If anyone didn't get what I meant when I said @ykilcher chose to "kick the hornets nest" or if anyone was wondering about the cost of speaking out against unethical behaviour in #AI, here's a little summary of my recent twitter feed.

CW: transphobia

ImageImageImageImage
Here's some more. If anyone doesn't understand why all these statements are explicitly transphobic ... well, it is because you don't face it. These are all extremely hurtful.

That's enough, but I've skipped all the misogyny, racism, anti-semitism etc. ImageImageImageImage
This isn't isolated. Another commentator on this stunt has needed to take some time off Twitter due to the reaction.

I honestly spent several days deciding to post on this, because I knew what would happen. It was important, but there is always a cost.

This behaviour and response was predictable. An unbroken causal chain from Yannic to me.

Which is perhaps unsurprising, given that he doesn't seem to think that hate-speech is harmful, that it is "just insults", and "something you simply don't like".

All things an ethics board shouldn't have to consider, but might have needed to.

"You are trolling an extremely toxic community and are likely to get publicly criticised on ethical grounds. Have you considered the risk you may mobilise an army of bigots against your colleagues?"
This tweet really resonated with me. It's always people from minoritised groups that need to perform "non-consensual maid-ery" and face the repercussions.

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More from @DrLaurenOR

Jun 6
This week an #AI model was released on @huggingface that produces harmful + discriminatory text and has already posted over 30k vile comments online (says it's author).

This experiment would never pass a human research #ethics board. Here are my recommendations.

1/7 I agree with KCramer. There...Text from huggingface discu...
@huggingface as the model custodian (an interesting new concept) should implement an #ethics review process to determine the harm hosted models may cause, and gate harmful models behind approval/usage agreements.

Medical research has functional models ie for data sharing.

2/7 ImageImage
Open science and software are wonderful principles but must be balanced against potential harm. Medical research has a strong ethics culture bc we have an awful history of causing harm to people, usually from disempowered groups.

See en.wikipedia.org/wiki/Medical_e… for examples.

3/7
Read 14 tweets
Apr 6
Very excited to have 2 new papers in press today in Lancet Digital Health, alongside an editorial from the journal highlighting our work.

I am immensely proud of the work we have done here and honestly think this is the most important work I have been involved in to date 🥳

1/7
#Medical #AI has a problem. Preclinical testing, including regulatory testing, does not accurately predict the risks that AI models pose once they are deployed in clinics.

I've written about this before in my blog, for example in:

google.com/amp/s/laurenoa…

2/7
In this work we:

1) describe a step by step method for algorithmic auditing in health, building on the 🔥 work by Raji et al

2) audit a high accuracy model we developed @theAIML for hip fracture dx, ID-ing several serious risks that were not detected by standard testing.

3/7
Read 9 tweets
Aug 2, 2021
#Medical #AI has the worst superpower... Racism

We've put out a preprint reporting concerning findings. AI can do something humans can't: recognise the self-reported race of patients on x-rays. This gives AI a path to produce health disparities.

1/8

lukeoakdenrayner.wordpress.com/2021/08/02/ai-…
This is a big deal, so we wanted to do it right. We did dozens of experiments, replication at multiple labs, on numerous datasets and tasks.

We are releasing all the code, as well as new labels to identify racial identity for multiple public datasets.

2/8
Humans can't detect race better than chance, but AI performs absurdly well on the task. As you can see here, AUC scores are in the high 90s, and are maintained on external validation on completely distinct datasets and across multiple different imaging tasks.

3/8 Image
Read 10 tweets
Dec 8, 2020
Docs are ROCs: A simple fix for a methodologically indefensible practice in medical AI studies.

Widely used methods to compare doctors to #AI models systematically underestimate doctors, making the AI look better than it is! We propose a solution.

lukeoakdenrayner.wordpress.com/2020/12/08/doc…

1/7
The most common method to estimate average human performance in #medical AI is to average sensitivity and specificity as if they are independent. They aren't though - they are inversely correlated on a curve.

The average points will *always* be inside the curve.

2/7
The only solution currently is to force doctors to rate images using confidence scores. While this works well in the few tasks where these scales are used in clinical practice, what does it mean to say you are 6/10 confident that there is a lung nodule?

3/7
Read 8 tweets
Aug 19, 2020
Alright, let's do this once last time. Predictions vs probabilities. What should we give doctors when we use #AI / #ML models for decision making or decision support?

#epitwitter

1/21
First, we need to ask: is there a difference?

This is a weird question, right? Of course there is! One is a categorical class prediction, the other is a continuous variable. Stats 101, amirite?

Well, no.

2/21
Let's set out the two ways that probabilities are supposed to be different than class predictions.

1) they are continuous, not categorical
2) they are probabilities, meaning the numbers reflects some truth about a patient group and are not arbitrary

Weeeeell...

3/21
Read 23 tweets
Jul 28, 2020
This discussion was getting long, so I thought I'd lay out my thoughts on a common argument: should models produce probabilities or decisions? Ie 32% chance of cancer vs "do a biopsy".

I favour the latter, because IMO it is both more useful and... more honest. IMO:

1/13
The argument against using a threshold to determine an action, at a basic level, seems to be:

1) you shouldn't discard information by turning a range of probabilities into a binary
2) probabilities are more useful at the clinical coalface

2/13
Re: 1.

No model discards information. The continuous output score always exists. It is how you make use of that information at point of care that "changes".

I use airquotes around "changes", because this is a ... false dichotomy 😆

3/13
Read 14 tweets

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