September 29, 2022

Recommendations for Updating the FTC’s Disclosure Guidelines to Combat Dark Patterns

Last week, CITP’s Tech Policy Clinic, along with Dr. Jennifer King, brought leading interdisciplinary academic researchers together to provide recommendations to the Federal Trade Commission on how it should update the 2013 version of its online digital advertising guidelines (the “Disclosure Guidelines”). This post summarizes the comment’s main takeaways.   

We focus on how the FTC should address the growing problem of “dark patterns,” also known as “manipulative designs.” Dark patterns are user interface techniques that benefit an online service by leading consumers into making decisions they might not otherwise make. Some dark patterns deceive consumers, while others exploit cognitive biases or shortcuts to manipulate or coerce them into choices that are not in their best interests. Dark patterns have been an important focus of research at CITP, as noted in two widely cited papers, “Measurement Methods and Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites,” and “What Makes a Dark Pattern… Dark?: Design Attributes, Normative Considerations.”

As documented in several research studies, consumers may encounter dark patterns in many online contexts, such as when making choices to consent to the disclosure of personal information or to cookies, when interacting with services and applications like games or content feeds that seek to capture and extend consumer attention and time spent, and in e-commerce, including at multiple points along a purchasing journey. Dark patterns may start with the advertising of a product or service, and can be present across the whole customer path, including sign-up, purchase, and cancellation. 

Given this landscape, we argue that FTC should provide guidance that covers the business’s entire interaction with the consumer to ensure that they are allowed to engage in free and informed transactions. Importantly, we highlight why the guidance needs to squarely address the challenge that providing additional disclosures, standing alone, will not cure the harms caused by a number of dark patterns. 

Our key recommendations include the following:

  • Offer consumers symmetric choices at crucial decision-making points. And offer that parity for crucial decision-making points across different modes of accessing the service so that consumers can exercise the same choices whether they are using web applications, mobile applications, or new forms of augmented reality applications. 
  • Do not preselect choices that favor the interest of the service provider at the expense of the consumer. 
  • Disclose material information in a manner that allows consumers to make informed decisions. Consider a heightened requirement to present information in a manner that serves the best interest of the consumer at critical decision points.
  • Follow ethical design principles when designing their interfaces. Such principles include taking account how different demographics, especially vulnerable populations, may interact with an interface by testing the usability and comprehensibility of interfaces across the different demographics. 
  • Disclose if and when businesses use personal data to shape the online choice architecture for users.  

Our comments also draw attention to how consumer protection authorities across different countries are addressing dark patterns. While each jurisdiction operates in its unique context, there is a growing set of strategies to counteract the negative effects of dark patterns that are worth drawing lessons from as the FTC develops its own guidance.

We also caution that legalistic, long form disclosures are not read or understood by the average consumer. In other words, relying on boilerplate disclosures alone will not cure most dark patterns. Instead, we point to  studies that demonstrate how disclosures shown close to the time of the decision and relevant to that decision are a lot more effective in educating consumers about their choices. As a way forward, we recommend that consumer-centered disclosures should be (1) relevant to the context of transaction, (2) understandable by the respective consumer audience/segment, and (3) actionable, in that the disclosure needs to be associated with the ability to express an informed decision.

To read our comment in its entirety, as well as those of other commentators such as a broad coalition of state attorneys general, please visit the FTC’s docket.

New Study Analyzing Political Advertising on Facebook, Google, and TikTok

By Orestis Papakyriakopoulos, Christelle Tessono, Arvind Narayanan, Mihir Kshirsagar

With the 2022 midterm elections in the United States fast approaching, political campaigns are poised to spend heavily to influence prospective voters through digital advertising. Online platforms such as Facebook, Google, and TikTok will play an important role in distributing that content. But our new study – How Algorithms Shape the Distribution of Political Advertising: Case Studies of Facebook, Google, and TikTok — that will appear in the Artificial Intelligence, Ethics, and Society conference in August, shows that the platforms’ tools for voluntary disclosures about political ads do not provide the necessary transparency the public needs. More details can also be found on our website: campaigndisclosures.princeton.edu.

