There has been considerable work recently in the natural language community and elsewhere on Responsible AI. Much of this work focuses on fairness and biases (henceforth Risks 1.0), following the 2016 best seller:Weapons of Math Destruction. Two books published in 2022, The Chaos MachineandLike, Comment, Subscribe, raise additional risks to public health/safety/security such as genocide, insurrection, polarized politics, vaccinations (henceforth, Risks 2.0). These books suggest that the use of machine learning to maximize engagement in social media has created a Frankenstein Monster that is exploiting human weaknesses with persuasive technology, the illusory truth effect, Pavlovian conditioning, and Skinner’s intermittent variable reinforcement. Just as we cannot expect tobacco companies to sell fewer cigarettes and prioritize public health ahead of profits, so too, it may be asking too much of companies (and countries) to stop trafficking in misinformation given that it is so effective and so insanely profitable (at least in the short term). Eventually, we believe the current chaos will end, like the lawlessness in Wild West, because chaos is bad for business. As computer scientists, this paper will summarize criticisms from other fields and focus on implications for computer science; we will not attempt to contribute to those other fields. There is quite a bit of work in computer science on these risks, especially on Risks 1.0 (bias and fairness), but more work is needed, especially on Risks 2.0 (addictive, dangerous, and deadly).

Computer-aided translation tools based on translation memories are widely used to assist professional translators. A translation memory (TM) consists of a set of translation units (TU) made up of source- and target-language segment pairs. For the translation of a new source segment $s^{\prime }$ , these tools search the TM and retrieve the TUs $(s,t)$ whose source segments are more similar to $s^{\prime }$ . The translator then chooses a TU and edit the target segment $t$ to turn it into an adequate translation of $s^{\prime }$ . Fuzzy-match repair (FMR) techniques can be used to automatically modify the parts of $t$ that need to be edited. We describe a language-independent FMR method that first uses machine translation to generate, given $s^{\prime }$ and $(s,t)$ , a set of candidate fuzzy-match repaired segments, and then chooses the best one by estimating their quality. An evaluation on three different language pairs shows that the selected candidate is a good approximation to the best (oracle) candidate produced and is closer to reference translations than machine-translated segments and unrepaired fuzzy matches ( $t$ ). In addition, a single quality estimation model trained on a mix of data from all the languages performs well on any of the languages used.

John E Ortega, Richard Alexander Castro-Mamani, Jaime Rafael Montoya Samame. Proceedings of the 3rd Workshop on Technologies for MT of Low Resource Languages. 2020.