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In 2016, IKEA, the world-renowned Swedish furniture manufacturer, recalled some of its adult and children’s chests and dressers in US and Canada. But, despite the fact that IKEA also sold the same more than a million times in China, the products were not recalled there. Even if the company has more than 20 stores in the country, Chinese law did not force a recall. This case discusses the difference between legal and ethical decision-making at IKEA and asks whether ethical decisions are of importance in the People’s Republic in China.

Genetic programming, a form of genetic algorithm, has begun to be applied to a fuzzy information retrieval system in order to improve the formulation of weighted Boolean queries by means of relevance feedback. Our theoretical approach assembles together the concepts of information retrieval, fuzzy set theory, and genetic programming. Records (textual documents) in a database (collection) can be viewed as being represented by vectors of weights corresponding to the index terms that describe record topicality. A weighted Boolean query can be viewed as a parse tree and is a chromosome in terms of a genetic algorithm. Through the mechanisms of genetic programming, the weighted query is modified in order to improve system performance via precision and recall. Relevance feedback is incorporated, in part via user defined measures, over a trial set of records. The fitness of a candidate query can be expressed directly as a function of the perceived relevance of the retrieved set. Preliminary results based on some small testbeds are given. The form of the fitness function has a significant effect upon performance and the proper fitness functions take into account relevance based on topicality (and perhaps other factors³).