Search Bias Research Articles

An Enhanced Memetic Algorithm using SKB tree representation for fixed-outline and temperature driven non-slicing floorplanning

Floorplanning is an ever-emerging field in the Very Large Scale Integration (VLSI) circuit design automation since it deals with essential design metrics of a floorplan, such as chip area, total wirelength, and temperature. This paper aims to optimize these design metrics to obtain an optimal floorplan with reduced computation time. A Skewed B* (SKB) tree based Enhanced Memetic Algorithm (EMA) is proposed for temperature-driven fixed-outline non-sliceable floorplanning. The proposed EMA has two search phases: a global search based on Genetic Algorithm (GA), and a local search based on the Adaptive Fast Simulated Annealing (AFSA) algorithm. A novel dynamic threshold bias search strategy is implemented to balance the search phases, thus increasing the convergence speed to obtain an optimal solution. The performance of the proposed algorithm is tested on MCNC (Microelectronic Centre for North Carolina), and GSRC (Gigascale System Research Centre) state-of-the-art benchmark circuits. Experimental results show that the EMA produces highly optimal design metrics on all tested benchmark circuits.

Leveraging Meta-Information of Products to Eliminate Bias in an E-Commerce Grid Search

Leveraging Meta-Information of Products to Eliminate Bias in an E-Commerce Grid Search

Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology

Information search, coherence effects, and their interplay in legal decision making

Legal judgments require one to make sense of a complex set of typically contradicting pieces of information that can easily be interpreted in a biased manner. This systematic judgment bias can be caused by biased information search (i.e., confirmatory search) as well as biased information processing (i.e., coherence effects) in which the interpretation of information is changed to fit the emerging favored option. In four studies, we investigate the complex interplay between both kinds of influences. Participants completed three legal cases in which they could freely search for information. We manipulated between subjects whether systematic search was possible or not and measured the assessment of each selected piece of information. In line with previous studies, we observe strong coherence effects in each study, in that the evidence interpretation strongly depended on the current tendency towards acquittal or conviction. In contrast to our expectation, however, people searched for information that was contrary to their current belief in the given case (i.e., disconfirmatory information search). We also observed a trend towards an interaction between both factors, in that coherence effects were slightly stronger for neutral and pro-guilty evidence when systematic information search was possible. Our results underline an unconscious striving for coherence when making complex judgments that is not easily corrected.

Proceed, With Caution: Unconscious Bias in Technical Assessment.

Proceed, With Caution: Unconscious Bias in Technical Assessment.

Can "Googling" correct misbelief? Cognitive and affective consequences of online search.

With increasing concern over online misinformation in perspective, this study experimentally examined the cognitive as well as the affective consequences of online search. Results of the two experiments using widely shared, prejudiced misinformation about an ethnic minority in Japan indicated that (a) online search reduces on average the likelihood of believing the misinformation, (b) the magnitude of the effect is larger among those who are predisposed to believe the misinformation, (c) cognitive correction is observed whether searchers are motivated to achieve a directional goal or an accuracy goal, and (d) online search deteriorates affective feeling toward the target groups of the misinformation. Theoretical implications are discussed in relation to the robustness of confirmation bias in online search and the “belief echo” in which exposure to negative misinformation continues to shape attitudes even after the misinformation has been effectively discredited.

Summaries of Papers in This Issue

Summaries of Papers in This Issue

Can self-regulation save digital platforms?

AbstractThis article explores some of the critical challenges facing self-regulation and the regulatory environment for digital platforms. We examine several historical examples of firms and industries that attempted self-regulation before the Internet. All dealt with similar challenges involving multiple market actors and potentially harmful content or bias in search results: movies and video games, radio and television advertising, and computerized airline reservation systems. We follow this historical discussion with examples of digital platforms in the Internet era that have proven problematic in similar ways, with growing calls for government intervention through sectoral regulation and content controls. We end with some general guidelines for when and how specific types of platform businesses might self-regulate more effectively. Although our sample is small and exploratory, the research suggests that a combination of self-regulation and credible threats of government regulation may yield the best results. We also note that effective self-regulation need not happen exclusively at the level of the firm. When it is in their collective self-interest, as occurred before the Internet era, coalitions of firms within the same market and with similar business models may agree to abide by a jointly accepted set of rules or codes of conduct.

On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms

For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicator-based MOEA (IB-MOEA) has specific search preferences related to its baseline QI, producing Pareto front approximations with different properties. In consequence, an IB-MOEA based on a single QI has a limited scope of multiobjective optimization problems (MOPs) in which it is expected to have a good performance. This issue is emphasized when the associated Pareto front geometries are highly irregular. In order to overcome these issues, we propose here an island-based multiindicator algorithm (IMIA) that takes advantage of the search biases of multiple IB-MOEAs through a cooperative scheme. Our experimental results show that the cooperation of multiple IB-MOEAs allows IMIA to perform more robustly (considering several QIs) than the panmictic versions of its baseline IB-MOEAs as well as several state-of-the-art MOEAs. Additionally, IMIA shows a Pareto-front-shape invariance property, which makes it a remarkable optimizer when tackling MOPs with complex Pareto front geometries.

