Comment by NarcolepticFrog | Hacker News Reader

Comment by NarcolepticFrog | Hacker News Reader

NarcolepticFrog

I agree with the sentiment that the discussion about AI and Machine Learning should not be entirely driven by industry.

At the same time, this article seems to be a bit down on AI itself, and part of their message is that AI doesn't provide "relevant and competent" solutions to problems. It also sounds like they are writing off real work (both in industry and academia) focusing on real ethical concerns with AI and what can be done to address them (e.g., the FAT* conference, a growing number of sessions on fairness, privacy, and other related topics at NeurIPS and ICML, etc).

I think the most important issue is educating the general public about AI and giving them familiarity with what types of things are automatable, what things can be learned from their data, where and how AI is being used in the real world currently, etc. A big part of this is to have the mainstream media be a bit more self-guided.

A final thought: one of the suggestions from the Reuter's article is that we should hear more from scientists and activists in the media. This seems a bit troubling to me, since in ML and AI research there are very strong ties between academia and industry (and often people move fairly freely between the two). I'm not sure we would hear a significantly different narrative if we talked to researchers in academia...

there is this humor show (the fix, on Netflix) where they were supposed to “fix” AI.

I think it’s quite telling that the entire show instead ended up talking about robotics. I don’t think any of the participants reflects at all on how AI is used today in the systems they interact with.

So that’s probably the first step in educating the public. Find a basic mental framework for thinking about AI that doesn’t involve robots or skynet.

That's a really interesting point - it does seem like most people immediately jump to skynet or our robot overlords whenever the topic of AI comes up. I don't have any really solid suggestions for what a good mental framework for thinking about AI would be, but I think giving an alternative to these unrealistic versions would be super helpful.

I always explain it using recommendation engines. You buy a vacuum and you get suggestions for 10 vacuums you might want to get next. Not so good. You buy a film with Tom Cruise, and you see 10 other Tom Cruise moves suggested, thats not so bad.

Related question: Does anyone have a good, accessible summary article of where AI is at, what can/can't be automated, etc?

I don't actually know of anything like this, though I think it should probably exist somewhere. If anyone else knows, I'd be interested to see it too!

All the research in fairness, privacy, and other related topics at academic conferences is irrelevant if the industry continues to recklessly deploy unethical machine learning in production.

In fact, it can be argued that industry funds such research only to appear to be ethical and to give themselves cover to continue to use unethical machine learning.

I don't know if I'd say it's irrelevant if it's not used by the current industry. It's always helpful to understand what is possible and how to achieve it. And beyond actual application, formalizations of fairness notions and thinking about their implications for computing gives us another lens to think about fairness more broadly (as in, these kinds of technical discussions can give specificity and clarity to discussions of fairness even beyond machine learning and computing).

To your second point, it's very difficult to attribute a single intention to large companies. There are certainly employees and researchers at large tech companies that work on fairness and privacy preservation because they think it's important and want to make their companies more responsible (I know some!). It's also certainly true that exploring this type of research looks good for the company, and some other employees are surely aware of that and promoting it for those reasons.

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