Our paper conducts the first large-scale analysis of public data from the 2020 presidential election cycle to critically evaluate how online platforms affect the distribution of political advertisements. We analyzed a dataset containing over 800,000 ads about the 2020 U.S. presidential election that ran in the 2 months prior to the election, which we obtained from the ad libraries of Facebook and Google that were created by the companies to offer more transparency about political ads. We also collected and analyzed 2.5 million TikTok videos from the same time period. These ad libraries were created by the platforms in an attempt to stave off potential regulation such as the Honest Ads Act, which sought to impose greater transparency requirements for platforms carrying political ads. But our study shows that these ad libraries fall woefully short of their own objectives to be more transparent about who pays for the ads and who sees the ads, as well the objectives of bringing greater transparency about the role of online platforms in shaping the distribution of political advertising. 

We developed a three-part evaluative framework to assess the platform disclosures: 

1. Do the disclosures meet the platforms’ self-described objective of making political advertisers accountable?

2. How do the platforms’ disclosures compare against what the law requires for radio and television broadcasters?

3. Do the platforms disclose all that they know about the ad targeting criteria, the audience for the ads, and how their algorithms distribute or moderate content?

Our analysis shows that the ad libraries do not meet any of the objectives. First, the ad libraries only have partial disclosures of audience characteristics and targeting parameters of placed political ads. But these disclosures do not allow us to understand how political advertisers reached prospective voters. For example, we compared ads in the ad libraries that were shown to different audiences with dummy ads that we created on the platforms (Figure 1). In many cases, we measured a significant difference between the calculated cost-per-impression between the two types of ads, which we could not explain with the available data.

  • Figure 1. We plot the generated cost per impression of ads in the ad-libraries that were (1) targeted to all genders & ages on Google, (2) to Females, between 25-34 on YouTube, (3) were seen by all genders & ages in the US on Facebook, and (4) only by females of all ages located in California on Facebook.  For Facebook, lower & upper bounds are provided for the impressions. For Google, lower & upper bounds are provided for cost & impressions, given the extensive “bucketing” of the parameters performed by the ad libraries when reporting them, which are denoted in the figures with boxes. Points represent the median value of the boxes. We compare the generated cost-per impression of ads with the cost-per impression of a set of dummy ads we placed on the platforms with the exact same targeting parameters & audience characteristics. Black lines represent the upper and lower boundaries of an ad’s cost-per-impression as we extracted them from the dummy ads. We label an ad placement as “plausible targeting”, when the ad cost-per-impression overlaps with the one we calculated, denoting that we can assume that the ad library provides all relevant targeting parameters/audience characteristics about an ad.  Similarly, an placement labeled as `”unexplainable targeting’”  represents an ad whose cost-per-impression is outside the upper and lower reach values that we calculated, meaning that potentially platforms do not disclose full information about the distribution of the ad.

Second, broadcasters are required to offer advertising space at the same price to political advertisers as they do to commercial advertisers. But we find that the platforms charged campaigns different prices for distributing ads. For example, on average, the Trump campaign on Facebook paid more per impression (~18 impressions/dollar) compared to the Biden campaign (~27 impressions/dollar). On Google, the Biden campaign paid more per impression compared to the Trump campaign. Unfortunately, while we attempted to control for factors that might account for different prices for different audiences, the data does not allow us to probe the precise reason for the differential pricing. 

Third, the platforms do not disclose the detailed information about the audience characteristics that they make available to advertisers. They also do not explain how the algorithms distribute or moderate the ads. For example, we see that campaigns placed ads on Facebook that were not ostensibly targeted by age, but the ad was not distributed uniformly.  We also find that platforms applied their ad moderation policies inconsistently, with some instances of moderated ads being removed and some others not, and without any explanation for the decision to remove an ad. (Figure 2) 

  • Figure 2. Comparison of different instances of moderated ads across platforms. The light blue bars show how many instances of a single ad were moderated, and maroon bars show how many instances of the same ad were not. Results suggests an inconsistent moderation of content across platforms, with some instances of the same ad being removed and some others not.

Finally, we observed new forms of political advertising that are not captured in the ad libraries. Specifically, campaigns appear to have used influencers to promote their messages without adequate disclosure. For example, on TikTok, we document how political influencers, who were often linked with PACs, generated billions of impressions from their political content. This new type of campaigning still remains unregulated and little is known about the practices and relations between influencers and political campaigns.  

