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Comment by u23KDd23 | Hacker News Reader

I'm worried about living in a society where we've become overly dependent upon statistical methods for important decision making especially when we aren't allowed to question their integrity, precision, and limitations. As a scientist I know the reality is there is a lot of terrible data being produced through various methods and I've met a lot of PhD students in the field of machine learning who argue against starting from scratch even when it's quite apparent their prior abstractions are incorrect. The reality is there are a lot of promises being made by ML researchers and dishonesty about their competency and ability to achieve those results. This is absolutely not unique to ML and plagues a lot of other fields as well. We really need more ML researchers to be actively critical about research being performed in the ML field if ML is going to achieve it's full potential.

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>I'm worried about living in a society where we've become overly dependent upon statistical methods for important decision making

That's very unlikely as people are incredibly biased against algorithms: http://lesswrong.com/r/discussion/lw/lsc/link_algorithm_aver...

There are many areas where even really simple algorithms have been shown to outperform humans for decades: http://lesswrong.com/lw/3gv/statistical_prediction_rules_out...

Even when humans are given the predictions of the algorithm and allowed to take that into account, they still do worse than just the algorithm on it's own. Sure the human might fix an obvious error of the algorithm in one case, but then makes 10 other worse errors elsewhere.

Despite that organizations are very slow and hesitant to adopt them. There are massive regulatory and liability issues in many areas. And people are just generally biased and scared of them. Even on tech friendly places like hacker news, your comment is at the top of the thread. I remember a post awhile ago about using machine learning to detect fraud in loans in the third world, and half the comments were about how evil and racist such and unfair such an algorithm would be. Not realizing humans are all of those things.

People are very overconfident in human ability despite overwhelming evidence we suck at predicting things and doing anything statistics related. Human error is just ignored or seen as an inevitable fact of life.

"People are very overconfident in human ability despite overwhelming evidence we suck at predicting things and doing anything statistics related. Human error is just ignored or seen as an inevitable fact of life."

Programs don't program themselves. Algorithmic biases often reflect human biases. If we want people to accept technology and give us opportunities to pursue our visions of what technology can offer society we need to be cognitive of ethical and moral challenges especially when there is so much at stake. Yes there are fields where there are regulatory and liability issues, but I'm more worried about the fields where there isn't as much oversight and transparency.

Algorithmic biases often reflect human biases

I've been doing this for a while and I've literally never met a human who told an algorithm to overweight x[23] ("good looking"), x[48] ("is white") and x[873] ("is wealthy"), for x a 1,100-dimensional feature vector.

Algorithms do have biases, but they are almost always orthogonal to the human ones. Witness, for example, all the recent "we can fool deep learning image recognition systems" papers.

http://arxiv.org/abs/1412.1897

http://arxiv.org/abs/1312.6199

At this point I'm 90% sure you are a layperson who's never actually programmed such a system.

Not to mention biases in data collection. "Garbage in, garbage out" certainly applies, and the situation probably worsens as datasets get bigger.

We should be equally worried about being overly dependent on clinical methods (human judgment) for important decision making. When looking at an algorithm, you can at least see how it works. Not so easy for "trust me, I'm a doctor, I know what I'm doing."

Check out classic papers like The Robust Beauty of Improper Linear Models or Paul Meehl's work on clinical vs. statistical expertise.

Have you heard of evidence-based medicine? It's actually a central component of clinical decision making.

I still don't trust them. Only 15% of doctors get this question right:

>1% of women at age forty who participate in routine screening have breast cancer. 80% of women with breast cancer will get positive mammographies. 9.6% of women without breast cancer will also get positive mammographies. A woman in this age group had a positive mammography in a routine screening. What is the probability that she actually has breast cancer?

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