FAT Approaches Internet Platforms Can Implement

Amid the various ways to promote FAT around high-risk algorithmic systems, there are two that internet platforms are best suited to implement: ML documentation frameworks and transparency reports. This section includes a discussion of three different types of ML documentation frameworks (Datasheets for Datasets, Model Cards, and FactSheets), and two types of transparency reports (content takedown transparency reports and political ad transparency reports). It discusses the strengths and limitations of these two approaches, and how these mechanisms contribute to overall efforts to promote FAT around high-risk algorithmic systems.

Currently, numerous industries implement standardized documentation methods to communicate the function and quality of a given system. For example, civil engineers use standardized engineering drawing practices to prepare structural plans that can be understood across the industry.¹ However, despite some proposals (discussed below), there are currently no standardized documentation procedures in the ML community,² hindering efforts to promote FAT around ML and algorithmic systems. As noted previously, documentation throughout the ML training lifecycle is important because it allows developers to track, revisit, and understand past design decisions and enables external reviewers to conduct a substantive audit of the algorithmic system. Although internet platforms have been the most prominent target for such a documentation framework, these methods could also be extended to other commercial and even government uses.

Datasheets for Datasets

In 2018, a team of Microsoft researchers proposed Datasheets for Datasets, a documentation framework primarily for companies in which each dataset in the ML process is accompanied by a datasheet that expresses the dataset’s motivation, composition, collection process, and recommended uses, among other characteristics.³ The Datasheets for Datasets framework helps facilitate meaningful communication between the creators and the consumers of datasets, encouraging creators to carefully reflect on the process of creating, distributing, and maintaining a dataset and empowering consumers with the facts they need about it. This is important given that flawed datasets can generate harm within an ML model or algorithmic system. However, should an individual use the dataset outside of the creator’s intended use and guidance, there is no clear mechanism for recourse.

FactSheets

Researchers at IBM have proposed the FactSheets framework, which relies on documents called FactSheets to collect relevant information about the development and deployment of an AI model or service in a common, transparent location. The documented information includes the purpose of the model, the dataset used to train the model, limitations of the model’s performance, and many other factors. Throughout the life cycle of the AI model, facts are recorded by various stakeholders in the process, including the business owner, data scientist, model validator, and operations engineer. FactSheets are tailored to the particular documentation needs of different audiences (e.g., model developers, regulators, consumers, etc.) and therefore may vary in content and format (e.g., full report, tabular view, slide format, etc.) depending on the targeted audience, even for the same AI tool.⁴

According to IBM, FactSheets enable the governance of algorithmic systems by providing enterprises the ability to track and understand information throughout the AI life cycle, analyze this information, specify policies to be adhered to during AI development and deployment, and facilitate communication and collaboration among stakeholders at various points in the AI life cycle.

Model Cards

Finally, researchers at Google have proposed using “model cards” to clarify the intended use cases of ML models and curtail the use of these models in situations for which they are not appropriate. Model cards are short documents which accompany trained ML models, providing evaluations of the models that are related to a number of conditions, including how the model fairs across different cultural, demographic, or relevant phenotypic groups. Model cards also outline the intended use cases of the model and include information on the performance evaluation procedures,⁵ the motivation behind chosen performance metrics, group definitions, and other factors that relate to bias, fairness, and inclusion.

Model cards can provide value to a range of stakeholders: they can inform policymakers on when an ML system would operate appropriately and when the system would fail, help permit developers to compare and contrast model results to better train their own systems, and empower individuals who have been negatively impacted by a system to understand how it works and how to pursue remediation.⁶ Model cards can thus serve as a valuable mechanism for promoting transparency between developers, users, and other stakeholders around how automated systems are developed and deployed across industries. The framework is also flexible and can therefore be applied in different contexts, to different stakeholders.⁷ Thus far, model cards have primarily been explored in the corporate context by internet platforms and associated researchers.⁸

