Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems (2020)

Abstract: Cognitive biases in the context of consuming online information filtered by recommender systems may lead to sub-optimal choices. One approach to mitigate such biases is through interface and interaction design. This survey reviews studies focused on cognitive bias mitigation of recommender system users during two processes: 1) item selection and 2) preference elicitation. It highlights a number of promising directions for Natural Language Generation research for mitigating cognitive bias including: the need for personalization, as well as for transparency and control.

## introduction decision-making at an individual, business, and societal levels is influenced by online news and social media. filtering and ranking algorithms such as recommender systems are used to support these decisions. further, individual cognitive selection strategies and homogeneous networks can amplify bias in customized recommendations, and influence which information we are exposed to @xcite . biased exposure to online information is known to accelerate extremism and the spread of misinformation @xcite . ultimately, these undesirable negative consequences of information filtering diminish the quality of public discourse and thus can pose a threat to democracy @xcite . one strategy for bias mitigation would be to raise users' awareness of filtering mechanisms and potential cognitive biases. approaches going one step further than creating awareness, actively nudge users in a direction of less biased information selection and diversification. explanations and nudges for mostly non-expert users of recommender systems in the domains of news and social media have to be designed in a way that they are understood intuitively, e.g., using natural language @xcite . to our knowledge, no previous work has summarized cognitive bias mitigation in the context of recommender systems. in this paper, we aim to identify research gaps and opportunities to improve natural language explanation interfaces that mitigate cognitive biases. we do this by providing an overview of approaches to mitigate cognitive bias of recommender system users in the domains of news and social media. we review the literature in the field and summarize ways of measuring bias and mitigation approaches for different biases in different contexts. we also consider how these occur at different stages of the recommendation process. in sum, we address the following research questions (rqs): in the next section, we introduce the method used in our literature review. then, in section 3, we analyze the resulting papers and identify commonalities. we see that human bias mitigation using natural language generation in recommender systems is still under-explored despite explanations being successfully applied in the fields of persuasive technology and argumentation @xcite . so, in section 4 we take a constructive approach and discuss promising directions for natural language generation (nlg) research, before concluding in section 5. ## methodology to find relevant literature for this survey, we defined inclusion criteria as a search string which we ran through the databases springerlink ( http://link.springer.com ) and acm digital library ( https://dl.acm.org ) in july 2020. these two databases are established and comprehensive databases in the field of computer science, and support complex search strings. the search results were filtered by scanning title, abstract, and discussion. inclusion criteria: our search string covers four main concepts: (1) bias-related; (2) targetsystem-related; (3); domain-related; (4) mitigationrelated. the terms used for each concept are: (1) ("cognitive bias" or "human bias" or "confirmation bias" or "availability bias" or "backfire effect" or "homophily" or "affinity bias" or "decoy effect" or "selective exposure" or "false consensus effect" or "saliency bias") and (2) ("recommender" or "recommendation") and (3) ("news" or "social media" or "search" or "information seeking") and (4) ("mitigat*" or "debiasing" or "reduce" or "explainable artificial intelligence" or "xai" or "intelligent user interface" or "iui" or "natural language"). this search resulted in 257 hits. exclusion criteria: papers are excluded if they do not: a) focus on recommender systems in the domains of news, social media, or search (40 excluded); b) do not propose a mitigation approach for human bias (137); c) do not present a user study (66); d) do not include measures of bias (5); e) we have no access to the full paper (5). these criteria lead to the exclusion of 253 papers, resulting in the four papers discussed in the remained of this paper (see ## analysis in this section we analyze and compare the four resulting papers based on five aspects which were chosen to answer the research questions: (rq1) objective: context and objective of the paper and bias: type of cognitive bias investigated; (rq2) measure: approach for measuring bias; (rq3) mitigation: approach of bias mitigation; and (rq4) evaluation: evaluation of the mitigation approach and moderating factors. further, the explicit indication of author position information aimed at encouraging attention to diverse viewpoints and motivating users to select attitude-challenging information. graells-garrido et al. (2016) recommended diverse profiles with shared latent topics and displayed visualizations of the user's own data