Could AI Help Prevent Mass Shootings?

Views: 567

Could machine learning/AI techniques help to prevent mass shootings or other kinds of terrorist attacks? That’s the question. I do not profess to know the answer — but it’s a question that as a society we must seriously consider.

A notable relatively recent attribute of many mass attacks is that the criminal perpetrators don’t only want to kill, they want as large an audience as possible for their murderous activities, frequently planning their attacks openly on the Internet, even announcing online the initiation of their killing sprees and providing live video streams as well. Sometimes they use private forums for this purpose, but public forums seem to be even more popular in this context, given their potential for capturing larger audiences.

It’s particularly noteworthy that in some of these cases, members of the public were indeed aware of such attack planning and announcements due to those public postings, but chose not to report them. The reasons for the lack of reporting can be several. Users may be unsure whether or not the posts are serious, and don’t want to report someone for a fake attack scenario. Other users may want to report but not know where to report such a situation. And there may be other users who are actually urging the perpetrator onward to the maximum possible violence.

“Freedom of speech” and some privacy protections are generally viewed as ending where credible threats begin. Particularly in the context of public postings, this suggests that detecting these kinds of attacks before they have actually occurred may possibly be viewed as a kind of “big data” problem.

We can relatively easily list some of the factors that would need to be considered in these respects.

What level of resources would be required to keep an “automated” watch on at least the public postings and sites most likely to harbor the kinds of discussions and “attack manifestos” of concern? Could tools be developed to help separate false positive, faked, forged, or other “fantasy” attack postings from the genuine ones? How would these be tracked over time to include other sites involved in these operations, and to prevent “gaming” of the systems that might attempt to divert these tools away from genuine attack planning?

Obviously — as in many AI-related areas — automated systems alone would not be adequate by themselves to trigger full-scale alarms. These systems would primarily act as big filters, and would pass along to human teams their perceived alerts — with those teams making final determinations as to dispositions and possible referrals to law enforcement for investigatory or immediate preventative actions.

It can be reasonably argued that anyone publicly posting the kinds of specific planning materials that have been discovered in the wake of recent attacks has effectively surrendered various “rights” to privacy that might ordinarily be in force.

The fact that we keep discovering these kinds of directly related discussions and threats publicly online in the wake of these terrorist attacks, suggests that we are not effectively using the public information that is already available toward stopping these attacks before they actually occur.

To the extent that AI/machine learning technologies — in concert with human analysis and decision-making — may possibly provide a means to improve this situation, we should certainly at least be exploring the practical possibilities and associated issues.

–Lauren–

Pressuring Google’s AI Advisory Panel to Wear a Halo Is Very Dangerous

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UPDATE (April 4, 2019): Google has announced that due to the furor over ATEAC (their newly announced external advisory panel dealing with AI issues), they have dissolved the panel entirely. As I discuss in the original post below, AI is too important for our typical political games — and closed-minded unwillingness to even listen to other points of view — to hold sway, and such panels are potentially an important part of the solution to that problem. As I noted, I disagree strenuously with the views of the panel member (and their own organization) that was the focus of the intense criticism that apparently pressured Google into this decision, but I fear that an unwillingness to permit such organizations to even be heard at all in such venues will come back to haunt us mightily in our toxic political environment.

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Despite my very long history of enjoying “apocalyptic” and “technology run amok” sci-fi films, I’ve been forthright in my personal belief that AI and associated machine learning systems hold enormous promise for the betterment of our lives and our planet (“How AI Could Save Us All” – https://lauren.vortex.com/2018/05/01/how-ai-could-save-us-all).

Of course there are definitely ways that we could screw this up. So deep discussion from a wide variety of viewpoints is critical to “accentuate the positive — eliminate the negative” (as the old Bing Crosby song lyrics suggest).

A time-tested model for firms needing to deal with these kinds of complex situations is the appointment of external interdisciplinary advisory panels. 

Google announced its own such panel — the “Advanced Technology External Advisory Council” (ATEAC), last week. 

Controversy immediately erupted both inside and outside of Google, particularly relating to the presence of prominent right-wing think tank Heritage Foundation president Kay Cole James. Another invited member — behavioral economist and privacy researcher Alessandro Acquisti — has now pulled out from ATEAC, apparently due to James’ presence on the panel and the resulting protests.

This is all extraordinarily worrisome. 

While I abhor the sentiments of the Heritage Foundation, an AI advisory panel composed only of “yes men” in agreement more left-wing (and so admittedly my own) philosophies regarding social issues strikes me as vastly more dangerous.

Keeping in mind that advisory panels typically do not make policy — they only make recommendations — it is critical to have a wide range of input to these panels, including views with which we may personally strongly disagree, but that — like it or not — significant numbers of politicians and voters do enthusiastically agree with. The man sitting in the Oval Office right now is demonstrable proof that such views — however much we may despise them personally — are most definitely in the equation.

“Filter bubbles” are extraordinarily dangerous on both the right and left. One of the reasons why I so frequently speak on national talk radio — whose audiences are typically very much skewed to the right — is that I view this as an opportunity to speak truth (as I see it) regarding technology issues to listeners who are not often exposed to views like mine from the other commentators that they typically see and hear. And frequently, I afterwards receive emails saying “Thanks for explaining this like you did — I never heard it explained that way before” — making it all worthwhile as far as I’m concerned.

Not attempting to include a wide variety of viewpoints on a panel dealing with a subject as important as AI would not only give the appearance of “stacking the deck” to favor preconceived outcomes, but would in fact be doing exactly that, opening up the firms involved to attacks by haters and pandering politicians who would just love to impose draconian regulatory regimes for their own benefits. 

The presence on an advisory panel of someone with whom other members may dramatically disagree does not imply endorsement of that individual.

I want to know what people who disagree with me are thinking. I want to hear from them. There’s an old saying: “Keep your friends close and your enemies closer.” Ignoring that adage is beyond foolish.

We can certainly argue regarding the specific current appointments to ATEAC, but viewing an advisory panel like this as some sort of rubber stamp for our preexisting opinions would be nothing less than mental malpractice. 

AI is far too crucial to all of our futures for us to fall into that sort of intellectual trap.

–Lauren–