tag:blogger.com,1999:blog-1254315679163990153.post5096756606441886495..comments2023-09-09T09:36:50.321+01:00Comments on Systems Thinking for Demanding Change: The Transparency of AlgorithmsRichard Veryardhttp://www.blogger.com/profile/04499123397533975655noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-1254315679163990153.post-1938718936595199272016-11-02T10:20:10.280+00:002016-11-02T10:20:10.280+00:00Thanks Scribe
As I see it, learning to reflect ex...Thanks Scribe<br /><br />As I see it, learning to reflect existing biases is still a form of learning. Where would our education system be without industrial quantities of "received wisdom"?<br /><br />Unfortunately, the concept of "received wisdom" also applies to many people's understanding of statistics. See for example David Colquhoun's argument that <a href="https://aeon.co/essays/it-s-time-for-science-to-abandon-the-term-statistically-significant" rel="nofollow">It’s time for science to abandon the term ‘statistically significant’</a> (Aeon Essays, October 2016).<br /><br />So in addition to an understanding of stats, wisdom calls for an understanding of epistemology and ethics. Given that this understanding seems to be in short supply, even among the PPE graduates who dominate the political elites in the UK, not to speak of the engineers who dominate Silicon Valley, I think we can safely say that algorithms *will* be misused.Richard Veryardhttps://www.blogger.com/profile/04499123397533975655noreply@blogger.comtag:blogger.com,1999:blog-1254315679163990153.post-63163060209515329282016-11-01T06:59:03.848+00:002016-11-01T06:59:03.848+00:00Hi Richard,
I found the quote by Thrun at the en...Hi Richard, <br /><br />I found the quote by Thrun at the end quite fascinating - an alternative interpretation for it might be that algorithms don't learn as much as *reflect* existing biases (which may be thought of a meatspace algorithm anyway, perhaps), and this reflection can become a "truth" of the existing world, rather than a new, or reinforced truth. <br /><br />The question of transparency becomes really interesting then - if we can find out what the factors are that lead to decisions being made or suggested, are we ready to face up to this as a "clearer" (or, at least, harder to argue against) view of how society already operates? <br /><br />Sadly I suspect we hit a continuing divide between those who understand stats and those who don't here. (A growing inequality/concern generally.) Will we be wise enough to know the difference between factors which are inherent to the world (eg biological tendencies) and factors which we ascribe to the world? Or will algorithms be misused left, right and centre to produce seemingly clever arguments for increased segregation (or worse)?Scribehttps://www.blogger.com/profile/08757616056135886893noreply@blogger.com