The Toxicity of “Autism Parent” Memoirs

But most autism parenting stories are not positive, or about doing our best to understand what our autistic kids need and deserve. In recent “autism parent” memoirs like Judith Newman’s To Siri With Love and Whitney Ellenby’s Autism Uncensored, the authors hang their kids out to dry for being autistic and having intensely legitimate autistic needs, while centering the parent-narrators as victims of that disembodied demon, “autism.” That these stories keep getting green-lit is both an embarrassment and a tragedy.

Thinking Person’s Guide to Autism: The Toxicity of “Autism Parent” Memoirs

I also discussed this in A Tale of Two Mothers back in 2007. I ended that post with the following:

The events in the book take place in the late ‘90s and early ‘00s. Sadly, things probably haven’t changed much in the past few years. (I’ve hear that evidence of this can be found in Jenny McCarthy’s recent book about her autistic son, but I’ve not been able to get myself to read it.)

Disappointing, an embarrassment and tragedy indeed, that these types of books are still the ones that people want to write. And, perhaps worse, to read.

Innovating in Complexity (Part I): Why Most Roadmaps Lead Straight to the Graveyard

Roadmaps for change are thus mere hypotheses, in both journey and destination. They represent the aspirations, desires, and hopes of their creators while putting forward a step-by-step action plan for how these objectives might be achieved. Herein lies the essence of the Roadmap Fallacy: a tool originally developed to represent existing realities doesn’t work well as a mental model for creating new realities.

Source: Innovating in Complexity (Part I): Why Most Roadmaps Lead Straight to the Graveyard

The Hidden Costs of Automated Thinking

In the past, intellectual debt has been confined to a few areas amenable to trial-and-error discovery, such as medicine. But that may be changing, as new techniques in artificial intelligence—specifically, machine learning—increase our collective intellectual credit line. Machine-learning systems work by identifying patterns in oceans of data…. And yet, most machine-learning systems don’t uncover causal mechanisms. They are statistical-correlation engines. They can’t explain why they think some patients are more likely to die, because they don’t “think” in any colloquial sense of the word—they only answer. As we begin to integrate their insights into our lives, we will, collectively, begin to rack up more and more intellectual debt.

Jonathan Zittrain – The Hidden Costs of Automated Thinking