Thank you for sharing your approach, I find it very interesting. I've worked in some systems that also optimize for "serendipity"[1], which is just optimizing not only for ranking accuracy but also for adding new relevant content into the mix. (I believe you need a lot of data for this to be viable though)
> I think the concerns about ML causing echo chambers/other problems, while not completely unfounded, are overblown
I disagree that it's overblown. This is a widely discussed topic in ML research, especially around recommender systems [0]. While I do agree that ML systems have enormous potential in augmenting human capability, we should be addressing possible flaws such as the one mentioned.
I'm personally interested in how to solve biases in machine learning systems, as it impacts so much of my professional work. But I also think bias, or echo-chamber, isn't unique to ML since we see it so much in the world and institutions that surround us, but we are in a unique position to address these problems directly on the systems we create.
Fair enough. To be clear, I'm not saying ML bias isn't a significant problem; and certainly I'll continue to address it as the project grows. Based on this, it sounds like we might be in agreement mostly:
> But I also think bias, or echo-chamber, isn't unique to ML since we see it so much in the world and institutions that surround us, but we are in a unique position to address these problems directly on the systems we create.
I often run into people (usually not ML practitioners) who appear to think that ML inherently results in filter bubbles/bias (as opposed to specific ML implementations) and thus think the entire approach should be abandoned; whereas I think ML, with a proper focus on reducing bias, is one of our most promising options.
> I think the concerns about ML causing echo chambers/other problems, while not completely unfounded, are overblown
I disagree that it's overblown. This is a widely discussed topic in ML research, especially around recommender systems [0]. While I do agree that ML systems have enormous potential in augmenting human capability, we should be addressing possible flaws such as the one mentioned.
I'm personally interested in how to solve biases in machine learning systems, as it impacts so much of my professional work. But I also think bias, or echo-chamber, isn't unique to ML since we see it so much in the world and institutions that surround us, but we are in a unique position to address these problems directly on the systems we create.
[0] https://arxiv.org/abs/2010.03240 [1] https://link.springer.com/article/10.1007/s11390-020-0135-9