In short, the online platform self-regulatory disclosures are inadequate and we need more comprehensive disclosures from platforms to understand their role in the political process. Our key recommendations include:

– Requiring that each political entity registered with the FEC use a single, universal identifier for campaign spending across platforms to allow the public to track their activity.

– Developing a cross-platform data repository, hosted and maintained by a government or independent entity, that collects political ads, their targeting criteria, and the audience characteristics that received them. 

– Requiring platforms to disclose information that will allow the public to understand how the algorithms distribute content and how platforms price the distribution of political ads. 

– Developing a comprehensive definition of political advertising that includes influencers and other forms of paid promotional activity.

 A Multi-pronged Strategy for Securing Internet Routing

By Henry Birge-Lee, Nick Feamster, Mihir Kshirsagar, Prateek Mittal, Jennifer Rexford

The Federal Communications Commission (FCC) is conducting an inquiry into how it can help protect against security vulnerabilities in the internet routing infrastructure. A number of large communication companies have weighed in on the approach the FCC should take. 

CITP’s Tech Policy Clinic convened a group of experts in information security, networking, and internet policy to submit an initial comment offering a public interest perspective to the FCC. This post summarizes our recommendations on why the government should take a multi-pronged strategy to promote security that involves incentives and mandates. Reply comments from the public are due May 11.

The core challenge in securing the internet routing infrastructure is that the original design of the network did not prioritize security against adversarial attacks. Instead, the original design focused on how to route traffic through decentralized networks with the goal of delivering information packets efficiently, while not dropping traffic. 

At the heart of this routing system is the Border Gateway Protocol (BGP), which allows independently-administered networks (Autonomous Systems or ASes) to announce reachability to IP address blocks (called prefixes) to neighboring networks. But BGP has no built-in mechanism to distinguish legitimate routes from bogus routes. Bogus routing information can redirect internet traffic to a strategic adversary, who can launch a variety of attacks, or the bogus routing can lead to accidental outages or performance issues. Network operators and researchers have been actively developing measures to counteract this problem.

At a high level, the current suite of BGP security measures depend on building systems to validate routes. But for these technologies to work, most participants have to adopt them or the security improvements will not be realized. In other words, it has many of the hallmarks of a “chicken and egg” situation. As a result, there is no silver bullet to address routing security.

Instead, we argue, the government needs a cross-layer strategy that embraces pushing different elements of the infrastructure to adopt security measures that protect legitimate traffic flows using a carrot-and-stick approach. Our comment identifies specific actions Internet Service Providers, Content Delivery Networks and Cloud Providers, Internet Exchange Points, Certificate Authorities, Equipment Manufacturers, and DNS Providers should take to improve security. We also recommend that the government funds and supports academic research centers that collect real-time data from a variety of sources that measure traffic and how it is routed across the internet.  

We anticipate several hurdles to our recommended cross-layer approach: 

First, to mandate the cross-layer security measures, the FCC has to have regulatory authority over the relevant players. And, to the extent a participant does not fall under the FCC’s authority, the FCC should develop a whole-of-government approach to secure the routing infrastructure.

Second, large portions of the internet routing infrastructure lie outside the jurisdiction of the United States. As such, there are international coordination issues that the FCC will have to navigate to achieve the security properties needed. That said, if there is a sufficient critical mass of providers who participate in the security measures, that could create a tipping point for a larger global adoption.

Third, the package of incentives and mandates that the FCC develops has to account for the risk that there will be recalcitrant small and medium sized firms who might undermine the comprehensive approach that is necessary to truly secure the infrastructure.

Fourth, while it is important to develop authenticated routes for traffic to counteract adversaries, there is an under-appreciated risk from a flipped threat model – the risk that an adversary takes control of an authenticated node and uses that privileged position to disrupt routing. There are no easy fixes to this threat – but an awareness of this risk can allow for developing systems to detect such actions, especially in international contexts.  

Holding Purveyors of “Dark Patterns” for Online Travel Bookings Accountable

Last week, my former colleagues at the New York Attorney General’s Office (NYAG), scored a $2.6 million settlement with Fareportal – a large online travel agency that used deceptive practices, known as “dark patterns,” to manipulate consumers to book online travel.

