AI-infused hiring programs have drawn scrutiny, most notably over whether they end up exhibiting biases based on the data they’re trained on.

AI-infused hiring programs have drawn scrutiny, most notably over whether they end up exhibiting biases based on the data they’re trained on.

  • stanleytweedle@lemmy.worldEnglish
    1·
    2 years ago

    They can ‘prove’ they don’t explicitly train the models on race or gender but that doesn’t really prove anything. A model will inevitably take into account data that it will correlate to race or gender- names, zip codes, education and financial history, etc, and those correlations will result in similarly biased decisions that regular human racism and sexism produce. Weeding that out completely may not even be possible.

    • ProfessorYakkington@lemmy.mlEnglish
      2·
      2 years ago

      Hey, I am a machine learning engineer that works with people data. Generally you measure bias in the training data, the validation sets, and the outcomes ( in an ongoing fashion - AIF 360 is a common library and approach ). There are lots of ways to measure bias and or fairness. Just checking if a feature was used isn’t considered “enough” by any standards or practitioner. There are also ways to detect and mitigate some of the proxy relationships you’re pointing to. That being said, I am 100% skeptical that any hiring algorithm isn’t going to be extremely bias. A lot of big companies have tried and quit because despite using all the right steps the models were still bias https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G. Also many of the metrics used to report fairness have some deep flaws ( disprate impact ).

      All that being said the current state is that there are no requirements for reporting so vendors don’t do the minimum 90% of the time because if they did it would cost a lot more and get in the way of the “AI will solve all your problems with no effort” narrative they want to put forward so I am happy to see any regulation coming into place even if it won’t be perfect.