Sources of Bias and Explanation
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The document discusses sources of bias in algorithms, particularly in machine learning and AI systems, highlighting the dangers of gender and ethnic biases. It emphasizes the need for algorithmic transparency and mechanisms to mitigate bias, such as 'query-by-browsing' and white-box methods. Ultimately, the paper advocates for a better understanding and control of biases in AI to promote fairness and accountability in societal impacts.
Sources of Bias and Explanation
- 1. sources of bias and explanation Alan Dix Computational Foundry Swansea http://alandix.com/academic/talks/PIT-2019-bias-and-explanation/
- 2. Tiree Tiree Tech Wave 3-7 October Computational Foundry Swansea University
- 7. types of algorithms … rules and regulations ordinary code classic AI machine learning and neural nets increasing opacity
- 8. when things go wrong – deliberate misuse hacking bad use cyberwarfare – Stuxnet, etc. autonomous weapons
- 9. when things go wrong – well meaning accidents autonomous car crashes unintended consequences bias (gender, ethnicity) disproportionate social effects https://www.bbc.co.uk/sounds/play/m00017s4 (report @ 1:41:00 in)
- 10. 25 years back …
- 12. warns of the danger of gender and ethnic bias in black-box machine learning systems gives example: database queries using ID3 offers (partial) solution: Query-by-Browsing and even some broader heuristics inter alia …
- 13. yes, 25 years ago!
- 16. Query by Browsing user chooses records of interest tick for those wanted cross for those not wanted system infers query web version uses rule induction variant of Quinlan’s ID3 www.meandeviation.com/qbb
- 17. Query by Browsing what it looks like user asks system to make a query system infers SQL query query results highlighted
- 18. Query by Browsing dual representation query (intensional) for precision listing (extensional) for understanding
- 19. Query by Browsing – how it works examples machine learning SQL query cond cond decision tree
- 21. it is not just about being accurate not just right but also upright
- 22. learning past bias in training data training data learnt rules objective function societal bias in goals ‘best’ may be biased
- 23. learning past bias in training data training data learnt rules objective function societal bias in goals ‘best’ may be biased
- 25. mimicking human behaviour and choices
- 26. learning past bias in training data training data learnt rules objective function societal bias in goals ‘best’ may be biased
- 27. pandering to human bias (effective outcomes?) • dating sites using ethnicity (CHI 2018!) • young pretty waitresses sell more drinks • Trump (reportedly) hiding black employees at casino when certain rich customers arrived • BBC (& others) paying male presenters more because they are more popular
- 28. ‘good’ business but is it good?
- 29. learning past bias in training data training data learnt rules objective function societal bias in goals ‘best’ may be biased
- 30. reinforcing societal/cultural norms at school boys more likely to study STEM subjects girls more likely to study humanities so, on average, with no other information gender is an (albeit poor) predictor of communication skills and engineering knowledge
- 31. as a society we choose to use other (and better) predictors
- 32. innate (but largely irrelevant) differences men are (on average) larger and stronger so gender is a Bayesian predictor of strength this may explain gender differences in some jobs but … it does NOT justify employment discrimination
- 33. bias is not about algorithmic correctness it is about social choice
- 34. the choice of input features often critical in creating or controlling bias more data not always better!
- 35. Note: human reasoning is poor at ignoring low quality cues even when we have better ones
- 36. algorithms may be better?
- 37. however … not sufficient to remove explicit indicators: gender/ethnicity/disability/religion potential correlating factors e.g. clothing algorithms need to actively avoid discrimination
- 38. and how do we know our algorithms are OK?
- 39. Not just bias safety – e.g. autonomous cars democracy – e.g. social media, fake news health and well being – e.g. soft-drink adverts social issues – e.g. credit ratings
- 40. we need to ask Why? algorithmic transparency c.f. court judgment
- 42. an AIX Kitbag AI explainability how to make sense of black-box machine-learning algorithms
- 43. crucial insight … human–human explanations rarely utterly precise or reproducible but are sufficient to inspire confidence and trust
- 44. white-box black-box grey-box creating scructable internal representations analysing and understanding from the outside peeking within understanding internal representations
- 46. but … this was all evident 25 years ago why didn’t I do more? if it is important not sufficient to publish you need to transform into publicity and policy
- 48. white-box methods creating scructable internal representations
- 49. WB0. choose a white box classifier! training set scrutable rules white-box algorithm unseen data white-box classifier outputs
- 50. WB1. black-box generation of white box classifier training set scrutable rules black-box algorithm unseen data white-box classifier outputs
- 51. WB2. Adversarial examples for white-box learning case-base of behaviour scrutable rules black-box adversarial learning white-box learning
- 52. WB3. Simplification of rule set scrutable rules black-box learning training set inscrutable rules tweak
- 53. black-box methods analysing and understanding from the outside
- 54. BB1. exploration analysis for human visualisation black-box learning training set inscrutable rules lots of examples black-box classifier visualise input-output
- 55. BB2. perturbation/exploration analysis for key feature detection black-box learning inscrutable rules randomly vary feature values black-box classifier hotspot visualisation
- 56. BB3. perturbation analysis for central and boundary cases lots of examples black-box classifier central and boundary cases user visualisation white-box learning
- 57. BB3. close up central cases perturbations do not change class boundary cases small perturbations change class penumbra larger perturbations change class
- 58. BB4. black-box oracle – white-box learning input examples black-box classifier scrutable rules white-box learning input–output pairs as training set output classes
- 60. GB0a. sensitivity analysis – weights perturb parameters in the inscrutable rules lots of examples black-box classifier hotspot analysis on parameters
- 61. GB0b. sensitivity analysis – activation input example black-box classifier (low level) extract intermediate activation black-box classifier (high level) perturb activations hotspot analysis of nodes
- 62. GB0c. sensitivity analysis – algorithmic apply black-box algorithm inverse algorithm
- 63. GB1. high level model generation input examples black-box classifier extract intermediate activation scrutable rules white-box learning activations with output class as training set output classes
- 64. GB2. Clustering and comprehension of low level input examples black-box classifier extract intermediate activation clusters various algorithms activations as input MDS SOM
- 65. GB3. triad distinctions input examples black-box classifier (low level) A B C hotspot analysis of nodes compare
- 66. GB4. apply generatively output to input activation to input output to activation between layers