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Manuel Gomez-Rodriguez
Human-centric machine learning at the Max Planck Institute for Software Systems.
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Manuel Gomez-Rodriguez 27. sij
If you are at at EPFL, don't miss the AI & Education track . Thrilled to join a great set of speakers to talk about the Memorize algorithm () and our on-going large-scale interventional experiment on personalized learning.
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Manuel Gomez-Rodriguez 24. sij
My take on "Human-Centric Machine Learning: Feedback loops, Human-AI Collaboration and Strategic Behavior": . A talk/lecture on some of my recent work. Tons of opportunities for future work!
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Manuel Gomez-Rodriguez 24. sij
Odgovor korisniku/ci @autreche
Finally, we experiment with synthetic and real lending data to both illustrate our theoretical findings and show that, under strategic behavior, the policies our algorithm find do much better than deterministic threshold rules (n/n)
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Manuel Gomez-Rodriguez 24. sij
Odgovor korisniku/ci @autreche
Moreover, under no assumptions on the cost individuals pay to change their features, we develop an iterative search algorithm that is guaranteed to find locally optimal decision policies also in polynomial time (5/n)
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Manuel Gomez-Rodriguez 24. sij
Odgovor korisniku/ci @autreche
But, if the cost individuals pay to change their features satisfies a monotonicity assumption, we can narrow down the search for the optimal policy to a family of decision policies with desirable properties. This allows for a polynomial time heuristic search algorithm (4/n)
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Manuel Gomez-Rodriguez 24. sij
Odgovor korisniku/ci @autreche
Then, we first show that, in general, we cannot expect to find optimal decision policies in polynomial time under strategic behavior and there are cases in which deterministic policies are suboptimal (3/n)
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Manuel Gomez-Rodriguez 24. sij
Odgovor korisniku/ci @autreche
Individuals may use knowledge, gained by transparency, to invest effort strategically in order to receive a beneficial decision. In our work, we show that this strategic investment of effort by individuals can be cast as an optimal transport problem at a population level (2/n)
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Manuel Gomez-Rodriguez 24. sij
Happy to share our (updated) work on optimal decision making under strategic behavior , which lies in the emerging (super exciting) field of strategic machine learning. This work aims to find decision policies that lead individuals to self-improvement (1/n)
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Manuel Gomez-Rodriguez 24. sij
Odgovor korisniku/ci @PpM_O @alex_peys
We just posted in arxiv the new version of our work: , where we significantly expand our results both in terms of theory and algorithms. In particular, we have a new section (and appendix) focusing on costs that satisfy a natural outcome monotonic property.
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Manuel Gomez-Rodriguez 23. sij
Odgovor korisniku/ci @martinvars
;-) Since a few weeks you just keep insisting on undermining the importance of climate change when highlighting another of the world’s problems. If climate change is not your fight, it is fine but, in this particular issue, climate does matter!
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Manuel Gomez-Rodriguez 21. sij
Odgovor korisniku/ci @neu_rips @vicen__gomez i 3 ostali
I agree with Gergely here, it is a piece of crap and not sure why many ppl started using it.
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Manuel Gomez-Rodriguez proslijedio/la je tweet
Niki Kilbertus 9. sij
There's an important difference between decisions and predictions, especially when it comes to fairness. Check out our paper "Fair Decisions Despite Imperfect Predictions" with Isabel Valera
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Manuel Gomez-Rodriguez 7. sij
Odgovor korisniku/ci @markvanderwilk
Congrats Mark!!
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Manuel Gomez-Rodriguez 8. pro
Odgovor korisniku/ci @Aaroth
Pretty surprising results, thx for sharing! You may like to check out , where we explore the idea of decision policies/data gathering under selective labels and the suboptimality of decision threshold rules in that context.
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Manuel Gomez-Rodriguez 7. pro
Thoughtful, nuanced, and well written article on algorithmic bias vs human bias:
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Manuel Gomez-Rodriguez 28. stu
If you go to , consider attending our workshop on human-centric ML , we have a terrific set of invited speakers (, Deirdre Mulligan, , Finale Doshi-Velez, ), contributed papers and panels!
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Manuel Gomez-Rodriguez 20. stu
Odgovor korisniku/ci @random_walker
I agree with you that many startups that claim to use AI to predict social outcomes may oversell. In that context, I believe it is important to raise awareness and be skeptical. However, judging scientific progress on the topic based on what startups claim seems unfair n/n
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Manuel Gomez-Rodriguez 20. stu
Odgovor korisniku/ci @random_walker
Moreover, there is a lot of peer-reviewed (and on-going) work on the influence that algorithmic predictions may have on those experts, which tweet threads like yous misrepresent greatly, e.g., , (5/n)
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Manuel Gomez-Rodriguez 20. stu
Odgovor korisniku/ci @random_walker
The use of machine learning predictive models does not necessarily means replacing an expert but informing one who will take the final decision. I agree it is super important to develop mechanisms (e.g., explanations) to help that expert contextualize/weigh such predictions (4/n)
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Manuel Gomez-Rodriguez 20. stu
Odgovor korisniku/ci @random_walker
You also argue that no social outcome can be predicted because no one can predict the future. You say that is common sense. Appealing to common sense is not a scientific argument, this is specially surprising coming from a Princeton professor (3/n)
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