3 ms·

Are there any real-world examples of an ML system causing protected-class discrimination based on non-protected criteria?

Arcsech 7y ago
Yes, for one example, a system used to recommend sentencing based on how likely a criminal was to re-offend was found to have been heavily biased based on race, despite not being given explicit information about race[1]. Edit: It’s unclear if this system is based on ML, but I think the point stands - if a system can be manually tuned to do this, the risk exists of this happening to a trained model as well. Given other cases of ML models learning an incorrect behavior (such as the system intended to detect skin cancer that ended up being a fancy ruler detector [2]). [1]: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://www.propublica.org/article/machine-bias-risk-assessm... [2]: https://www.thedailybeast.com/why-doctors-arent-afraid-of-better-more-efficient-ai-diagnosing-cancer https://www.thedailybeast.com/why-doctors-arent-afraid-of-be...
- harryh 7y ago
  That is a fine answer to mieseratte's question but I feel compelled to mention that the propublica article you link to is...pretty bad. Here's a pretty good summary of why: https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html https://www.chrisstucchio.com/blog/2016/propublica_is_lying....
AmericanChopper 7y ago
Using zip codes to implement discriminatory policies isn’t ML, but has almost certainly occurred. It’s referred to as redlining, and has been around for a while. The New Deal had some pretty indefensible redlining conditions. A lot of people will say the practice continues to this day, but the modern examples are a lot more open to interpretation than some of the historical ones are.
- mieseratte 7y ago
  Was the New Deal redlining out-and-out discrimination or a matter of accidentally creating conditions, i.e. based on bad intelligence, that resulted in discrimination. I always understood it to be the former.
  - AmericanChopper 7y ago
    The level of plausible deniability regarding The New Deal is mostly a matter of opinion, but personally I think it was pretty transparent in how it intentionally discriminated. It’s the modern examples that I think are much more questionable. For example, there are more liquor stores in black neighbourhoods. Is this because: a) A conspiracy to use alcohol to suppress the black population? b) Those neighbourhoods have a greater demand for liquor stores?
jetrink 7y ago
A recruiting tool used by Amazon developed a bias against women despite not being told candidates' genders. It penalized candidates who were graduates of all-women's colleges and also those who had the word "women" in their resume (e.g. “women’s chess club captain.”) It had been trained on resumes submitted to Amazon during the previous ten years, so the tool's bias was likely reflective of real human bias in Amazon's recruiting process. 1. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G https://www.reuters.com/article/us-amazon-com-jobs-automatio...
- mieseratte 7y ago
  I think that’s a great example on needing to sanitize data, but being a member of a women’s club or attending a women’s college is itself telling you the candidates gender so I don’t know that this is deriving protected information from non-protected information.
  - jetrink 7y ago
    This is a game of whack-a-mole. Given enough data, the ML system will just find other characteristics or groups of characteristics that act as proxies. It might unfairly penalize candidates in ways that are impossible to detect by human evaluators. The promise of these systems is that if you give them a pile of raw data, they will detect subtle patterns that aid in assessing individuals (be they job applicants, ex-cons, whatever.) However, some of the patterns that they detect are our biases against classes of people. If the solution is to sanitize the data to such a degree that the ML system can no longer infer that someone is a woman or that someone is over 40, etc., then the training data is probably also useless for detecting the non-obvious patterns that we want the system to discover.
    - mieseratte 7y ago
      That it is a “game of whack-a-mole” isn’t clear to me, and you are asserting so without any backing, which is why I asked if anyone has real-world examples. Citing an example where they don’t provide gender but then provide “went to an all-women college” is not an example of proving non-protected information which results in protected-class discrimination, it is an example of telling an ML system protected information in a roundabout way.

Arcsech 7y ago

Yes, for one example, a system used to recommend sentencing based on how likely a criminal was to re-offend was found to have been heavily biased based on race, despite not being given explicit information about race[1]. Edit: It’s unclear if this system is based on ML, but I think the point stands - if a system can be manually tuned to do this, the risk exists of this happening to a trained model as well. Given other cases of ML models learning an incorrect behavior (such as the system intended to detect skin cancer that ended up being a fancy ruler detector [2]). [1]: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing https://www.propublica.org/article/machine-bias-risk-assessm... [2]: https://www.thedailybeast.com/why-doctors-arent-afraid-of-better-more-efficient-ai-diagnosing-cancer https://www.thedailybeast.com/why-doctors-arent-afraid-of-be...

harryh 7y ago
That is a fine answer to mieseratte's question but I feel compelled to mention that the propublica article you link to is...pretty bad. Here's a pretty good summary of why: https://www.chrisstucchio.com/blog/2016/propublica_is_lying.html https://www.chrisstucchio.com/blog/2016/propublica_is_lying....

AmericanChopper 7y ago

Using zip codes to implement discriminatory policies isn’t ML, but has almost certainly occurred. It’s referred to as redlining, and has been around for a while. The New Deal had some pretty indefensible redlining conditions. A lot of people will say the practice continues to this day, but the modern examples are a lot more open to interpretation than some of the historical ones are.

mieseratte 7y ago
Was the New Deal redlining out-and-out discrimination or a matter of accidentally creating conditions, i.e. based on bad intelligence, that resulted in discrimination. I always understood it to be the former.
- AmericanChopper 7y ago
  The level of plausible deniability regarding The New Deal is mostly a matter of opinion, but personally I think it was pretty transparent in how it intentionally discriminated. It’s the modern examples that I think are much more questionable. For example, there are more liquor stores in black neighbourhoods. Is this because: a) A conspiracy to use alcohol to suppress the black population? b) Those neighbourhoods have a greater demand for liquor stores?

jetrink 7y ago

A recruiting tool used by Amazon developed a bias against women despite not being told candidates' genders. It penalized candidates who were graduates of all-women's colleges and also those who had the word "women" in their resume (e.g. “women’s chess club captain.”) It had been trained on resumes submitted to Amazon during the previous ten years, so the tool's bias was likely reflective of real human bias in Amazon's recruiting process. 1. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G https://www.reuters.com/article/us-amazon-com-jobs-automatio...

mieseratte 7y ago
I think that’s a great example on needing to sanitize data, but being a member of a women’s club or attending a women’s college is itself telling you the candidates gender so I don’t know that this is deriving protected information from non-protected information.
- jetrink 7y ago
  This is a game of whack-a-mole. Given enough data, the ML system will just find other characteristics or groups of characteristics that act as proxies. It might unfairly penalize candidates in ways that are impossible to detect by human evaluators. The promise of these systems is that if you give them a pile of raw data, they will detect subtle patterns that aid in assessing individuals (be they job applicants, ex-cons, whatever.) However, some of the patterns that they detect are our biases against classes of people. If the solution is to sanitize the data to such a degree that the ML system can no longer infer that someone is a woman or that someone is over 40, etc., then the training data is probably also useless for detecting the non-obvious patterns that we want the system to discover.
  - mieseratte 7y ago
    That it is a “game of whack-a-mole” isn’t clear to me, and you are asserting so without any backing, which is why I asked if anyone has real-world examples. Citing an example where they don’t provide gender but then provide “went to an all-women college” is not an example of proving non-protected information which results in protected-class discrimination, it is an example of telling an ML system protected information in a roundabout way.