Regulating algorithmic filtering on social media

1 Introduction . In recent years , there have been increasing calls to regulate how social media platforms algorithmically filter the content that appears on a user ’ s feed . For example , one may ask that the advertisements a user sees not be based ( explicitly or implicitly ) on their sexual orientation [ 65 ] or that content related to public health ( e.g. , COVID-19 ) does not reflect a user ’ s political affiliation [ 35 ] . However , translating a regulatory guideline into an auditing procedure has proven difficult . Developing such a method is the focus of this work . As our main contribution , we provide a general procedure such that , given a regulation , an auditor can test whether the platform complies with the regulation . Providing a general procedure is important because , without it , auditing is destined to be reactive : auditors must design ways to enforce each regulation as they arise , and there is an inevitable delay between design and enforcement in order to test the proposed solutions . On the other hand , designing a general procedure is challenging because audits can have unintended side effects on the social media ecosystem and its many stakeholders . As a result , we consider two additional questions in this work : 1 . How does the proposed auditing procedure affect the platform ’ s bottom line ? We consider whether the procedure imposes a high performance cost on the platform . 2 . How does the auditing procedure affect the user ’ s content ? We consider what type of content the platform is incentivized to show the user when the platform complies with the regulation . Our main contributions are summarized as follows . Auditing procedure . As our main contribution , we provide a procedure such that , given a regulation , an auditor can test whether the platform complies with the regulation ( Section 3 ) . We restrict our attention to regulations that can be written in counterfactual form , including the two examples given at the top of this Introduction . Namely , the procedure applies to any regulation that can be written as : 35th Conference on Neural Information Processing Systems ( NeurIPS 2021 ) . “ the filtering algorithm F should behave similarly under inputs x and x0 for all ( x , x0 ) 2 S ” . How to quantify “ similarity ” under x and x0 is crucial , and our second main contribution is to provide a precise notion of “ similarity ” in the context of algorithmic filtering ( Section 2 ) . Operationally , the auditing procedure has several desirable properties . First , it needs only black-box access to the algorithm and therefore holds even if the filtering algorithm F changes . Second , it does not require access to the platform ’ s users or their personal data . Third , its parameters are interpretable and easy to tune . Finally , the procedure is modular , which allows for many possible configurations . Provable guarantees . To audit a counterfactual regulation , we begin by observing that algorithmic filtering is powerful ( and often harmful ) because information influences decisions : the content that a user sees can affect how they vote , whether they choose to receive a vaccine , what restaurants they frequent , and more . Therefore , if one seeks to enforce a counterfactual regulation , the notion of “ similarity ” that is enforced should be with respect to the outcome of interest : the users ’ decisions . Suppose that the auditor seeks to audit content of type T ( e.g. , the advertisements or the entire feed ) . Suppose the filtering algorithm F generates content Z of type T when given inputs x , and Z 0 when given x0 . In Theorem 1 , we prove that , if F passes the audit , the decision-making of any user if shown Z and their decision-making if shown Z 0 instead are ( asymptotically ) indistinguishable . This guarantee—which we call decision robustness and formalize in Section 2—is powerful because it holds for any user and their decision-making even though the audit does not have access to the users ’ personal data ( e.g. , gender ) or knowledge about the user ’ s decision-making tendencies ( e.g. , how easily a user is influenced by restaurant advertisements ) . Providing such a strong guarantee without access to users is made possible by two well-known concepts from decision and learning theory : the hypothesis test and minimum-variance unbiased estimator . Combining these tools is one of our main technical insights and discussed in Section 4 . Cost of regulation . In Section 5.1 , we study how the audit affects a platform ’ s ability to maximize an objective function R—which we refer to as reward—and find that being audited does not necessarily place a high performance cost on the platform . Studying the cost of regulation is important because there are concerns that regulations can hurt innovation or profits , and our findings surface conditions under which a performance-regulation trade-off does not exist . Because we leave R unspecified , the analysis in Section 5.1 is applicable to any R. As examples , R could measure time spent on the platform , the number of clicks on posts , or a combination of these factors . Content diversity . In Section 5.2 , we turn our attention to how an audit would affect the users ’ content and discover an unexpected connection to content diversity . We find that , under regulation , social media platforms are incentivized to add doses of content diversity . Specifically , when faced with a regulation , it is in the platform ’ s interest to ensure that the content of type T that it shows users is sufficiently diverse along the dimension by which x and x0 differ for all ( x , x0 ) 2 S. Because content diversity is not a part of the regulatory test by design , this result is unexpected and suggests that content diversity plays a key role in aligning the interests of regulators and platforms . All proofs are given in the Appendix as well as a toy example and further discussion of the audit . 