Artificial Unintelligence by @merbroussard explores the really important topic "algorithmic accountability reporting"
Follow Meredith Broussard on twitter @merbroussard
Highly recommended reading, and if interested also pick up Weapons of Math Destruction by Cathy O’Neill
How computers misunderstand the world. A great and very accessible book on why understanding the inner workings and outer limits of technology help us appreciate that we should never assume that computers will always get it right. It explores the limits of artificial intelligence (AI) and techno-solutionism, furthermore showing how we can easily replicate existing structural inequalities which is not an achievement.
This beautifully written book by Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally; hiring, driving, paying bills, even choosing romantic partners, that we have stopped demanding that our technology actually work.
Meredith, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. Making a case that technology is always the solution the book explores that it's just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming.
Any book that say computer are not brains is worth reading, and even adds that most of the ideals of AI are just not true. Hooray! Would love for her to have explored federate data for AI, but that may be lost on many, but there is some great insights shared into the critically important topic of bias (data and people) As you read you can pick up a frustration that much of reporting equates compute (power) = intelligence and Meredith speaks to this very clearly.
The quote for me “just because we have open, don’t mean we cannot get corruption.” I love that Meredith addresses the issues of data bias, algorithm bias and what it really means and how it occurs. Just asking the machine to replicate faster what we have is not an improvement and she shows how our we can all to easily replicates existing structural inequalities rather than solve them.
The book grounds the sociological analysis in an accessible technical account of the key computational processes involved in machine learning. Machines will never actually be intelligent, in the sense of having consciousness, sentience, common sense or imagination.
It breaks down the unrealistic belief that technology, #AI, can solve everything. Humans and our society are complex, random and unpredictable; rendering compute (maths and algorithms) cannot be the solution. However human-centric design, human-machine collaboration, ‘human-in-the-loop’ systems are our next step.
She picks up on my favourite topic (governance) and specifically the emerging field of ‘algorithmic accountability reporting’: the use of investigative code to check how decision-making algorithms work.