I found myself thinking about something I had never really questioned before. Where does an AI model's decision actually begin?
My first instinct was to say it starts when the model receives an input. After spending time reading about SolidML in @OpenGradient , I don't think it's that simple anymore.
What I've noticed is that SolidML lets smart contracts perform preprocessing on chain before inference. Operations like normalization, standardization, mean, median, variance, and correlation can all be executed through a preprocessing precompile. At first, I thought this was mainly about moving computation on chain.
The more I thought about it, the more I realized the real story is different.
The calculations themselves can be verified, but the choices behind them cannot. A developer still decides which dataset to use, which variables matter, how the data should be transformed, and what time window best represents the problem. The math may be correct, yet the model can still receive an incomplete picture.
That distinction keeps pulling me back. In my view, OpenGradient is trying t0 make preprocessing transparent instead of invisible, but transparency is not the same as judgment. Verifiable computation strengthens trust, yet it does not remove the responsibility behind the decisions that shape the input.
I am also keeping in mind that SolidML and on chain ML inference are still part of the deprecated alpha testnet, with support for the primary testnet still under development.
My take is that trustworthy AI will need both verifiable infrastructure and thoughtful human decisions.
Does verifiable preprocessing make AI more trustworthy, or does it simply make our assumptions easier to examine?
#opg $OPG
My first instinct was to say it starts when the model receives an input. After spending time reading about SolidML in @OpenGradient , I don't think it's that simple anymore.
What I've noticed is that SolidML lets smart contracts perform preprocessing on chain before inference. Operations like normalization, standardization, mean, median, variance, and correlation can all be executed through a preprocessing precompile. At first, I thought this was mainly about moving computation on chain.
The more I thought about it, the more I realized the real story is different.
The calculations themselves can be verified, but the choices behind them cannot. A developer still decides which dataset to use, which variables matter, how the data should be transformed, and what time window best represents the problem. The math may be correct, yet the model can still receive an incomplete picture.
That distinction keeps pulling me back. In my view, OpenGradient is trying t0 make preprocessing transparent instead of invisible, but transparency is not the same as judgment. Verifiable computation strengthens trust, yet it does not remove the responsibility behind the decisions that shape the input.
I am also keeping in mind that SolidML and on chain ML inference are still part of the deprecated alpha testnet, with support for the primary testnet still under development.
My take is that trustworthy AI will need both verifiable infrastructure and thoughtful human decisions.
Does verifiable preprocessing make AI more trustworthy, or does it simply make our assumptions easier to examine?
#opg $OPG