Technological Evolution Will Make Search Engine Bias Moot
Currently, search engines principally use "one size fits all" ranking algorithms to deliver homogeneous search results to searchers with heterogeneous search objectives.  One-size-fits-all algorithms exacerbate the consequences of search engine bias in two ways:
- They create "winners" (web sites listed high in the search results) and "losers" (those with marginal placement).
- They deliver suboptimal results for searchers with minority interests. 
These consequences will abate when search engines migrate away from one-size-fits-all algorithms toward "personalized" ranking algorithms.  Personalized algorithms produce search results that are custom-tailored to each searcher’s interests, so different searchers will see different results in response to the same search query. For example, Google offers searchers an option that "orders your search results based on your past searches, as well as the search results and news headlines you’ve clicked on." 
Personalized ranking algorithms represent the next major advance in search relevancy. One-size-fits-all ranking algorithms have inherent limits on their maximum relevancy potential, and further improvements in one-size-fits-all algorithms will yield progressively smaller relevancy benefits. Personalized algorithms transcend those limits, optimizing relevancy for each searcher and thus implicitly doing a better job of searcher mind-reading. 
Personalized ranking algorithms also reduce the effects of search engine bias. Personalized algorithms mean that there are multiple "top" search results for a particular search term, instead of a single "winner,"  so web publishers won’t compete against each other in a zero-sum game. In turn, searchers will get results more influenced by their idiosyncratic preferences and less influenced by the embedded preferences of the algorithm-writers. Also, personalized algorithms necessarily will diminish the weight given to popularity-based metrics (to give more weight for searcher-specific factors), reducing the structural biases due to popularity.
Personalized ranking algorithms are not a panacea—any process in which humans select and weight algorithmic factors will produce some bias,  but personalized algorithms will eliminate many of the current concerns about search engine bias.