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Focusing on such statistics usually encourages equality of outcome as opposed to equality of opportunity. A far more helpful metric would probably be to know h

Focusing on such statistics usually encourages equality of outcome as opposed to equality of opportunity. A far more helpful metric would probably be to know how many people want to be in tech (or tech journalism in this case) but can’t, along with the various factors that are preventing them from getting into their desired profession. If you do then break down the numbers by ethnicity, gender, etc., you may be able to tease out a correlation that may hint at institutional bias, but there’s still no way to prove causation with just that data. You would at least be clearer on the what the obstacles are that’re faced by people, perhaps marginalized people, that put them at a disadvantage when it comes to trying to get into those fields. Then you can proactively and practically start to do something about those specific obstacles.

whimsicalism 5y ago
Can you explain more? It seems like it would only encourage equality of outcome if you took the assumption that black people are innately less interested in tech as true.
- xondono 5y ago
  > if you took the assumption that black people are innately less interested in tech as true. Or maybe more interested but much more discriminated. The point is that you can’t see equality of opportunity by looking at the output.
  - whimsicalism 5y ago
    I think a good baseline is similar levels of innate interest. The fact that the output is so skewed seems to logically suggest some issue with equality of opportunity?
    - sokoloff 5y ago
      I agree it suggests that possibility. Now what? IMO, you have to look deeper to try to understand the drivers of the suggested inequality for fixing the drivers is what fixes the system. What are the filters or moments that create skew between interest and eventual accomplishment? How do we remove/reduce them?
      - whimsicalism 5y ago
        Oh I absolutely agree that you have to look deeper to understand the drivers. I also agree that many of the drivers are from far before any candidate hits the hiring pipeline or anything like that. But given that in this thread there are lots of people denying that this is even suggestive of a problem, I can also see the merit in collecting the information just to demonstrate that a problem even exists - it is hard to get buy-in among people who do not believe that race should be discussed in the 21st century.
  - MonaroVXR 5y ago
    >but much more discriminated. This right here.
- mactavish88 5y ago
  It seems to me as though people who make a big deal out of "lack of diversity" in any field are saying that it's because of some form of discrimination or bias in hiring. This is the primary hypothesis they're putting forward, often as fact, as opposed to the reality that it's a hypothesis until it's adequately substantiated with unbiased data collected from multiple sources. The hypothesis may be true, but it's hard to tell objectively (e.g. I don't know many managers who would even be capable of seeing that they're biased, let alone acknowledging their biases). Accusing people of being biased without evidence isn't constructive for anyone involved either. So we need to come at the problem a little differently, and accept the fact that we're never going to get a 100% clear-cut answer, but we can get much closer to the truth than we are right now. We also need to accept the fact that the reality will change over time, and so regular interrogation of the hypothesis is necessary. We can look at a variety of metrics to try to tease out whether there are biases in specific companies (this list is by no means complete, these are just two studies that I can think of conducting off the top of my head): 1. Compare the candidates who actively apply for jobs to those who actually get the jobs. This comparison must not only include candidates' group affiliations (ethnicity, gender, age, etc.), but reliable measures of competency for the particular role for which they applied. 2. Find out from a larger pool of people outside of (1) whether they would have applied for a particular job but did not or could not because of particular barriers. If you find that the data from (1) shows that there are candidates who have particular group affiliations and meet the competency criteria for the roles, and they are less preferred by a particular company than people of other group affiliations of similar or lower competency, then you can say with a reasonable degree of likelihood that whoever made the hiring decisions at those specific companies is/are probably biased. Not the company as a whole - just the people who made the hiring decisions (there won't be data on the other people in the company, so you can't make judgements of them from such a study). Do that for enough companies in different geographies and you'll get a sense of the different biases in different cities/states/countries (e.g. some places may be biased against competent women, others may be biased against competent older people). Then, (2) should help us to find out qualitatively what factors are actively contributing to people not even applying for particular jobs in the first place. These may be structural problems in countries'/states'/cities' education systems, lack of access to food or housing, poverty, etc. (the possibilities are almost endless). This will form the basis for more study to figure out how to best address particular structural problems/impediments.

whimsicalism 5y ago

Can you explain more? It seems like it would only encourage equality of outcome if you took the assumption that black people are innately less interested in tech as true.

xondono 5y ago
> if you took the assumption that black people are innately less interested in tech as true. Or maybe more interested but much more discriminated. The point is that you can’t see equality of opportunity by looking at the output.
- whimsicalism 5y ago
  I think a good baseline is similar levels of innate interest. The fact that the output is so skewed seems to logically suggest some issue with equality of opportunity?
  - sokoloff 5y ago
    I agree it suggests that possibility. Now what? IMO, you have to look deeper to try to understand the drivers of the suggested inequality for fixing the drivers is what fixes the system. What are the filters or moments that create skew between interest and eventual accomplishment? How do we remove/reduce them?
    - whimsicalism 5y ago
      Oh I absolutely agree that you have to look deeper to understand the drivers. I also agree that many of the drivers are from far before any candidate hits the hiring pipeline or anything like that. But given that in this thread there are lots of people denying that this is even suggestive of a problem, I can also see the merit in collecting the information just to demonstrate that a problem even exists - it is hard to get buy-in among people who do not believe that race should be discussed in the 21st century.
- MonaroVXR 5y ago
  >but much more discriminated. This right here.
mactavish88 5y ago
It seems to me as though people who make a big deal out of "lack of diversity" in any field are saying that it's because of some form of discrimination or bias in hiring. This is the primary hypothesis they're putting forward, often as fact, as opposed to the reality that it's a hypothesis until it's adequately substantiated with unbiased data collected from multiple sources. The hypothesis may be true, but it's hard to tell objectively (e.g. I don't know many managers who would even be capable of seeing that they're biased, let alone acknowledging their biases). Accusing people of being biased without evidence isn't constructive for anyone involved either. So we need to come at the problem a little differently, and accept the fact that we're never going to get a 100% clear-cut answer, but we can get much closer to the truth than we are right now. We also need to accept the fact that the reality will change over time, and so regular interrogation of the hypothesis is necessary. We can look at a variety of metrics to try to tease out whether there are biases in specific companies (this list is by no means complete, these are just two studies that I can think of conducting off the top of my head): 1. Compare the candidates who actively apply for jobs to those who actually get the jobs. This comparison must not only include candidates' group affiliations (ethnicity, gender, age, etc.), but reliable measures of competency for the particular role for which they applied. 2. Find out from a larger pool of people outside of (1) whether they would have applied for a particular job but did not or could not because of particular barriers. If you find that the data from (1) shows that there are candidates who have particular group affiliations and meet the competency criteria for the roles, and they are less preferred by a particular company than people of other group affiliations of similar or lower competency, then you can say with a reasonable degree of likelihood that whoever made the hiring decisions at those specific companies is/are probably biased. Not the company as a whole - just the people who made the hiring decisions (there won't be data on the other people in the company, so you can't make judgements of them from such a study). Do that for enough companies in different geographies and you'll get a sense of the different biases in different cities/states/countries (e.g. some places may be biased against competent women, others may be biased against competent older people). Then, (2) should help us to find out qualitatively what factors are actively contributing to people not even applying for particular jobs in the first place. These may be structural problems in countries'/states'/cities' education systems, lack of access to food or housing, poverty, etc. (the possibilities are almost endless). This will form the basis for more study to figure out how to best address particular structural problems/impediments.