In this month's edition of our Privacy & Cybersecurity Update, we take a look at guidance on artificial intelligence released by the U.K. Information Commissioner's Office and the Turing Institute, as well as guidance released in the U.S. by the National Association of Insurance Commissioners. We also examine the FTC's settlement with an email management company over misleading statements regarding access and use of consumers' emails.
ICO and Turing Institute Publish Draft Guidance on Explaining Artificial Intelligence Decisions
In response to a directive from the U.K. government, on December 2, 2019, the U.K. Information Commissioner's Office (ICO) and The Alan Turing Institute (Turing Institute) published a three-part draft guidance for consultation explaining decisions made with artificial intelligence (AI). The consultation period is open until January 24, 2020. The guidance makes clear that it is not a binding statutory code of practice, but rather a practical instruction that sets out good practices and can aid in compliance.
Part 1: The Basics of Explaining AI
Part 1 includes an analysis of the benefits and risks to organizations of explaining AI-assisted decisions to those that are affected by these decisions. Key benefits noted include: (1) legal compliance (for example, with the General Data Protection Regulation's (GDPR) information requirements for solely automated decision making); (2) the building of trust with an organization's use of AI; and (3) improved internal governance on the basis that having to explain AI-assisted decisions means that organizations will have a better understanding of what these systems do, which may in turn lead to better outcomes. Key risks of explaining AI-assisted decisions include: (1) commercial sensitivities, such as source code or algorithmic trade secrets that an organization would not want to share; (2) inappropriate disclosure of personal data as a result of how AI is trained; and (3) gaming of an organization's AI model if too much about the reasoning underlying the model is shared.
This part of the guidance also sets out the six explanations that the ICO and Turing Institute believe can be used to clarify AI-assisted decisions: (1) a "rationale explanation" provides the reasoning behind a decision; (2) a "responsibility explanation" concerns who is involved in the development and management of the AI model and who can be contacted for a human review of the decision; (3) a "data explanation" gives information on what data has been used with regard to a particular decision, which may involve looking at the data used to train and test the AI model itself; (4) a "fairness explanation" describes the steps taken to ensure that AI-assisted decisions are unbiased and fair; (5) a "safety and performance explanation" helps people to understand the steps taken to maximize the accuracy, reliability, security and robustness of an AI system's decisions and behaviors; and (6) an "impact explanation" considers the impact an AI system in a decision-making process has or may have on an individual and on general society. The guidance suggests that these can be combined into one explanation depending on the decision at hand. For example, if an AI-assisted decision produces a cancer diagnosis, the guidance suggests that the rationale, responsibility, impact, and safety and performance explanation types should be prioritized.
The basics of explaining AI also includes four guiding principles to provide a "broad steer" to assist in explaining AI-assisted decisions to a wide range of individuals given that AI-assisted decisions are not unique to a single sector or type of organization. These principles aid in helping organizations to: (1) be transparent about the use of AI for decision-making; (2) be accountable for AI-assisted decision systems if challenged internally or by external bodies, such as regulators or individuals; (3) consider the context in which the AI-assisted decisions are made; and (4) reflect on the impact of the organization's use of AI on the individuals affected and on wider society. The guidance makes clear that these principles are complementary to the principles outlined under Article 5 of the GDPR.
Part 2: Explaining AI in Practice
Part 2 of the guidance sets out a seven-step process to help guide organizations explain their AI-assisted decisions.
First, organizations should select which of the six explanation types established in Part 1 should be prioritized while keeping in mind the context in which the AI-assisted decision is to be made. The guidance notes that rationale and responsibility explanations will likely always be priority explanations given the need to know what an AI system is doing and who is responsible for it in order to be able to provide an accurate explanation.
Second, organizations should collect the information for each explanation type that they have identified as relevant. For instance, with regard to the responsibility explanation, information on both those responsible within an organization as a whole for the use of the AI, as well as those actually involved in an individual's case, will need to be recorded.
Third, organizations may want to ensure that the underlying logic of the AI system being used can be understood. Of particular concern are the use of "black box" AI systems, which include systems whose workings are unclear to human understanding. The guidance states that maximally interpretable AI systems ought to be used. However, where this is not possible and "black box" models are implemented, the potential impacts and risks should be "thoroughly considered" in advance and supplemental interpretability tools should be put in place.
