Paige Tsai

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I actually found the results of this paper very encouraging, since it detailed precisely the mechanism through which Uber could act to further reduce the gender wage gap. However, to answer the question raised in the title of your blog post, I do not think that Uber should adjust the driver compensation algorithm to address the wage discrepancy, but rather implement other initiatives to help close the gap.

First, approximately 33% of the wage gap is attributed to experience on Uber’s platform. The presents a lot of opportunities for intervention. While there is no substitute for experience, Uber could partner with drivers to develop educational material to help ramp up inexperienced drivers with the platform. As part of this, Uber could provide additional information on the mechanisms of the Surge map, since 16% of the gap is attributed to male driver’s decisions to to drive more in areas with higher surge and lower wait times.

If anything, I think this article reveals the value in collecting, measuring and analyzing data. It enables to identify when gender gaps emerge and identify interventions to help address these issues!

On April 15, 2020, Paige Tsai commented on Your Instagram Told Me So… :

While I too appreciate the intentions of employers and schools who are trying to attend to employees’ and students’ mental health, I agree that resources may be better spent investing in other initiatives. I absolutely agree that individuals present a curated version of themselves on social media, and it may be futile to try to understand an individual’s emotional state based exclusively on their social media presence. I imagine that a 10-minute conversation with an employee or student would be a lot more informative than a glimpse of someone’s Instagram, for example.

Furthermore, even if employers could successfully deduce an individual’s mental health state from social media posts, I am not sure it would be appropriate for my employer to initiate a conversation about my mental health based on observations of my behavior outside of the work context.

On April 15, 2020, Paige Tsai commented on Locked in by Algorithms? :

In addition to the benefits you outline, Paula, there are a few key advantages to an algorithm-based system that tip the scales for me. Most notably, algorithms help reduce intra- and inter-judge inconsistency on sentencing determinations. A 1999 research paper found that inter-judge disparity on average sentence length was about 17 percent (or 4.9 months) [1] and the statistics on intra-personal inconsistencies (whether you have a meeting before or after lunch) are similarly troubling. The only scalable way in my mind to ensure consistency is sentencing is through algorithms. While you may be able to help individual judges or specific counties become more consistent in their rulings, an algorithm is more reliable for system-wide change.

However, I absolutely agree that it’s key that any algorithm implemented needs to help correct for the biases that are deeply engrained in our judicial system. While I’m optimistic that such a solution is possible, with, as you argue, the appropriate checks and balances in place.

[1] http://users.nber.org/~kling/interjudge.pdf

On April 15, 2020, Paige Tsai commented on Locked in by Algorithms? :

In addition to the benefits you outline, Paula, there are a few key advantages to an algorithm-based system that tip the scales for me. Most notably, algorithms help reduce intra- and inter-judge inconsistency on sentencing determinations. A 1999 research paper found that

Algorithms are not to blame for biases that are deeply engrained in the system. A well designed algorithm will not perpetuate such biases.