portrait in the form of wordclouds with interests and opinions to explain the given profile recommendations and mitigate confirmation bias and homophily. profile recommendations were presented in the form of visual clusters of accounts with shared latent intermediary topics, from which the user could select accounts for exploration. this approach aimed to overcome cognitive dissonance produced by direct approaches of exposure to challenging information. the aim was to provide context to a given recommendations, both in form of the user's own data profile and the basis of a shared intermediary topic, to give the new connection a chance. another approach to mitigate homophily in addition to position biases was chosen by @xcite , who presented scientists as points in a two-dimensional scatterplot. the position of a point was calculated by social (co-authorship) and academic (publication content) feature similarity (0 -100 %) between user and scholar. meaningful feature combinations, defined by higher degrees of feature similarities, were highlighted through color-coding. this approach aimed to enable the presentation of more than one recommendation aspect, to guide conference attendee's attention to areas of scientists with meaningful feature combinations, and overall, to promote diversity of profile exploration. @xcite propose input methods and interfaces for preference elicitation which result in equal mental preference model and system preference representation to achieve a mitigation of framing, anchoring and loss aversion biases. they investigated different methods of preference elicitation, such as rating with a nine point likert scale (like to dislike), ordering, navigational (receiving immediate feedback after changing preference for one item), and affective rating. in summary, the mitigation approaches of confirmation bias and homophily use the visual display of information to increase users' awareness for itemfeatures of interest (e.g., position valence, similarity,..) and to encourage and facilitate the intuitive exploration of diverse items. approaches include multidimensional feature representation plots, and additional highlighting in form of color-coding or clustering of meaningful feature combinations. two studies aim to enable users to understand contingencies between preferences, item selections and recommendation outcome and thus to a certain degree explaining recommendations. they do this by visually displaying the system's user model in form of a word cloud or an interest profile, preference summary, value chart or outcome view. in summary, multiple studies highlight that no one-size-fits-all mitigation approach exists due to moderating user-related factors, such as the accuracy motive, diversity seeking or challenge averseness, motivation, political involvement and opinion. thus the authors emphasize that interfaces should thus be designed to be personalizable. in addition, the need for transparent and interactive interface designs which allow control of user-profile and recommendations was highlighted. ## discussion in this paper, we reviewed interface-based approaches for the mitigation of confirmation bias, homophily, position bias, framing, anchoring, and loss aversion (rq1). to measure bias, the studies compared the effect of an intervention with a baseline system on a set of metrics (rq2). the reviewed studies applied interactive multidimensional visualizations, rearranging, sorting, and highlighting through color-coding and size to increase users' awareness for diverse features, to facilitate and increase exploration of recommended items, and to align the system's user model with the user's mental preference model (rq3). during the evaluation of the approaches (rq4), multiple user-related factors that moderated the effectiveness of the reviewed mitigation approaches were identified. consequently, the studies highlighted the need for personalized interfaces that can adapt to these factors. they include users' accuracy motive, motivation, political involvement, and prior opinions on recommended items or topics, all measured with tailor-made questionnaires or inferred from the user's behavior. overall, transparency, control, as well as immediate feedback were found to enhance the users' understanding and to mitigate cognitive bias. while the surveyed methods are within graphical interfaces, they help to uncover research questions for future studies in all interactive interfaces, also for natural language-based mitigation strategies: our literature review also suggests that bias mitigation strategies using natural language could be used at different stages of interaction: a) conversational preference elicitation, b) pre-evaluation and explanation of recommended items, or c) to motivate behavior modifications for bias mitigation. such interactions could promote the users' understanding of their profiles and the functioning of the system. using nlg to increase user-control on the user-profile, algorithmic parameters, and the recommendation outcomes @xcite , appears to be a promising way to mitigate cognitive biases. ## conclusion the analysed studies demonstrate effective approaches of implementing and evaluating interfacebased cognitive bias mitigation for recommender system users. on this basis, we suggest promising areas for future research for bias mitigation using interactive nlg: personalization of explanations, and more immediate transparency and control.