The investigation exposes how Fareportal, which operates under several brands, including CheapOair and OneTravel — used a series of deceptive design tricks to pressure consumers to buy tickets for flights, hotels, and other travel purchases. In this post, I share the details of the investigation’s findings and use them to highlight why we need further regulatory intervention to prevent similar conduct from becoming entrenched in other online services.

The NYAG investigation picks up on the work of researchers at Princeton’s CITP that exposed the widespread use of dark patterns on shopping websites. Using the framework we developed in a subsequent paper for defining dark patterns, the investigation reveals how the travel agency weaponized common cognitive biases to take advantage of consumers. The company was charged under the Attorney General’s broad authority to prohibit deceptive acts and practices. In addition to paying $2.6 million, the New York City-based company agreed to reform its practices.

Specifically, the investigation documents how Fareportal exploited the scarcity bias by displaying, next to the top two flight search results, a false and misleading message about the number of tickets left for those flights at the advertised price. It manipulated consumers through adding 1 to the number of tickets the consumer had searched for to show that there were only X+1 tickets left at that price. So, if you searched for one round trip ticket from Philadelphia to Chicago, the site would say “Only 2 tickets left” at that price, while a consumer searching for two such tickets would see a message stating “Only 3 tickets left” at the advertised price. 

In 2019, Fareportal added a design feature that exploited the bandwagon effect by displaying how many other people were looking at the same deal. The site used a computer-generated random number between 28 and 45 to show the number of other people “looking” at the flight. It paired this with a false countdown timer that displayed an arbitrary number that was unrelated to the availability of tickets. 

Similarly, Fareportal exported its misleading tactics to the making of hotel bookings on its mobile apps. The apps misrepresented the percentage of rooms shown that were “reserved” by using a computer-generated number keyed to when the customer was trying to book a room. So, for example, if the check-in date was 16-30 days away, the message would indicate that between 41-70% of the hotel rooms were booked, but if it was less than 7 days away, it showed that 81-99% of the rooms were reserved. But, of course, those percentages were pure fiction. The apps used a similar tactic for displaying the number of people “viewing” hotels in the area. This time, they generated the number based on the nightly rate for the fifth hotel returned in the search by using the difference between the numerical value of the dollar figure and the numerical value of the cents figure. (If the rate was $255.63, consumers were told 192 people were viewing the hotel listings in the area.)

Fareportal used these false scarcity indicators across its websites and mobile platforms for pitching products such as travel protection and seat upgrades, through inaccurately representing how many other consumers that had purchased the product in question. 

In addition, the NYAG charged Fareportal with using a pressure tactic of making consumers accept or decline purchase a travel protection policy to “protect the cost of [their] trip” before completing a purchase. This practice is described in the academic literature as a covert pattern that uses “confirmshaming” and “forced action” to influence choices. 

Finally, the NYAG took issue with how Fareportal manipulated price comparisons to suggest it was offering tickets at a discounted price, when in fact, most of the advertised tickets were never offered for sale at the higher comparison price. The NYAG rejected Fareportal’s attempt to use a small pop-up to cure the false impression conveyed by the visual slash-through image that conveyed the discount. Similarly, the NYAG called out how Fareportal hid its service fees by disguising them as being part of the “Base Price” of the ticket rather than the separate line item for “Taxes and Fees.” These tactics are described in the academic literature as using “misdirection” and “information hiding” to influence consumers. 


The findings from this investigation illustrate why dark patterns are not simply aggressive marketing practices, as some commentators contend, but require regulatory intervention. Specifically, such shady practices are difficult for consumers to spot and to avoid, and, as we argued, risk becoming entrenched across different travel sites who have the incentive to adopt similar practices. As a result, Fareportal, unfortunately, will not be the first or the last online service to deploy such tactics. But this creates an opportunity for researchers, consumer advocates, and design whistleblowers to step forward and spotlight such practices to protect consumers and help create a more trustworthy internet.    

Calling for Investing in Equitable AI Research in Nation’s Strategic Plan

By Solon Barocas, Sayash Kapoor, Mihir Kshirsagar, and Arvind Narayanan

In response to the Request for Information to the Update of the National Artificial Intelligence Research and Development Strategic Plan (“Strategic Plan”) we submitted comments  providing suggestions for how the Strategic Plan for government funding priorities should focus resources to address societal issues such as equity, especially in communities that have been traditionally underserved. 

