Counter-stereotypes reduce emotional intergroup bias by eliciting surprise in the face of unexpected category combinations

In three experiments we investigated the impact that exposure to counter-stereotypes has on emotional reactions to outgroups. In Experiment 1, thinking about gender counter-stereotypes attenuated stereotyped emotions toward females and males. In Experiment 2, an immigrant counterstereotype attenuated stereotyped emotions toward this outgroup and reduced dehumanization tendencies. Experiment 3 replicated these results using an alternative measure of humanization. In both Experiments 2 and 3 sequential meditational analysis revealed that counter-stereotypes produced feelings of surprise which, in turn, elicited a cognitive process of expectancy violation which resulted in attenuated stereotyped emotions and an enhanced use of uniquely human characteristics to describe the outgroup. The findings extend research supporting the usefulness of counter-stereotype exposure for reducing prejudice and highlight its positive impact on intergroup emotions.

Exploiting heterogeneous scientific literature networks to combat ranking bias:evidence from the computational linguistics area

It is important to help researchers find valuable papers from a large literature collection. To this end, many graph-based ranking algorithms have been proposed. However, most of these algorithms suffer from the problem of ranking bias. Ranking bias hurts the usefulness of a ranking algorithm because it returns a ranking list with an undesirable time distribution. This paper is a focused study on how to alleviate ranking bias by leveraging the heterogeneous network structure of the literature collection. We propose a new graph-based ranking algorithm, MutualRank, that integrates mutual reinforcement relationships among networks of papers, researchers, and venues to achieve a more synthetic, accurate, and less-biased ranking than previous methods. MutualRank provides a unified model that involves both intra- and inter-network information for ranking papers, researchers, and venues simultaneously. We use the ACL Anthology Network as the benchmark data set and construct the gold standard from computer linguistics course websites of well-known universities and two well-known textbooks. The experimental results show that MutualRank greatly outperforms the state-of-the-art competitors, including PageRank, HITS, CoRank, Future Rank, and P-Rank, in ranking papers in both improving ranking effectiveness and alleviating ranking bias. Rankings of researchers and venues by MutualRank are also quite reasonable.

Earnings and hindsight bias:an experimental study

We conduct prediction experiments where subjects estimate, and later reconstruct probabilities of up-coming events. Subjects also value state-contingent claims on these events. We find that hindsight bias is greater for events where subjects earned more money