AI risks

From Tournesol
Jump to navigation Jump to search
This page contains changes which are not marked for translation.

Numerours AI risks have been listed in books like Bostrom-14 RDT-15 ONeil-16 Tegmark-17 Lee-18 Sharre-18 Russell-19 HoangElmhamdi-19FR.

A list of risks

They include cyberbullying HindujaPatchin-10, fairness MehrabiMSLG-19, privacy TanuwidjajaCK-19, increased inequalities Makridakis-17, job displacement MartensTolan-18, radicalization RibeiroOWAM-20, political manipulation WoolleyHoward+18, misinformation WuMCL-19 VosoughiRA-18, mute news (information that is drowned within the flood of information) RoslingRönnlundRR-19, information overload Roetzel-19, anger BergerMilkman-12, hate SchmidtWiegand-17, geopolitical tensions, addiction TurelBB18 HawiSamaha16, inability to focus MarkICJS-16, mental health EichstaedtSMUC+18, loss of control OrseauArmstrong-16, global (financial) crisis through unexpected disruption, resource depletion, autonomous weapons RussellHAV-15, arms race Geist-16, instrumental goals BensontilsenSoares-16 and existential risk Yudkowsky-08 Bostrom-13.

While there is already a lot of research, even more research on AI risks seems desirable, especially in areas with huge stakes and great uncertainty that could be reduced by more data collection or better thinking (see optimal exploration).

Security mindset

It has been argued Yudkowsky-17 that there is a lack of security mindset, especially regarding performant large-scale algorithms. Security mindset can be regarded as focusing on worst-case (or near-worst-case) scenarios, especially if they are not completely unlikely and pose huge risks. This is in sharp contrast with Facebook's old motto "move fast and break things" Taplin-17; and more generally with today's agile software development. It is also in sharp contrast with current trial-and-error machine learning development, especially in light of Goodhart's law and the flaws of testing [work in progress].

More influential algorithms surely represent greater risks. Arguably, this is already evidenced by the case of YouTube. But crucially, we should not be overfitting on today's deployed algorithms to ponder risks from tomorrow's algorithms. Recall that the rise of machine learning algorithm is quite recent, and very spectacular. The security mindset urges us to prepare for a similar, if not much faster, rate of progress in the coming years. Not necessarily because this scenario is more likely. But because it is not completely unlikely, and poses much greater risks.

The security mindset is argued to be hugely neglected by Bostrom-18, especially given the current fast rate of discoveries and innovations. The paper also discusses global governance that may greatly increase or reduce risks caused by dangerous discoveries. This seems critical to AI governance. The question "Can YouTube's algorithm become super-intelligent?" is unsettled.

Side effects

It is crucial to note that nearly all the above-mentioned AI risks are side effects of the large-scale deployment of algorithms (the main exception is autonomous weapons explicitly designed to cause harm). In particular, AI risks mostly don't come from the malicious intent of some algorithm, software developer or company. They mostly seem to arise from algorithms, software developers or companies' (sometimes intentional) tendency to neglect side effects of their behaviors.

To better understand this problem, it is worth comparing it to climate change. Greenhouse gas emission is not the intent of companies, car drivers and data centers. But it has become a problem because it was discarded as the companies', drivers' or data centers' goal and responsibility, sometimes intentionally, but also often by mere ignorance of the risks of such emissions.

The AI risk problem is similar. Unless we pay great attention to potential side effects and actively try to avoid them, the large-scale deployment of our algorithms will inevitably cause unintended potentially deadly side effects — as was the empowering of anti-vaccination propagandas (see YouTube).

Side effects of algorithms can also occur because of vulnerabilities of algorithms, typically to adversarial attacks GoodfellowSS-15 BlanchardMGS-17 NeffNagi-16.

Side effects were listed as the main concrete problem in AI safety by AmodeiOSCS+16. They are especially concerning for large-scale algorithms like social media recommender systems scholar. And they may be even more so, as such algorithms acquire more and more performant planning capabilities, built upon a much better modeling of their environments (see YouTube, human-level AI, AIXI).

In particular, algorithms with performant long-term planning capabilities feature risks of instrumental convergence. This corresponds to achieving instrumental goals like resource acquisition, which may endanger human activities or survival.

There seems to be a consensus that the only robust solution to AI safety and AI ethics is alignment, typically based on volition learning Yudkowsky-04 and social choice aggregation NoothigattuGADR+18. A lot more research in this direction is needed.