jm + feedback   5

“Racist algorithms” and learned helplessness
Whenever I’ve had to talk about bias in algorithms, I’ve tried be  careful to emphasize that it’s not that we shouldn’t use algorithms in search, recommendation and decision making. It’s that we often just don’t know how they’re making their decisions to present answers, make recommendations or arrive at conclusions, and it’s this lack of transparency that’s worrisome. Remember, algorithms aren’t just code.

What’s also worrisome is the amplifier effect. Even if “all an algorithm is doing” is reflecting and transmitting biases inherent in society, it’s also amplifying and perpetuating them on a much larger scale than your friendly neighborhood racist. And that’s the bigger issue. [...] even if the algorithm isn’t creating bias, it’s creating a feedback loop that has powerful perception effects.
feedback  bias  racism  algorithms  software  systems  society 
april 2016 by jm
Kate Heddleston: How Our Engineering Environments Are Killing Diversity
'[There are] several problem areas for [diversity in] engineering environments and ways to start fixing them. The problems we face aren't devoid of solutions; there are a lot of things that companies, teams, and individuals can do to fix problems in their work environment. For the month of March, I will be posting detailed articles about the problem areas I will cover in my talk: argument cultures, feedback, promotions, employee on-boarding, benefits, safety, engineering process, and environment adaptation.'

via Baron Schwartz.
via:xaprb  culture  tech  diversity  sexism  feminism  engineering  work  workplaces  feedback 
september 2015 by jm
Inceptionism: Going Deeper into Neural Networks
This is amazing, and a little scary.
If we choose higher-level layers, which identify more sophisticated features in images, complex features or even whole objects tend to emerge. Again, we just start with an existing image and give it to our neural net. We ask the network: “Whatever you see there, I want more of it!” This creates a feedback loop: if a cloud looks a little bit like a bird, the network will make it look more like a bird. This in turn will make the network recognize the bird even more strongly on the next pass and so forth, until a highly detailed bird appears, seemingly out of nowhere.

An enlightening comment from the G+ thread:

This is the most fun we've had in the office in a while. We've even made some of those 'Inceptionistic' art pieces into giant posters. Beyond the eye candy, there is actually something deeply interesting in this line of work: neural networks have a bad reputation for being strange black boxes that that are opaque to inspection. I have never understood those charges: any other model (GMM, SVM, Random Forests) of any sufficient complexity for a real task is completely opaque for very fundamental reasons: their non-linear structure makes it hard to project back the function they represent into their input space and make sense of it. Not so with backprop, as this blog post shows eloquently: you can query the model and ask what it believes it is seeing or 'wants' to see simply by following gradients. This 'guided hallucination' technique is very powerful and the gorgeous visualizations it generates are very evocative of what's really going on in the network.
art  machine-learning  algorithm  inceptionism  research  google  neural-networks  learning  dreams  feedback  graphics 
june 2015 by jm
Why Google Flu Trends Can't Track the Flu (Yet)
It's admittedly hard for outsiders to analyze Google Flu Trends, because the company doesn't make public the specific search terms it uses as raw data, or the particular algorithm it uses to convert the frequency of these terms into flu assessments. But the researchers did their best to infer the terms by using Google Correlate, a service that allows you to look at the rates of particular search terms over time. When the researchers did this for a variety of flu-related queries over the past few years, they found that a couple key searches (those for flu treatments, and those asking how to differentiate the flu from the cold) tracked more closely with Google Flu Trends' estimates than with actual flu rates, especially when Google overestimated the prevalence of the ailment. These particular searches, it seems, could be a huge part of the inaccuracy problem.

There's another good reason to suspect this might be the case. In 2011, as part of one of its regular search algorithm tweaks, Google began recommending related search terms for many queries (including listing a search for flu treatments after someone Googled many flu-related terms) and in 2012, the company began providing potential diagnoses in response to symptoms in searches (including listing both "flu" and "cold" after a search that included the phrase "sore throat," for instance, perhaps prompting a user to search for how to distinguish between the two). These tweaks, the researchers argue, likely artificially drove up the rates of the searches they identified as responsible for Google's overestimates.

via Boing Boing
google  flu  trends  feedback  side-effects  colds  health  google-flu-trends 
march 2014 by jm
feedback loop n-gram analyzer
'a simple parser of ARF compliant FBL complaints, which normalizes the email complaints and generates a 6-tuple n-gram version of the message. These n-grams are stored in a Redis database, keyed by the file in which they can be found. An inverse index also exists that allow you to find all messages containing a particular n-gram word.'
anti-spam  spam  fbl  feedback  filtering  n-grams  similarity  hashing  redis  searching 
september 2011 by jm

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