The Strategic Plan highlights the importance of investing in research about developing trust in AI systems, which includes requirements for robustness, fairness, explainability, and security. We argue that the Strategic Plan should go further by explicitly including a commitment to making investments in research that examines how AI systems can affect the equitable distribution of resources. Specifically, there is a risk that without such a commitment, we make investments in AI research that can marginalize communities that are disadvantaged. Or, even in cases where there is no direct harm to a community, the research support focuses on classes of problems that benefit the already advantaged communities, rather than problems facing disadvantaged communities.  

We make five recommendations for the Strategic Plan:  

First, we recommend that the Strategic Plan outline a mechanism for a broader impact review when funding AI research. The challenge is that the existing mechanisms for ethics review of research projects – Institutional Review Boards (“IRB”) –  do not adequately identify downstream harms stemming from AI applications. For example, on privacy issues, an IRB ethics review would focus on the data collection and management process. This is also reflected in the Strategic Plan’s focus on two notions of privacy: (i) ensuring the privacy of data collected for creating models via strict access controls, and (ii) ensuring the privacy of the data and information used to create models via differential privacy when the models are shared publicly. 

But both of these approaches are focused on the privacy of the people whose data has been collected to facilitate the research process, not the people to whom research findings might be applied. 

Take, for example, the potential impact of face recognition for detecting ethnic minorities. Even if the researchers who developed such techniques had obtained approval from the IRB for their research plan, secured the informed consent of participants, applied strict access control to the data, and ensured that the model was differentially private, the resulting model could still be used without restriction for surveillance of entire populations, especially as institutional mechanisms for ethics review such as IRBs do not consider downstream harms during their appraisal of research projects. 

We recommend that the Strategic Plan include as a research priority supporting the development of alternative institutional mechanisms to detect and mitigate the potentially negative downstream effects of AI systems. 

Second, we recommend that the Strategic Plan include provisions for funding research that would help us understand the impact of AI systems on communities, and how AI systems are used in practice. Such research can also provide a framework for informing decisions on which research questions and AI applications are too harmful to pursue and fund. 

We recognize that it may be challenging to determine what kind of impact AI research might have as it affects a broad range of potential applications. In fact, many AI research findings will have dual use: some applications of these findings may promise exciting benefits, while others would seem likely to cause harm. While it is worthwhile to weigh these costs and benefits, decisions about where to invest resources should also depend on distributional considerations: who are the people likely to suffer these costs and who are those who will enjoy the benefits? 

While there have been recent efforts to incorporate ethics review into the publishing processes of the AI research community, adding similar considerations to the Strategic Plan would help to highlight these concerns much earlier in the research process. Evaluating research proposals according to these broader impacts would help to ensure that ethical and societal considerations are incorporated from the beginning of a research project, instead of remaining an afterthought.

Third, our comments highlight the reproducibility crisis in fields adopting machine learning methods and the need for the government to support the creation of computational reproducibility infrastructure and a reproducibility clearinghouse that sets up benchmark datasets for measuring progress in scientific research that uses AI and ML. We suggest that the Strategic Plan borrow from the NIH’s practices to make government funding conditional on disclosing research materials, such as the code and data, that would be necessary to replicate a study.

Fourth, we focus attention on the industry phenomenon of using a veneer of AI to lend credibility to pseudoscience as AI snake oil. We see evaluating validity as a core component of ethical and responsible AI research and development. The strategic plan could support such efforts by prioritizing funding for setting standards for and making tools available to independent researchers to validate claims of effectiveness of AI applications. 


Fifth, we document the need to address the phenomenon of “runaway datasets” — the practice of broadly releasing datasets used for AI applications without mechanisms of oversight or accountability for how that information can be used. Such datasets raise serious privacy concerns and they may be used to support research that is counter to the intent of the people who have contributed to them. The Strategic Plan can play a pivotal role in mitigating these harms by establishing and supporting appropriate data stewardship models, which could include supporting the development of centralized data clearinghouses to regulate access to datasets.