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A Factory Reset Left the Previous Owner's GPS Coordinates on a Kasa Camera

https://www.vincentschmalbach.com/factory-reset-kasa-camera/
1•vincent_s•1m ago•0 comments

The Galaxy Card Is Samsung's Answer to the Apple Card

https://www.wired.com/story/galaxy-card-is-samsung-answer-to-the-apple-card/
1•divbzero•1m ago•0 comments

Show HN: Use This Instead of 4Chan

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1•squeezes•3m ago•0 comments

How to Build the ChatGPT Typing Effect in Modern JavaScript

https://jsdev.space/howto/chatgpt-typing-effect/
1•javatuts•5m ago•0 comments

9 months building an advanced StarCraft reporting tool with Go and Claude

https://marianogappa.github.io/thoughts/building-an-advanced-starcraft-reporting-tool/
1•maloga•6m ago•0 comments

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https://www.yashthapliyal.com/blog/do-we-just-want-slaves
2•yash1hi•14m ago•0 comments

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https://github.com/razzant/claudexor
2•abstractDL•14m ago•0 comments

Zoom is now Chrome only

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2•asdfsa32•15m ago•2 comments

Bloomberg's Last Stand

https://theterminalist.substack.com/p/bloombergs-last-stand
1•hubraumhugo•16m ago•0 comments

Unity ships a CLI that lets AI agents operate running game projects

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1•ryanmerket•17m ago•0 comments

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https://www.flocksafety.com/industries/places-of-worship
2•maieuticagent•22m ago•0 comments

Looking for contributors – trie based memory efficient LLM runner

https://github.com/oteomamo/SALT
1•omamo•25m ago•0 comments

ShotPlan: Cinematic Video Generation with Learnable Planning Token

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2•ilreb•32m ago•0 comments

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4•vinhnx•32m ago•0 comments

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2•AlecMurphy•34m ago•0 comments

Laura Grego on satellites, nuclear weapons and the battle for our skies

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3•oriettaxx•37m ago•0 comments

AI's solution to 87-year-old riddle takes mathematicians by surprise

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3•vismit2000•37m ago•0 comments

What Becomes Scarce After Intelligence?

https://manasbihani.substack.com/p/every-ai-bet-is-the-same-bet
2•manasb25•39m ago•0 comments

Perverse Polymorphism

https://www.science.org/content/blog-post/perverse-polymorphism
2•bryanrasmussen•40m ago•0 comments

Eliminating Conda/CUDA dependency hell in computational biology pipelines

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Paper Soccer

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2•richcreations•1h ago•0 comments
Open in hackernews

Ontario prison AI assigns black prisoners harsher living conditions

https://breachmedia.ca/black-prisoners-are-assigned-harsher-living-conditions-in-ontario-jails-thanks-to-ai/
9•hn_acker•12h ago

Comments

hn_acker•12h ago
The original title is:

> Black prisoners are assigned harsher living conditions in Ontario jails—thanks to AI

polski-g•12h ago
> The ministry responsible for Ontario’s prisoners agrees. It wrote in internal training documents viewed exclusively by The Breach that “Indigenous and racialized individuals face systemic discrimination in our justice system … As a result, assessments like SAFER would likely contribute to the overrepresentation of Indigenous inmates in maximum security.”

If "Indigenous and racialized individuals" (whatever the fuck that means) are more violent (even in prison), then it would make sense to put them in higher security prisons to lower likelihood of harm to other prisoners and/or guards. But that isn't what's happening, as the race of the individual prisoner is not even inputted into the algorithm, only their behavior is: "SAFER inputs a prisoner’s personal information—including arrests, charges, and disciplinary records—into an algorithm."

hn_acker•11h ago
You're ignoring the garbage-in-garbage-out problem that forms the basis of the article.
polski-g•9h ago
An individual person's previous criminal behavior is not "garbage" for predicting their future behavior -- in fact prior behavior is one of the best predictors for future behavior that we have. Even the Prison Policy Initiative's own data shows this.
hn_acker•8h ago
>> The Security Assessment for Evaluating Risk (SAFER) program has been operating quietly in Ontario’s jails since early 2021. SAFER inputs a prisoner’s personal information—including arrests, charges, and disciplinary records—into an algorithm.

> An individual person's previous criminal behavior is not "garbage" for predicting their future behavior

Just as in your first reply, you're ignoring the main problem posed by the article, that the information getting inputted is not truthful for some individuals and not equally truthful across different individuals. People of different races are not getting the same information put in for the same crimes because a person of one race gets a charge or conviction where a person of another race might not [1]. People of one race tend to get more severe punishments for the same type and level of crime compared to people of another race [2]. ([1] and [2] are linked in TFA.) Disciplinary actions (and thus, disciplinary records) toward detainees (before/no conviction) and inmates (after conviction) by jail and prison staff respectively are arbitrary and subject to in-the-moment biases that might normally get diluted (not always eliminated) during the process between charge and conviction. If all of these data were fed into an algorithm as if equally weighted and equally truthful, it's a given that such an algorithm would produce biased outcomes, even if the algorithm were not the source of the bias. Another problem emphasized in the article is that the public doesn't know how the SAFER algorithm works nor how the government inputs data.

The Breach's investigation suggests that at least one (possibly both) of the following are happening:

1. Black people are getting put in higher level "security" (meaning isolation) than they should be.

2. People of at least one other race are getting put in lower level "security" than they should be.

> in fact prior behavior is one of the best predictors for future behavior that we have. Even the Prison Policy Initiative's own data shows this.

(For clarity: statistical predictions are correlations, not causation.) Here you're overstating how meaningful the correlation is for the purposes of deciding both the dangerousness of a person with multiple convictions and the appropriate different severity of punishments for people with different numbers of convictions [3]:

> An analysis of prior convictions shows that even those with many prior convictions are still unlikely to be rearrested for a new violent crime while on release. Figure 1 divides all those released into four groups based on their number of prior convictions. We then calculate the fraction of each group that was rearrested for a violent crime. About 1.5% of those with no prior record were rearrested, a number that rose only slightly for defendants with one conviction. On the other end of the spectrum, about 2.5% of those with four or more prior convictions were rearrested. Thus, the rearrest rate was not quite twice as high for those with many convictions as for those with none. A single past conviction, though, hardly appears to predict any increased dangerousness. Repeat offenders—those who have four prior convictions—were only arrested for pretrial violent crime in about 1 in 30 instances.

[1] https://www.ohrc.on.ca/en/impact-action-final-report-anti-bl...

[2] https://www.justice.gc.ca/eng/rp-pr/jr/rrbb-bbrr/conclusions...

[3] https://static.prisonpolicy.org/scans/Baradaran_predictingvi...