Wrote a brief reflection on poor conceptualizations in AI work and why they matter, something I believe is so startlingly neglected. TL;DR - Poor conceptual foundations can severely undermine the credibility and reliability of knowledge claims.
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Alexandra Olteanu
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Researcher with public evidence across Responsible AI, AI research.
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Ethical/Responsible AI. Rigor in AI. Grumpy eastern european in north america. Lovingly nitpicky.
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Back-to-basics: on poor conceptualizations in AI work - (Un)rigorous AI rigor-in-ai.leaflet.pub
Back-to-basics: so what do evaluations do - (Un)rigorous AI rigor-in-ai.leaflet.pub
Back-to-basics: unobservable constructs and their ‘surplus meaning’ - (Un)rigorous AI rigor-in-ai.leaflet.pub
Articles & links
So what do AI evaluations do or should do? Wrote a brief reflection about prototypical questions evaluations often try to answer and why it is useful to have clarity about what exactly one is trying to learn about some phenomenon of interest. rigor-in-ai.leaflet.pub/3ms4ax4sojs2j
Wrote another brief post motivated by a reflection on how in AI research folks might not fully appreciate the care that working with unobservable constructs requires. TL;DR -- Because unobservable constructs hold ‘surplus meaning,’ your metric is not your construct.
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