Fourth, the AI system's results and the underlying logic behind them should be converted to language that is easy to understand. This could, for example, involve explaining what is meant by a probability that an AI system has produced.
Fifth, human decision-makers involved in providing an AI-assisted decisions should receive appropriate training that should provide a basic knowledge of machine learning and its limitations.
Sixth, the organization should consider the context in which the explanation is being delivered. The ICO and the Turing Institute, identified five contextual factors that affect why people may want explanations of AI-assisted decisions: (1) the setting in which the decision is being made (e.g., criminal justice or health care) (domain factor); (2) the impact that the decision can have on an individual and wider society (impact factor); (3) the data used to train and test the AI model and the input data at the point of the decision, as this may impact an individual's willingness to accept or contest an AI-assisted decision (data factor); (4) the importance of receiving or acting upon the outcome of a decision within a short timeframe (urgency factor); and (5) who the recipient of the explanation will be (e.g., what is their level of expertise) (audience factor).
Seventh, organizations should consider how to present their explanation. The guidance notes in particular that explanations may be layered so that priority explanations are given first (e.g., in person), while other explanations could be provided at a later point or in a different form (e.g., a leaflet).
Annex 1 of Part 2 provides an example of AI being used to aid in the explanation of a cancer diagnosis while going through the above seven steps.
Part 3: What Explaining AI Means for Organizations
Part 3 of the guidance considers the roles, policies, procedures and documentation that should be put in place to ensure that an organization is set up to provide explanations to affected individuals. Regarding organizational roles, the guidance notes that anyone involved in the decision-making pipeline has a role to play in explaining an AI-assisted decision. The policies should set out what the rules are, why they are in place and to whom they apply, while the procedures should provide directions on how to implement the rules. Finally, a documentation system should be in place to demonstrate how an AI system can be explained. This should cover both the design and implementation of the AI system, as well as the actual explanation of the AI-assisted decision that is given to an individual.
The ICO has made clear that the guidance1 is intended to be applicable in the real world and hopes that the consultation period will adhere to that goal. The final version of the guidance is expected to be published in 2020.
Email Management Company Settles FTC Allegations Over Alleged Misleading Statements
Pursuant to a settlement reached with the Federal Trade Commission (FTC), email management company Unrollme is enjoined, for the next 20 years, from making any misleading statements regarding how it accesses and shares users' emails. The FTC had alleged that the company misled consumers who had initially declined to grant access to their emails during the signup process for the company's services.
Unrollme, Inc. is a company that offers two services to help consumers organize and manage daily subscription emails. First, it helps users unsubscribe from unwanted subscription emails. Second, to declutter users' inboxes, it consolidates all the wanted subscription emails into a single, daily email called the "Rollup." To provide these services, Unrollme requests full access to users' email accounts.
Upon obtaining full access to users' email inboxes, Unrollme allows its parent company Slice Technologies, Inc., a market research company, to generate anonymized market research from users' e-receipts (i.e., purchase or order confirmation emails). Slice uses an automated crawler to capture the entire body of e-receipts — which results in the capture of personally identifiable or sensitive information, such as the user's name, billing and shipping addresses, credit card information, and the identity of purchased products or services that reveal, for example, a user's medical condition. Slice then uses a "parser" to extract data from the e-receipts and create a database of anonymous purchase information, which it sells as part of its market research analytics products. Slice stores the data until the user deletes his or her Unrollme account.
When consumers declined to grant Unrollme permission to access their email accounts, a message would appear encouraging them to change their minds and continue with the signup process. The messages changed over time. From approximately January to November 2015, Unrollme's message stated:
“It looks like you clicked No thanks. In order to use Unrollme, you need to tell [your email service provider] to allow us to monitor your emails. Don’t worry, we won’t touch your personal stuff.”
From approximately November 2015 through October 2016, Unrollme's message stated:
“Authorization Declined. In order to use Unrollme, you need to authorize us to access your emails. Don’t worry, this is just to watch for those pesky newsletters, we’ll never touch your personal stuff.”
And from October 2016 through September 2018, Unrollme's message stated:
“Oops! Looks like you declined access. Unrollme requires access to your inbox so we can scan for subscriptions and allow you to begin clearing out your inbox."
More than 55,000 users completed the sign-up process after viewing one of these messages.