Hey BERT! Meet the Databases: Explorations of Bidirectional Encoder Representation from Transformers Model Use in Database Search Algorithms

Bidirectional Encoder Representations from Transformers (BERT) is an open-source algorithm that allows for easier searching in search engines as it looks for natural language combinations in Google. The same algorithm could be applied to free database resources and commercial databases to make searching easier for users. This article examines the possibilities of BERT in resources other than Google. Many of our users come to database searching using Google type searches only to find controlled vocabulary is the way in which many databases are structured. If BERT is incorporated into popular databases along with controlled vocabulary, searching for information and finding it would be much easier for novice and advanced users, though careful consideration should be given to the search bias the algorithm may induce.

Observations of Atypical Habitat Use by Foothill Yellow-Legged Frogs (Rana boylii) in the Coast Range of California

Foothill Yellow-legged Frogs (Rana boylii) have experienced extreme range-wide declines in recent decades. This stream-dwelling species is thought to be in decline primarily due to the alteration of streams through anthropogenic water development, the effects of which may be exacerbated by climate change. Although R. boylii has long been considered an obligate inhabitant of perennial streams, recent observations indicate that this species exhibits greater behavioral plasticity in habitat use, particularly for reproduction. Herein, we report on several observations of R. boylii using atypical habitat for both refuge and reproduction. We found several R. boylii individuals reproducing within lentic habitat over a several year period. We also found up to 27 egg masses of R. boylii in a primarily mud-substrate intermittent stream, where the species bred and recruited individuals in 5 of 10 years. Behavioral plasticity in the use of habitat types described herein indicates a possible search bias in traditional Rana boylii surveys. Our observations may have implications for future assessment of R. boylii habitats and for species recovery.

Multi- and many-objective path-relinking: A taxonomy and decomposition approach

This paper proposes a decomposition-based path-relinking method for multi- and many-objective combinatorial optimization problems. The proposed approach, referred to as MOPR/D, considers the localization of solutions in the objective space and can handle this information to make choices regarding intermediate solutions in the path-relinking trajectory. Because it does not require aggregation functions, it does not suffer from a tendency to introduce search bias to specific regions of the Pareto front. The proposed approach was compared with seven path-relinking techniques from the literature on three differently structured combinatorial optimization problems: 0/1 multidimensional knapsack, quadratic assignment, and spanning tree. Experiments were conducted on sets of benchmark instances with up to five objectives. This study also reviewed multi-objective path generation strategies, investigated their behaviors, and a taxonomy is proposed for standardizing and classifying them.

Ideological and Partisan Bias in the Canadian Public

AbstractPartisan and affective polarization should have observable consequences in Canada, such as bias in political information search and processing. This article presents the results of three studies that test for partisan and ideological bias using the Digital Democracy Project's study of the 2019 Canadian election. Study 1 uses a conjoint experiment where respondents choose from pairs of hypothetical news stories where the slant of the source and headline are both randomized. Study 2 tests for partisan-motivated responsiveness to elite cues with a policy vignette that manipulates the presence of party elite cues and a motivational prime. Study 3 requires respondents to solve a randomly assigned numeracy task that is either political or nonpolitical in nature. Results suggest that Canadians (1) select politically congenial information, though not sources of such information, (2) follow elite cues when partisan motivation is primed and (3) evaluate evidence in ways that are biased by their ideological beliefs.

A corrected and improved symbiotic organisms search algorithm for continuous optimization

The symbiotic organisms search (SOS) algorithm is investigated and corrected by removing the benefit factors due to their biased search around the original dot. However, the removal of these benefit factors results in performance that is far inferior to the outstanding performance of the basic SOS algorithm. Accordingly, this paper suggests adopting combination schemes for the mutualistic equations in order to prevent premature convergence, and further recommends adopting lower combination rates for parasitic equations. Combination schemes are found to be not applicable to the commensal equation, as this equation is not greedy. Therefore, this paper proposes two types of combination schemes to improve the corrected SOS version in terms of achieving high early convergence speed, attaining convergence precision at a lower cost, arriving at the convergence plateau at either a lower cost or a higher level of precision, handling tests of composition functions well, and achieving competitive performance on CEC2015 test problems.

Predicting CircRNA-Disease Associations Based on Improved Weighted Biased Meta-Structure

Circular RNAs (circRNAs) are RNAs with a special closed loop structure, which play important roles in tumors and other diseases. Due to the time consumption of biological experiments, computational methods for predicting associations between circRNAs and diseases become a better choice. Taking the limited number of verified circRNA-disease associations into account, we propose a method named CDWBMS, which integrates a small number of verified circRNA-disease associations with a plenty of circRNA information to discover the novel circRNA-disease associations. CDWBMS adopts an improved weighted biased meta-structure search algorithm on a heterogeneous network to predict associations between circRNAs and diseases. In terms of leave-one-out-cross-validation (LOOCV), 10-fold cross-validation and 5-fold cross-validation, CDWBMS yields the area under the receiver operating characteristic curve (AUC) values of 0.921 6, 0.917 2 and 0.900 5, respectively. Furthermore, case studies show that CDWBMS can predict unknow circRNA-disease associations. In conclusion, CDWBMS is an effective method for exploring disease-related circRNAs.

