Google Scholar has over 2000 occurrences of "algorithmic bias"
<https://scholar.google.com/scholar?q=%22algorithmic+bias%22> -- the idiom
now apparently used to prevent machine learning from learning things that
violate norms.

35 of them
<https://scholar.google.com/scholar?q=%22algorithmic+bias%22++%22solomonoff%22+OR+%22minimum+description+length%22+OR+%22kolmogorov+complexity%22+OR+%22algorithmic+information%22++OR+%22algorithmic+probability%22>
refer to  "solomonoff" OR "minimum description length" OR "kolmogorov
complexity" OR "algorithmic information"  OR "algorithmic probability"



On Sat, Feb 15, 2020 at 3:54 PM James Bowery <[email protected]> wrote:

> On Sat, Feb 15, 2020 at 2:48 PM Robert Levy <[email protected]> wrote:
>
>> Are the mods just going to ignore James Bowery?
>>
>
> Since algorithmically correcting "bias" is now seen as a central
> responsibility of network effect content monopolies like Google, Youtube,
> Twitter, Facebook, etc. rigorously measuring a dataset's "bias" is even
> more urgent than is measuring "intelligence" or even "friendliness".
>
> Exactly _how_ urgent?
>
> Consider this:
>
> These content monopolies are intent on avoiding "a repeat of the 2016
> election", whatever that means.  One thing is for certain:  Claims that
> they are attempting to provide an unbiased view of the world via their
> machine learning algorithms in the run up to the 2020 election is viewed
> with a great deal of suspicion by people wielding on the order of 400 guns
> in the US alone.
>
> That's _exactly_ how urgent.
>
> Since we're stuck with some form of "prior" (speed prior, space prior,
> etc.), and any prior will introduce bias in some sense, it seems the more
> minimal that prior, the less bias it introduces to a minimum description
> length of all available data.
>
> So why aren't all these content giants striving to create the largest
> database of diverse, longitudinal social measures that their hardware and
> human resources can support, and losslessly compressing it, so as to have
> an unbiased platform upon which to measure "bias" in new data being added
> to their content stores?
>

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