Ml-reading-group April 2024

ml-reading-group@lists.saarland-informatics-campus.de

2 participants
2 discussions

[Cancelled RG] Worker's day --> Workers don't work
by Adrián Javaloy 15 May '24

15 May '24

Dear all, As you all are aware, this Wednesday is Worker's day and therefore a public holiday in Saarland. Therefore, we will *cancel this week's reading group* and will *resume it in two weeks* with Krikamol as speaker, talking about Credal Networks. I hope you have a good mid-week rest, and that you use the occasion to *not work* *at all*. Best regards, Adrián

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Not So Fair: The Impact of Presumably Fair Machine Learning Models
by Adrián Javaloy 16 Apr '24

16 Apr '24

Dear ML reading group, This week we will have Deborah presenting the paper *Not So Fair: The Impact of Presumably Fair Machine Learning Models* (paper here <https://dl.acm.org/doi/10.1145/3600211.3604699>). The reading group will take place on *Wednesday at 2:30pm *as usual, and this time we will host physically at *room 0.01 of building E1.7 @ UdS*. For those attending remotely, you can find the Zoom link in the reading group spreadsheet <https://docs.google.com/spreadsheets/d/1vtgEezBqS4d_ACPt-emK2NT52x7nofX9jxg…>. *Abstract:* When bias mitigation methods are applied to make fairer machine learning models in fairness-related classification settings, there is an assumption that the disadvantaged group should be better off than if no mitigation method was applied. However, this is a potentially dangerous assumption because a “fair” model outcome does not automatically imply a positive impact for a disadvantaged individual—they could still be negatively impacted. Modeling and accounting for those impacts is key to ensure that mitigated models are not unintentionally harming individuals; we investigate if mitigated models can still negatively impact disadvantaged individuals and what conditions affect those impacts in a loan repayment example. Our results show that most mitigated models negatively impact disadvantaged group members in comparison to the unmitigated models. The domain-dependent impacts of model outcomes should help drive future bias mitigation method development. Cheers, Adrián

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Ml-reading-group April 2024