Not only are they using AI before they've properly assessed them, they also end up using Copilot which must be one of the worse AIs currently available, probably because of existing Microsoft relations. And on top of all that, they hope to be able to rely on "Please review the outputs" which obviously isn't an actual solution here, of course people will get complacent and throw stuff over the wall whenever they can.
Everyone I talk to (including outside of tech) is going through this phase at their companies. It’s not working.
Checking the output seems like a simple request, but the question becomes: Check against what? If the police are making a document that sources from another report that another officer used AI to produce from their notes which were also run through AI and on and on, an inconsistency that leaks in at a previous step will check out when someone reviews the output against the inputs.
We’re all also discovering that many people’s idea of reviewing the output is to skim it and verify that it looks convincing enough. Checking facts is hard and takes time. These people are using AI because they want to work less, not to give themselves extra work.
It’s not typing that’s the bottleneck, at least not often, so this is essentially assuming that you can do all the needed work without actually doing it, which is obviously wishful thinking.
A person produces the content and AI removes barriers, and contextually accelerates the process keeping you in a flow state, rather than AI generates human edits.
I think "check everything that it produces" will ultimately have to happen in cross examination on the witness stand. "Did you use AI" will be the first question.
This is honestly the fundamental problem of AI as I see it
When we offload our work to a different person we can calibrate our expectations to our past experiences with that person. With AI the experience is not very consistent. To use AI effectively you basically should treat it as a low trust, brand new coworker every single time you use it
That doesn't really scale, so people have two choices: be constantly hyper vigilant for mistakes the AI makes, or become complacent and trust it more than they should
People rightly point out that humans make mistakes too, not just AI. But humans have a pretty manageable cap on the amount of output they can produce. One human can pretty thoroughly review the outputs of a small team of other humans
One human can't possibly thoroughly review the volume of output that an LLM they are prompting can produce
But AI proponents are more than happier to brandish carefully curated anecdotes than to do a systematic study of risks and impacts.
Courts prefer to have live witness testimony for a good reason. Detectives prefer to have statements made with the events as fresh as possible for a good reason. At the same time an oral report can save time and labor. Where we can take police or witness testimony verbally, more promptly, and with less work, and including body language, we should.
And video is more AI tamper evident than text.
Are we thinking about how we’re using it, or???
It seems like; there’s two kinds of data that might go into this, boilerplate and subjective information. Subjective information should be input by the police, because I would assert the specific wording matters. It matters that the words used to describe what the policeman saw comes out of the policeman’s brain. If it’s boilerplate, I’d AI really more reliable then copy-paste?
WSJ and Bloomberg took a lot of the top markets/companies people almost a decade ago now (Andrea Felstead was one, genuinely someone who knew UK retail very well). The majority of the remaining columnists either worked in politics or are politics-adjacent. There is almost no detailed finance market coverage. The UK companies stuff was spun out into the Shares magazine 20 years ago.
The FT reflects British society, nothing could be more grubby than becoming involved in commerce. Many of the people who moved up and out go into political journalism because that is high status (i.e. Peston). The FT also has a nasty habit of creating special jobs for people if they are high status enough (Kuper is one, Keynes is the new one, there are many more). The FT is a basically unreformed backwater that is a bit like it was 80s, nothing has really changed. I know a few people who work there in undemanding roles (every couple of weeks attending an expenses paid dinner with a celebrity) and got their job through nepotism. It isn't like anywhere else in UK business or even journalism because of the corporate subscription revenue, the editor is able to run it like a fief.
To give specific examples: Chris Giles is somewhat notorious for being a complete hack. If you are somewhat familiar with how news is made, you should be able to read his stories and work out exactly what conversations led to that story being written. In many cases it is Giles talking to someone adjacent to or in politics. Martin Wolf is a complete dinosaur, if he writes a column you can predict exactly what his take will be because he hasn't had a new idea since 1990. JBM is probably the only journalist who actually writes interesting things, these things however often seem to be conflicted with his personal interests/conversations with civil servants. Stuart Kirk...how does he have a column? Barely worked in markets, somehow the markets guy. Shrimsley, politics guy. Cavendish, worked for Cameron. Beattie, basically a Martin Wolf-lite. Pilita Clark, Lidl Kellaway. It goes on and on. Ineffectual posh people with the most anodyne, pro-establishment positions boring everyone to death with their thoughts.
Exceptions: Lucy Kellaway, long gone now but she was very good (nothing to do with business or finance though, more social commentary). Janen Ganesh, also good (again, social commentary). In actual business or finance...nothing interesting. John Lee was quite interesting. MSW was somewhat interesting but also said catastrophically incorrect things often...but she was good for marketing if you started a fund and actually was primarily a fund management journalist (although at the FT she often strayed into politics).
Also, a special mention for Lionel Barber...admitted to leaking stories to traders in a documentary, there are multiple laws against this in the UK, never charged, never investigated. A lot of what changed with the paper happened because of Barber and his opinions on things like Brexit where he interpreted the role of the FT as being a political activist first. Same thing has happened at the Economist when Micklethwait left, the lure of politics and being culturally relevant is too strong.
Lex is also useless. They had some decent people there writing on niche topics. But after the incident with Barber/Wirecard, there has been a big change in how that part of the world works. Many years ago, you would call up someone from Lex to leak a fake M&A rumour and (if you gave them something real later) they would "leak" it (this was confirmed publicly in the Operation Tabernula trial, this is why many papers have pulled back completely on any market-adjacent coverage...afaik, Mark Kleinman is basically the only person trying to do this stuff anymore and it is a million miles from what it once was).
Thinking about all of the fake citations in legal submissions that have come up of late, it seems pretty straightforward to set up a regex that captures all forms in which a cited case might be written (I could be wrong but I'd assume there's some standard variety of formats) and search those against a database (again assuming such a database exists) to ensure they all exist.
Then for the tougher problem of making sure that the cited cases say whatever the document citing them says they do, you could have an LLM run through the document, pull out the text with the case name and text about why it's being cited, then read the case and try to determine whether the reason for citing it is valid. Rather than just give a yes/no, you'd put the doc in front of the user and let them jump from citation to citation. On each citation, it'd pop up a card that shows the literal text of why it's being cited, a judgement from the LLM of whether it matches what the case says, and snippets of text from the case as evidence + deeplinks to that text within the case.
Or maybe you wouldn't even want to give the LLM's judgement since people might rely on that without reading, but there's definitely a way to speed up the review.
I believe OpenEvidence does something like this with medical papers. If you ask it a medical question, it doesn't answer so much as link you directly to the relevant papers so you can read them and determine if they're useful. Avoids all of the potential risks of using an LLM but still hugely valuable and time-saving for docs.
remember how the bank giving your money to the wrong person was a crime? and then when "the computet" did it was just business as usual and you paid more for banking because now they had "computer fraud" insurance?
same thing. cop deliver false report, jail (hah! i know). now, it was "the Ai". so no jail, they will go back and put rules for the cop to read or something.
and we are making everything worse by the minute. One gov push back on Ai nonsense, ibm/rh cames up with all sort of lies that would make any engineer or research laugh on their faces (federated learning being for privacy, instead of cost cutting. or explainable Ai being real, and not something bolted after the inference with extra unexplainable inference. etc.) but that are good enough to fool the regulator.