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: This paper studies the problem of auditing whether algorithmic filtering implementations respect a given regulation. The authors propose a simple hypothesis test that regulators could use to determine compliance given black-box access to a filtering algorithm. They show that their algorithm has desirable asymptotic properties. Finally, the authors show that under their framework platforms are incentivized to provide diverse content to their users.