However, model cards are limited in that their value is dependent on the creator of cards. If the card creator is not fully transparent, the model card will not be useful. Further, although a model card creator may outline their intended use cases for a model, there is nothing stopping another individual or entity from deploying a model in inappropriate ways.⁹

Datasheets for datasets, fact sheets, and model cards are important steps toward universal documentation practices and promoting FAT around algorithmic systems—some combination of them may be useful—but individually there is still room for improvement in each framework. In comparison to fact sheets, model cards are missing some critical information about an ML model, such as robustness (how stable the model’s performance is in a variety of environments) and substantive information about bias present in the model.

The Partnership on AI (PAI), a multi-stakeholder organization that aims to identify best practices for AI development and deployment, is working on increasing transparency and accountability around internet platforms’ use of algorithmic systems with ML system documentation through their ongoing initiative called the Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles (ABOUT ML).¹⁰ ABOUT ML seeks to synthesize best practices for data and systems documentation, building on datasheets for datasets, fact sheets, and model cards. These kinds of multi stakeholder efforts are promising, but they need to be inclusive and account for stakeholder power dynamics in order to generate meaningful outcomes.

Over the past decade, transparency reports have become an effective mechanism for obtaining transparency and accountability from internet platforms and companies in other industries. Originally, internet platforms published transparency reports to outline the scope and scale of government requests for user data they received.¹¹ This practice became more common after the Snowden revelations in 2013, after which a broad range of internet and telecommunications companies began issuing reports. Today, transparency reporting on government requests for user data is considered an industry-wide best practice for internet platforms.

Over the past several years, internet platforms and some telecommunications companies have expanded their transparency reporting to include data on government requests for content takedowns and network shutdowns.¹² More recently, a handful of internet companies that host user-generated content have also begun reporting on how they enforce their content policies when moderating content.¹³

Some of these transparency reports include data that relates to how these companies use AI, but only in the content moderation context. For example, Facebook’s Community Standards Enforcement Report (CSER) includes a metric called “proactive rate,” which outlines how much content the platform proactively removed using human reviewers and automated tools (rather than relying on user reports).¹⁴ However, the metrics shared are relatively broad, and therefore provide limited insight into how the platform uses AI and ML for content detection and moderation. Similarly, YouTube details the number of videos and comments it has removed and breaks these figures down by source of first detection in its community guidelines enforcement report.¹⁵

Although internet platforms rely on AI and ML tools to facilitate their content moderation processes, these companies provide very little transparency around how these tools are used and what impact they have.¹⁶ Additionally, out of the handful of companies that issue transparency reports on how they enforce their content policies, only Facebook and Twitter report on their use of automated tools.¹⁷

After the 2016 U.S. presidential election, internet platforms began publishing a new type of transparency report that covered their online advertising operations. This new form of reporting emerged in response to calls for platforms to be more transparent around their algorithmic advertising systems, especially after these advertising systems were used to sow discord during the electoral cycle.¹⁸ In addition, platforms such as Facebook have received additional scrutiny from policymakers and researchers after investigations revealed its algorithmic ad targeting and delivery service enabled the discriminatory targeting of ads, including in the housing, employment, and credit contexts.¹⁹

In response to mounting pressure from policymakers and civil society on platforms to provide transparency around their algorithmic advertising operations, companies such as Facebook, Google, and Reddit started issuing ad transparency reports, also known as ad libraries. Facebook’s ad library includes information on its housing, employment, credit, issues, elections, and political ads. Google and Reddit’s reports focus on political ads.