Confirmation bias in information search, interpretation, and memory recall: evidence from reasoning about four controversial topics

Confirmation bias is often used as an umbrella term for many related phenomena. Information searches, evidence interpretation, and memory recall are the three main components of the thinking process involved in hypothesis testing most relevant to investigations of confirmation bias; yet these have rarely been explored using a unified paradigm. Therefore, this paper examines how confirmation bias works in each of these three stages of reasoning, using four controversial topics. Participants (N = 199) first indicated their attitudes and then answered tasks measuring confirmation bias. The results showed that confirmation bias was most prevalent in information search as participants tended to search for information confirming their prior attitudes. During information interpretation, confirmation bias occurred only for more polarizing topics. On the other hand, our results did not show confirmation bias in memory recall, as there was no difference in recall of information confirming or disconfirming prior attitudes for any of the topics. Although our attitudes affect the way we process information, it seems the effect varies depending on the reasoning stage, and this can have implications for debiasing strategies.

Evaluation metrics for measuring bias in search engine results

Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated using many features and sophisticated algorithms where search neutrality is not necessarily the focal point. Therefore, it is important to evaluate the search engine results with respect to bias. In this work we propose novel web search bias evaluation measures which take into account the rank and relevance. We also propose a framework to evaluate web search bias using the proposed measures and test our framework on two popular search engines based on 57 controversial query topics such as abortion, medical marijuana, and gay marriage. We measure the stance bias (in support or against), as well as the ideological bias (conservative or liberal). We observe that the stance does not necessarily correlate with the ideological leaning, e.g. a positive stance on abortion indicates a liberal leaning but a positive stance on Cuba embargo indicates a conservative leaning. Our experiments show that neither of the search engines suffers from stance bias. However, both search engines suffer from ideological bias, both favouring one ideological leaning to the other, which is more significant from the perspective of polarisation in our society.

Seeking confirmation? Biased information search and deliberation in the food domain

Individuals are often confronted with choosing between competing food attributes (e.g., healthiness vs. tastiness). To promote healthier food choices, is it important to understand what drives these choices. One potential driver of food choices is a confirmation bias, namely, a systematic bias in our approach to seeking information related to food attributes. This bias may generate avoidance of belief-disconfirming information and/or discounting of the value of belief-disconfirming arguments, both of which are documented in other domains (e.g., politics, religion). This study aimed to examine the confirmation bias in the food domain. We conducted an online study with a large sample (N = 427) stratified by sex and body mass index. Our sample, hypotheses, and analysis plan were pre-registered on the Open Science Framework. We measured pro-health and pro-taste food-related information aimed at directly testing the presence of the confirmation bias in the food domain. We also examined whether higher deliberation (indexed by higher amount of thought, lower sleepiness, and higher cognitive reflection) modulated this bias. Results showed that individuals avoided exposure to information that promoted the food attribute that they utilized less (i.e., healthiness or tastiness). Furthermore, arguments promoting less used food attributes were perceived as weaker, and this effect was enhanced for more alert individuals (i.e., less sleepy). To our knowledge, these findings provide the first evidence for a confirmation bias in the food domain and suggest that unlike other cognitive biases, higher deliberation may enhance this bias. Marketing could focus efforts on convincing consumers that healthy food is tasty, rather than convincing them to care more about health.

Officer bias, over-patrolling and ethnic disparities in stop and search.

Black and Asian people in the United Kingdom are more likely to be stopped and searched by police than White people. Following a panel of 36,000 searches by 1,100 police officers at a major English police force, we provide officer-specific measures of over-searching relative to two baselines: the ethnic composition of crime suspects officers interact with and the ethnic composition of the areas they patrol. We show that the vast majority of officers over-search ethnic minorities against both baselines. But we also find that the over-searching by individual officers cannot account for all of the over-representation of ethnic minorities in stop and search: over-patrolling of minority areas is also a key factor. Decomposing the overall search bias, we find that the over-representation of Asian people in stop and search is primarily accounted for by over-patrolling, while the over-representation of Black people is a combination of officer and patrol effects, with the larger contribution coming from biases of officers.

The Interactive Effect of Time Pressure and Client Preference on Tax Professionals' Information Search Emphasis, Judgments, and Recommendations

ABSTRACT Tax professionals commonly search large databases of information to identify tax authority necessary to provide compliance and planning advice to clients. Prior research indicates tax professionals' information search processes are subject to confirmation bias in the direction of client preferences and that this bias can lead professionals to make overly aggressive recommendations. However, very little is known about how time pressure may affect tax professionals' judgment and decision-making processes. This study contributes to practice and to the time pressure and decision bias literatures by providing theory and evidence that increasing time pressure leads to confirmation bias during tax information search and that time pressure enhanced confirmation bias affects recommendations through professionals' assessments of the evidential support for the client-preferred position. With an understanding of how time pressure can influence confirmation bias in information search, professionals and their firms can take steps to manage time pressure and its potential biasing effects.