The FTC’s Allegations
The FTC’s complaint alleged that Unrollme violated Section 5(a) of the Federal Trade Commission Act, 15 U.S.C. § 45(a) by making material, false and deceptive statements designed to encourage users to grant access to their email accounts and continue the sign-up process for Unrollme's services.2
The first count alleged that the messages in effect from approximately January 2015 through October 2016 were false or misleading because Unrollme granted Slice "access to its users’ inboxes, including personal emails in the form of e-receipts, which is then used to collect and sell purchase information contained therein to third parties."
The second count alleged that the message in effect from approximately October 2016 through September 2018 failed to disclose (or adequately disclose) that Unrollme also granted Slice access to its users’ inboxes and that "fact would be material to consumers in their decision to use Unrollme's services."
Under the FTC’s consent order, Unrollme is prohibited, for the next 20 years, from misrepresenting the "extent to which it accesses, collects, uses, stores, or shares" users' emails and personally identifiable information in regard to any product, service or software requiring access to users' emails. Unrollme also must take affirmative steps in regard to current users who enrolled in the company's services after viewing the allegedly misleading messages. First, Unrollme must send those users an email notice, prescribed in the consent order itself, informing users that the company or its parent "access or collect email purchase receipts for use in market research products that are sold to third parties." Second, Unrollme must delete all stored e-receipts and all personally identifiable information obtained from those receipts. The consent order also imposes reporting and compliance obligations, which include keeping consumer complaints and screenshots of the user enrollment interface. The consent order carries the force of law and each violation may result in a civil penalty of up to $42,530.3
National Association of Insurance Commissioners Releases Draft Principles for the Use of AI in Insurance
In early December 2019, the National Association of Insurance Commissioners (NAIC) released draft principles on artificial intelligence, which are intended to guide insurance companies and persons or entities facilitating the business of insurance on the “responsible stewardship of trustworthy AI” in the insurance industry. The high-level principles are organized under five headings that form the acronym “FACTS," which state that the use of AI should be Fair and Ethical; Accountable; Compliant; Transparent; and Secure, Safe and Robust.
The NAIC Draft Principles on Artificial Intelligence4 state that all insurance companies and employees that use or design AI systems (AI actors) should adhere to the following principles:
Fair and Ethical
“AI actors should respect the rule of law” and “should proactively engage in responsible stewardship of trustworthy AI in pursuit of beneficial outcomes for consumers and society,” while respecting “cultural, social and legal norms where they operate.”
AI actors “should be accountable for the proper functioning of AI systems” that they design and use, even when those AI systems create unintended outcomes. AI actors should “implement mechanisms and safeguards” to ensure that AI systems are subject to “ongoing human monitoring” and “human intervention.” Stakeholders should have access to “plain, easy-to-understand” information about how AI systems use their insurance data, as well as ways to “seek recourse of AI-driven decisions.”
AI actors are required to have “specific knowledge of all applicable federal and state insurance laws and regulations,” and ensure that AI systems are compliant. AI systems must be subject to “consistent monitoring” for legal compliance, particularly to guard against outcomes that are “unfairly discriminatory” or violate “cultural, social and legal standards.”
To increase public confidence, AI actors must be transparent about their use of AI and make “proactive disclosures to stakeholders” about the data being used by AI systems, the purpose of that use and the potential consequences. Transparency should be achieved “while maintaining the ability to protect confidentiality of proprietary algorithms and adherence to individual state regulations in all states where AI is deployed.”
Secure, Safe and Robust
AI systems should remain “robust, secure and safe” so that they can “function accurately and appropriately” during “normal use and reasonably foreseeable use or misuse.” AI systems should be designed to “ensure traceability in relation to datasets, processes and decisions made” by the system and to enable analysis of the system’s actions and responses. AI actors should employ a “systematic risk management approach” to AI systems to properly address risks related to privacy, security and bias.
It is anticipated that the FACTS AI principles should be finalized and adopted by the NAIC in 2020, after which the association likely will follow with more specific guidance or model law. In light of the continued increase in use of AI in the insurance industry, including in areas such as customer analysis, claims management, operations and fraud prevention, the NAIC’s work may meaningfully impact consumer protection and privacy, marketplace dynamics and the state-based insurance regulatory framework in this ever-evolving arena.
This memorandum is provided by Skadden, Arps, Slate, Meagher & Flom LLP and its affiliates for educational and informational purposes only and is not intended and should not be construed as legal advice. This memorandum is considered advertising under applicable state laws.