk: admit to hospital ( 10% of pneumonia patients die) One goal was to compare various ML methods: logistic regression rule-based learning k-nearest neighbor neural nets Bayesian methods hierarchical mixtures of experts... Most accurate ML method: multitask neural nets Safe to use neural nets on patients? No we used logistic regression instead... Why??? Rich Caruana (Microsoft Research) FAT/ML 2017: Intelligible Models August 14, / 41

32 Motivation: Predicting Pneumonia Risk Study (mid-90 s) RBL learned rule: HasAsthma(x) => LessRisk(x) True pattern in data: asthmatics presenting with pneumonia considered very high risk receive agressive treatment and often admitted to ICU history of asthma also means they often go to healthcare sooner treatment lowers risk of death compared to general population If RBL learned asthma is good for you, NN probably did, too if we use NN for admission decision, could hurt asthmatics Key to discovering HasAsthma(x)... was intelligibility of rules even if we can remove asthma problem from neural net, what other bad patterns don t we know about that RBL missed? Rich Caruana (Microsoft Research) FAT/ML 2017: Intelligible Models August 14, / 41

33 the LIME algorithm [source] Ribeiro et al. (2016) Why Should I Trust You? Explaining the Predictions of Any Classifier; Python code:

34 legal right to an explanation some legal systems define a right to explanation of automatic decisions that significantly affect individuals Credit bureau X reports that you declared bankruptcy last year; this is the main factor in considering you too likely to default, and thus we will not give you the loan you applied for. for instance, credit scoring regulation in the U.S. gives the right to explanation: (2) Statement of specific reasons. The statement of reasons for adverse action required by paragraph (a)(2)(i) of this section must be specific and indicate the principal reason(s) for the adverse action. Statements that the adverse action was based on the creditor s internal standards or policies or that the applicant, joint applicant, or similar party failed to achieve a qualifying score on the creditor s credit scoring system are insufficient.

35 coming soon: the GDPR

36 GDPR and the right to explanation article 22 of the GDPR: the data subject shall have the right not to be subjected to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her [with some exceptions] article 13 also states that data subjects have the right to explanation of the logic involved WP 29: as a rule, there is a prohibition on fully automated individual decision-making, including profiling that has a legal or similarly significant effect

37 explainable machine learning explainable ML is an active area of research for instance, the Explainable Artificial Intelligence workshop meetings/ijcai17-xai/ is likely to become more important, because of the growing role of ML in society and the attention from regulators

38 overview introduction bias and fairness explainability domain effects and domain adaptation

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42 in vision VisDA2017: Visual Domain Adaptation Challenge

43 what is the effect of domain differences? [source]

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61 Friday s guest lecture Daniel Langkilde, Annotell real-world annotation of training data