Priorworkhastackledthisissue ways (see Section 5).

Although the fairness community has recognized the importance of data, re-searchers inthe area primarily rely on UCIAdult when itcomes totabular data.

Table 6: Results of selected models on Task 1 (Grouping) using contextual embeddings. In this section, we provide additional t-SNE projections of embeddings from various methods used. Figure 7: Solved wall for Task 1 (Grouping) using GloV e. Left: ( " Suspension" is " a term used in musical harmony " in this context. Grief " in the embedding space, which matches the " Good ___! " connection. Figure 8: Solved wall for Task 1 (Grouping) using FastText (Crawl). Left: contextual embedding solved 3/4 groups. Here the clue " Rambrandt" is placed near other Dutch painters. Right: static embedding solved 0/4 groups. The following section provides answers to questions listed in datasheets for datasets. For what purpose was the dataset created? Was there a specific task in mind? Who created this dataset (e.g., which team, research group) and on behalf of which entity (e.g., The dataset has been collectively curated by the authors of this paper. What support was needed to make this dataset?

Censorship has been a constant in Ai Weiwei's life. The 68-year-old Chinese dissident, whose activist art has made him among Beijing's most prominent critics, has seen his films, sculptures and other works restricted for their criticisms of China as well as his outspoken advocacy for human rights around the world. Speaking in London ahead of the January 29 launch of his new book On Censorship," he discussed returning to China for the first time in a decade, the impact of AI on freedom of expression, and what he sees as the erosion of free speech in the West. This conversation has been edited and condensed for clarity. In a time of both misinformation and too much information, quality journalism is more crucial than ever.