Free Speech Around the World | EFFector 36.6

EFF update
1 week 4 days ago

Let's gather around the campfire and tell tales of the latest happenings in the fight for privacy and free expression online. Take care in roasting your marshmallows while we share ways to protect your data from political campaigns seeking to target you; seek nominees for our annual EFF Awards; and call for immediate action in the case of activist Alaa Abd El Fattah.

As the fire burns out, know that you can stay up-to-date on these issues with our EFFector newslettter! You can read the full issue here, or subscribe to get the next one in your inbox automatically! You can also listen to the audio version of the newsletter on the Internet Archive, or by clicking the button below:

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EFFECTOR 36.6 - Free Speech Around the World

Since 1990 EFF has published EFFector to help keep readers on the bleeding edge of their digital rights. We know that the intersection of technology, civil liberties, human rights, and the law can be complicated, so EFFector is a great way to stay on top of things. The newsletter is chock full of links to updates, announcements, blog posts, and other stories to help keep readers—and listeners—up to date on the movement to protect online privacy and free expression. 

Thank you to the supporters around the world who make our work possible! If you're not a member yet, join EFF today to help us fight for a brighter digital future.

Christian Romero

【お知らせ】JCJ２４年度総会開く　古川事務局長を再任　オンラインで熱く討議＝編集部

日本ジャーナリストクラブ（JCJ）
1 week 4 days ago
　ＪＣＪは３月31日、オンライン で総会を開催し古川英一事務局長 を再任、24年度の活動のスタート を切った。役員体制では長く代表 委員を務めた清水正文さんが退任 を表明。代表委員は６人となった。◆24年度の役員体制◆【代表委員】白垣詔雄▽隅井孝雄▽中村悟郎 ▽藤森研▽山口昭雄▽吉原功【事務局長】古川英一【同次長】鈴木賀津彦【機関紙編集長】廣瀬功【運営委員】伊東良平▽大場幸 夫▽川田マリ子▽川村高子▽河野 慎二▽須貝道雄▽杉山正隆▽鈴木 賀津彦▽谷岡理香▽橋詰雅博▽廣 瀬功..
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What Can Go Wrong When Police Use AI to Write Reports?

EFF update
1 week 4 days ago

Axon—the makers of widely-used police body cameras and tasers (and that also keeps trying to arm drones)—has a new product: AI that will write police reports for officers. Draft One is a generative large language model machine learning system that reportedly takes audio from body-worn cameras and converts it into a narrative police report that police can then edit and submit after an incident. Axon bills this product as the ultimate time-saver for police departments hoping to get officers out from behind their desks. But this technology could present new issues for those who encounter police, and especially those marginalized communities already subject to a disproportionate share of police interactions in the United States.

Responsibility and the Codification of (Intended or Otherwise) Inaccuracies

We’ve seen it before. Grainy and shaky police body-worn camera video in which an arresting officer shouts, “Stop resisting!” This phrase can lead to greater use of force by officers or come with enhanced criminal charges.  Sometimes, these shouts may be justified. But as we’ve seen time and again, the narrative of someone resisting arrest may be a misrepresentation. Integrating AI into narratives of police encounters might make an already complicated system even more ripe for abuse.

If the officer says aloud in a body camera video, “the suspect has a gun” how would that translate into the software’s narrative final product?

The public should be skeptical of a language algorithm's ability to accurately process and distinguish between the wide range of languages, dialects, vernacular, idioms and slang people use. As we've learned from watching content moderation develop online, software may have a passable ability to capture words, but it often struggles with content and meaning. In an often tense setting such as a traffic stop, AI mistaking a metaphorical statement for a literal claim could fundamentally change how a police report is interpreted.

Moreover, as with all so-called artificial intelligence taking over consequential tasks and decision-making, the technology has the power to obscure human agency. Police officers who deliberately speak with mistruths or exaggerations to shape the narrative available in body camera footage now have even more of a veneer of plausible deniability with AI-generated police reports. If police were to be caught in a lie concerning what’s in the report, an officer might be able to say that they did not lie: the AI simply mistranscribed what was happening in the chaotic video.

It’s also unclear how this technology will work in action. If the officer says aloud in a body camera video, “the suspect has a gun” how would that translate into the software’s narrative final product? Would it interpret that by saying “I [the officer] saw the suspect produce a weapon” or “The suspect was armed”? Or would it just report what the officer said: “I [the officer] said aloud that the suspect has a gun”? Interpretation matters, and the differences between them could have catastrophic consequences for defendants in court.

Review, Transparency, and Audits

The issue of review, auditing, and transparency raises a number of questions. Although Draft One allows officers to edit reports, how will it ensure that officers are adequately reviewing for accuracy rather than rubber-stamping the AI-generated version? After all, police have been known to arrest people based on the results of a match by face recognition technology without any followup investigation—contrary to vendors’ insistence that such results should be used as an investigative lead and not a positive identification.

Moreover, if the AI-generated report is incorrect, can we trust police will contradict that version of events if it's in their interest to maintain inaccuracies? On the flip side, might AI report writing go the way of AI-enhanced body cameras? In other words, if the report consistently produces a narrative from audio that police do not like, will they edit it, scrap it, or discontinue using the software altogether?

And what of external reviewers’ ability to access these reports? Given police departments’ overly intense secrecy, combined with a frequent failure to comply with public records laws, how can the public, or any external agency, be able to independently verify or audit these AI-assisted reports? And how will external reviewers know which portions of the report are generated by AI vs. a human?

Police reports, skewed and biased as they often are, codify the police department’s memory. They reveal not necessarily what happened during a specific incident, but what police imagined to have happened, in good faith or not. Policing, with its legal power to kill, detain, or ultimately deny people’s freedom, is too powerful an institution to outsource its memory-making to technologies in a way that makes officers immune to critique, transparency, or accountability.

Matthew Guariglia