These are my notes from research papers I read. Each page’s title is also a link to the abstract or PDF.

Better, Nicer, Clearer, Fairer: a critical assessment of the movement for ethical artificial intelligence and machine learning

I will present this paper in the FATE (fairness, accountability, transparency, ethics) reading group tomorrow (2023-10-25). You can view the slides I’ll use here. There are unresolved tensions in the algorithmic ethics world. Here are two examples: Is inclusion always good? Gebru: “you can’t have ethical A.I. that’s not inclusive… [a]nd whoever is creating the technology is setting the standards” Nelson: “… I struggle to understand why we want to make black communities more cognizant in facial recognition systems that are disproportionately used for surveillance.
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InstructEval: systematic evaluation of instruction selection methods

This was a paper I presented about in Bang Liu’s research group meeting on 2023-09-25. You can view the slides I used here.

"Low-resource" text classification: a parameter-free classification method with compressors

I presented this paper in Bang Liu’s research group meeting on 2023-07-24. You can view the slides I used here. It seems like the authors made a mistake that inflated the scores for the multilingual experiments, according to Ken Schutte.

Artificial intelligence, values, and alignment

I presented this paper in Bang Liu’s research group meeting in two installments on 2023-02-20 and 2023-02-27, and also in Irina Rish’s scaling and alignment course (IFT6760A) on 2023-03-07. You can view the slides I used here.The thumbnail for this post was generated with stable diffusion! See the alt text for details. Behind each vision for ethically-aligned AI sits a deeper question. How are we to decide which principles or objectives to encode in AI—and who has the right to make these decisions—given that we live in a pluralistic world that is full of competing conceptions of value?
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Unsolved problems in ML safety

This was a paper we presented about in Irina Rish’s neural scaling laws course (IFT6760A) in winter 2023. You can view the slides we used here, and the recording here (or my backup here).