Although these ad transparency reports are a valuable first step toward providing insight into how these companies’ ad targeting and delivery systems work, they offer very little granular information on the algorithms themselves. Facebook’s ad library includes information about the impressions an ad has accrued (Facebook defines an impression as the number of times an ad was seen on a screen) and aggregate information on the age and gender of users who were shown an ad, among other metrics.²⁰ Google’s political-ad transparency report includes data on impressions and the targeting criteria an advertiser selected before running an ad.²¹ Reddit’s subreddit r/RedditPoliticalAds offers similar data on impressions and geo- and subreddit targeting.²² None of these reports, however, provide granular information on the reach and engagement of an ad (e.g., how many likes, shares, and video views an ad received) or a comparison of what audience segments the ad initially targeted and what audience segments eventually received the ad.²³ This information is critical to understanding the role advertising algorithms play in facilitating online harms, including discrimination. Companies—including Facebook—have stated that they are unable to share such granular information publicly due to privacy concerns.²⁴ However, numerous researchers have suggested safeguards that could enable these disclosures to be made responsibly.²⁵

Thus far, transparency reports have been a valuable mechanism for obtaining aggregate data from internet platforms around their privacy and freedom of expression commitments, but improvements are needed for this practice to be a valuable method for promoting FAT around platform use of algorithms. Companies need to report metrics that are more directly related to the use, accuracy, and impact of algorithmic tools in content moderation and digital advertising systems, such as error rates. One of the challenges here is that platform use of automated tools is not always binary—when moderating content, a platform may use automated tools to detect content and route it to a human reviewer who will make the relevant decision, or vice versa—so crafting meaningful metrics that capture when automated tools are in use and when they are not can be difficult. However, companies can publish qualitative information explaining how and when they use these tools during the content moderation process nonetheless. Platforms can also publish quantitative data that outlines when automated tools were used for certain purposes, such as to flag content or to remove it. In addition, transparency reporting is an expensive and labor-intensive process, especially considering that not all platforms have the necessary data collection structures already in place. As a result, some platforms have asserted that they have to triage requests for new metrics they receive and prioritize ones they determine will provide the most meaningful transparency (and are beneficial to their own business priorities).

However, these challenges can be overcome to develop transparency reporting into an effective mechanism for promoting FAT around both high-risk systems and lower risk AI systems. Recognizing these obstacles, civil society and advocates should come together to identify a set of metrics related to content moderation and digital advertising that platforms should prioritize when issuing transparency reports. One example of this is the Santa Clara Principles on Transparency and Accountability in Content Moderation, which include some metrics that touch on the use of automated tools during content moderation.²⁶ Ranking Digital Rights’s Corporate Accountability Index also includes numerous indicators related to transparency reporting and the use of automated tools for content moderation and curation.²⁷ As advocates continue to think about key metrics in this regard and in relation to other forms of content curation, such as content ranking and recommendation systems, they should identify a set of priority metrics that platforms should initially focus on and how they can expand on those in the future.²⁸ Such conversations will also help clarify what aggregate data would be most meaningful to have in order to promote algorithmic FAT around the use of content moderation and digital advertising systems.

Policymakers in the European Union²⁹ and the United States³⁰ are also exploring whether transparency reporting around content moderation and digital advertising should be mandated by legislation, and these conversations should continue and be given proper attention. As noted above, some experts have also suggested establishing a regulator that would focus on transparency disclosures and information sharing around algorithmic systems that have been deemed as warranting additional scrutiny.³¹ Transparency reporting could be part of these proposed regulatory efforts.

Internet platforms should also supplement transparency reporting efforts by publishing accessible and easy to understand algorithmic-system use policies, which explain to consumers how the company uses algorithms and for what purposes. Companies should also enable users to determine whether and how their personal data is used to train these systems, to understand what data points are used to inform the companies’ algorithmic systems, and to opt out of using these systems altogether where possible (e.g., they should be able to opt out of receiving an algorithmically curated news feed). Further, companies should establish mechanisms for consumers to provide feedback on adverse outcomes that result from an algorithmic system. Some platforms currently offer this for content moderation decisions, which can be made using algorithmic systems. On platforms such as Facebook, Twitter, and YouTube, users can appeal the suspension or removal of their content and accounts in certain cases.³² This is also an important accountability mechanism.