Our last AI post on this blog, the New (if Decidedly Not ‘Final’) Frontier of
Artificial Intelligence Regulation, touched on both the Federal
Trade Commission’s (FTC) April 19, 2021, AI guidance and the European
Commission’s proposed AI Regulation. The FTC’s 2021
guidance referenced, in large part, the FTC’s April 2020 post
Artificial Intelligence and Algorithms.” The recent FTC
guidance also relied on older FTC work on AI, including a January
2016 report, “Big Data: A Tool for Inclusion or
Exclusion?,” which in turn followed a September 15, 2014,
workshop on the same topic. The Big Data workshop addressed data
modeling, data mining and analytics, and gave us a prospective look
at what would become an FTC strategy on AI.
The FTC’s guidance begins with the data, and the 2016
guidance on big data and subsequent AI development addresses this
most directly. The 2020 guidance then highlights important
principles such as transparency, explain-ability, fairness,
accuracy and accountability for organizations to consider. And the
2021 guidance elaborates on how consent, or opt-in, mechanisms work
when an organization is gathering the data used for model
Taken together, the three sets of FTC guidance – the 2021, 2020,
and 2016 guidance ? provide insight into the FTC’s approach to
organizational use of AI, which spans a vast portion of the data
life cycle, including the creation, refinement, use and back-end
auditing of AI. As a whole, the various pieces of FTC guidance also
provide a multistep process for what the FTC appears to view as
responsible AI use. In this post, we summarize our takeaways from
the FTC’s AI guidance across the data life cycle to provide a
practical approach to responsible AI deployment.
– Evaluation of a data set should assess the quality of
the data (including accuracy, completeness and representativeness)
? and if the data set is missing certain population data, the
organization must take appropriate steps to address and remedy that
– An organization must honor promises made to consumers
and provide consumers with substantive information about the
organization’s data practices when gathering information for AI
purposes (2016). Any related opt-in mechanisms for such data
gathering must operate as disclosed to consumers (2021).
– An organization should recognize the data compilation
step as a “descriptive activity,” which the FTC defines
as a process aimed at uncovering and summarizing “patterns or
features that exist in data sets” – a reference to data mining scholarship (2016) (note that the
FTC’s referenced materials originally at mmds.org are now
– Compilation efforts should be organized around a life
cycle model that provides for compilation and consolidation before
moving on to data mining, analytics and use (2016).
– An organization must recognize that there may be
uncorrected biases in underlying consumer data that will surface in
a compilation; therefore, an organization should review data sets
to ensure hidden biases are not creating unintended discriminatory
– An organization should maintain reasonable security over
consumer data (2016).
– If data are collected from individuals in a deceitful or
otherwise inappropriate manner, the organization may need to delete
the data (2021).
Model and AI Application Selection
– An organization should recognize the model and AI
application selection step as a predictive activity, where an
organization is using “statistical models to generate new
data” – a reference to predictive analytics scholarship (2016).
– An organization must determine if a proposed data model
or application properly accounts for biases (2016). Where there are
shortcomings in the data model, the model’s use must be
accordingly limited (2021).
– Organizations that build AI models may “not sell
their big data analytics products to customers if they know or have
reason to know that those customers will use the products for
fraudulent or discriminatory purposes.” An organization must,
therefore, evaluate potential limitations on the provision or use
of AI applications to ensure there is a “permissible
purpose” for the use of the application (2016).
– Finally, as a general rule, the FTC asserts that under
the FTC Act, a practice is patently unfair if it causes more harm
than good (2021).
– Organizations must design models to account for data
– Organizations must consider whether their reliance on
particular AI models raises ethical or fairness concerns
– Organizations must consider the end uses of the models
and cannot create, market or sell “insights” used for
fraudulent or discriminatory purposes (2016).
Model Testing and Refinement
– Organizations must test the algorithm before use (2021).
This testing should include an evaluation of AI outcomes
– Organizations must consider prediction accuracy when
using “big data” (2016).
– Model evaluation must focus on both inputs
and AI models may not discriminate against a
protected class (2020).
– Input evaluation should
include considerations of ethnically based factors or proxies for
– Outcome evaluation is
critical for all models, including facially neutral models.
– Model evaluation should consider alternative models, as
the FTC can challenge models if a less discriminatory alternative
would achieve the same results (2020).
– If data are collected from individuals in a deceptive,
unfair, or illegal manner, deletion of any AI models or algorithms
developed from the data may also be required (2021).
Front-End Consumer and User Disclosures
– Organizations must be transparent and not mislead
consumers “about the nature of the interaction” ? and not
utilize fake “engager profiles” as part of their AI
– Organizations cannot exaggerate an AI model’s
efficacy or misinform consumers about whether AI results are fair
or unbiased. According to the FTC, deceptive AI statements are
– If algorithms are used to assign scores to consumers, an
organization must disclose key factors that affect the score,
rank-ordered according to importance (2020).
– Organizations providing certain types of reports through
AI services must also provide notices to the users of such reports
– Organizations building AI models based on consumer data
must, at least in some circumstances, allow consumers access to the
information supporting the AI models (2016).
Back-End Consumer and User Disclosures
– Automated decisions based on third-party data may
require the organization using the third-party data to provide the
consumer with an “adverse action” notice (for example, if
under the Fair Credit Reporting Act 15 U.S.C. § 1681
(Rev. Sept. 2018), such decisions deny an applicant an
apartment or charge them a higher rent) (2020).
– General “you don’t
meet our criteria” disclosures are not sufficient. The FTC
expects end users to know what specific data are
used in the AI model and how the data are used by
the AI model to make a decision (2020).
– Organizations that change specific terms of deals based
on automated systems must disclose the changes and reasoning to
– Organizations should provide consumers with an
opportunity to amend or supplement information used to make
decisions about them (2020) and allow consumers to correct errors
or inaccuracies in their personal information (2016).
– When deploying models, organizations must confirm that
the AI models have been validated to ensure they work as intended
and do not illegally discriminate (2020).
– Organizations must carefully evaluate and select an
appropriate AI accountability mechanism, transparency framework
and/or independent standard, and implement as applicable
– An organization should determine the fairness of an AI
model by examining whether the particular model causes, or is
likely to cause, substantial harm to consumers that is not
reasonably avoidable and not outweighed by countervailing benefits
– Organizations must test AI models periodically to
revalidate that they function as intended (2020) and to ensure a
lack of discriminatory effects (2021).
– Organizations must account for compliance, ethics,
fairness and equality when using AI models, taking into account
four key questions (2016; 2020):
– How representative is the
data set?– Does the AI model account for biases?– How accurate are the AI predictions?– Does the reliance on the data set raise ethical or fairness
– Organizations must embrace transparency and
independence, which can be achieved in part through the following
– Using independent,
third-party audit processes and auditors, which are immune to the
intent of the AI model.– Ensuring data sets and AI source code are open to external
inspection.– Applying appropriate recognized AI transparency frameworks,
accountability mechanisms and independent standards.– Publishing the results of third-party AI audits.
– Organizations remain accountable throughout the AI data
life cycle under the FTC’s recommendations for AI transparency
and independence (2021).
The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.