2 Problem statement . Consider a system with two agents : a social media platform and an auditor . 1 . The platform selects the content that is shown to its users using a filtering algorithm F : X ! Z such that Z = F ( x ) is the feed produced by F given inputs x 2 X . Here , a feed is a collection of content that is shown to a user , and x captures any inputs that the platform uses to filter , such as the user ’ s interaction history , social network , available content sources , and so on . Each feed Z = { z1 , . . . , zm } consists of m pieces of content , where zi 2 Rn for all i 2 [ m ] . We assume that F can be written as a generative model such that F generates the content in Z by drawing m samples from a distribution pz ( · ; ✓ ( x ) ) , where ✓ ( x ) 2 ⇥ 2 Rr is unknown.1 Note that if one is only concerned with a certain type T of content ( e.g. , the auditor only wishes to monitor advertisements ) , then one can take Z to be the content of type T that is filtered by F rather than the entire feed . 1This representation is without loss of generality . For example , any deterministic mapping from x to Z can be achieved by letting r = nm , ✓ ( x ) = ( z > 1 , . . . , z > m ) , and sequentially generating zi from the entries of ✓ ( x ) . 2 . The auditor is given a regulatory guideline that they wish to enforce . The auditor ’ s goal is to check whether the platform ’ s filtering algorithm F is in compliance with the given regulation . We assume that the auditor has black-box access to F . In other words , the auditor can run F on a set of inputs { xj } and observe its outputs { Zj = F ( xj ) } . Note that the inputs x and x0 need not correspond to real users and could represent hypothetical users . In this work , we restrict our analysis to counterfactual regulations . Specifically , the auditor is given a regulation in the form : “ The filtering algorithm F should behave similarly on content of type T under inputs x and x0 for all ( x , x0 ) 2 S . ” In the remainder of this work , we omit references to type T and refer directly to content Z . We give three examples of counterfactual regulations . Example 1 . A regulation that prohibits targeted advertisements from being based on a user ’ s indicated sexual orientation [ 65 ] can be written as “ the advertisements shown by F should be similar when given two users who are identical except for their sexual orientations ” .2 Example 2 . A regulation that requires articles containing medical advice on COVID-19 be robust to whether the user is left- or right-leaning can be framed as “ the articles that are selected by F and provide medical advice on COVID-19 should be similar for left- and right-leaning users ” . Example 3 . A regulation prescribing that a platform not sway voting preferences beyond serving as a social network can be framed as “ content about political candidates that are injected by F should be similar to the content a user would see from its social network without algorithmic filtering. ” The goal of this work is to enforce a regulation of the form “ F should behave similarly under x and x0 for all ( x , x0 ) 2 S ” . The question remains : What is an appropriate notion of “ similarity ” ? 2.1 Decision robustness . We begin by observing that algorithmic filtering is powerful ( and often harmful ) because information influences decisions : the content that a user sees can affect how they vote , whether they get vaccinated , what restaurants they frequent , what items they purchase , and more . Stated differently , if algorithmic filtering did not influence users ’ decisions , then there would be no desire to regulate it . Therefore , if one seeks to enforce a counterfactual regulation , the notion of “ similarity ” that is enforced should be with respect to the outcome of interest : the users ’ decisions . However , an auditor does not and should not have access to the users or their decisions ( e.g. , whether they get vaccinated ) . As such , the problem that the auditor faces can be stated as follows . Consider a ( hypothetical ) user . Suppose that there are two identical worlds except that the user is shown F ( x ) in one world and F ( x0 ) in the other . Because the worlds are otherwise identical , both users subsequently face an identical set of queries Q ( e.g. , where to eat , whether to get vaccinated , what to wear ) . Let D and D0 denote the ( hypothetical ) decisions that the first and second users make given queries Q . Then , the auditor enforces similarity by ensuring decision robustness as follows : F is decision-robust to ( x , x0 ) if and only if , for any user and any Q , one can not determine with high confidence that x 6= x0 from the decisions D and D0 . Decision robustness guarantees that the decision-making behavior of any user under F ( x ) and F ( x0 ) is indistinguishable with respect to ( x , x0 ) . However , ensuring decision robustness is challenging because the auditor does not have access to users or their decisions . Our objective is to provide an auditing procedure that guarantees decision robustness given only S and black-box access to F .

Summary: In this paper, the authors study issues on regulating algorithmic filtering in social networks. They describe a system involving three actors — user, auditor, and social media platform. They prove a result claiming that a regulator only needs to test regulation for the "most gullible user"; then, for a specific class of regulations, they give an algorithm that they claim tests the regulation for said user. Finally, they give some technical conditions on when regulations do not affect the reward function of the social media platform.