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Graphic depicting the three phases of analysis under NIST's AI RMF: map, measure, manage.

Creating trustworthy AI and reducing liability with NIST’s AI Risk Management Framework

AI & Algorithms, United States
On January 26, 2023, the National Institute of Standards and Technology (NIST) released the first version of the Artificial Intelligence Risk Management Framework (AI RMF).[1] The AI RMF is a voluntary resource meant to help organizations “manage the many risks of AI and promote trustworthy and responsible development and use of AI systems.”[2] To support the goal of the AI RMF, NIST supplemented its release with a companion NIST AI RMF Playbook,[3] AI RMF Explainer Video,[4] an AI RMF Roadmap,[5] AI RMF Crosswalk,[6] and statements from organization and individuals interested in the success of the AI RMF.[7] Together, these resources provide organizations with a comprehensive toolbox for identifying and managing AI risks. Given the growing regulatory interest in scrutinizing AI, these resources — although voluntary to use — provide important insights into what regulators may or may not want to see in AI products, services, and systems.

Background

The US Federal Government has long recognized the need for AI regulation. In 2016, the National Science and Technology Council produced a report stating that “the approach to regulation of AI-enabled products to protect public safety should be informed by assessment of the aspects of risk.”[8] In 2018, President Donald Trump signed a law establishing the National Security Commission on Artificial Intelligence to consider how to defend against AI threats and promote AI innovation.[9] In 2019, following Executive Order 13859,[10] the White House’s Office of Science and Technology Policy released guidance detailing the ten principles that Federal agencies should consider when determining how to regulate AI.[11] In response, NIST released a position paper, which called for US agencies to create globally relevant, non-discriminatory AI standards. Recognizing that AI has the potential to transform every sector of the US economy and society, Congress passed the National AI Initiative Act of 2020, which established the National Artificial Intelligence Initiative (NAIA), and directed NIST to “develop voluntary standards for artificial intelligence systems.”[12] On July 29, 2021, NIST issued a Request for Information to Help Develop an AI Risk Management Framework,[13] in which NIST asked individuals, groups, and organizations to submit comment on the goals of the AI RMF and on how those goals should be achieved. On October 15, 2021, NIST published a summary analysis of those comments,[14] and on December 13, 2021, the agency published a concept paper incorporating input from the initial Request for Information.[15] NIST released a draft AI RMF on March 17, 2022,[16] but, based on comments received during a NIST workshop held that same month,[17] the agency released a modified second draft on August 18, 2022,[18] and held another workshop in October 2022.[19] Four months later, NIST released the first version of the AI RMF.

Seven characteristics of trustworthy AI

As a flexible framework designed to adapt to a wide range of systems, products, and organizations, the AI RMF does not prescribe specific technical requirements that must be satisfied before an AI is considered trustworthy. Instead, the AI RMF provides a list of characteristics that must be balanced “based on the AI system’s context of use.”[20] These characteristics are:
  1. Valid and reliable
    1. Valid: Confirmation, though the provision of objective evidence, that the requirements for the AI’s specific intended use or application have been fulfilled. (ISO 9000:2015.)
    2. Reliable: The ability of AI system to perform as required, without failure, for a given time interval, under given conditions, including the entire lifetime of the system. (ISO/IEC TS 5723:2022.)
    3. Accurate: The closeness of the AI system’s results of observations, computations, or estimates to the true values or the values accepted as being true. (ISO/IEC TS 5723:2022.)
    4. Robust / Generalized: The ability of an AI system to maintain its level of performance under a variety of circumstances, which includes performing in ways that minimize potential harm to people if it is operating in an unexpecting setting. (ISO/IEC TS 5723:2022.)
  2. Safe
    1. AI systems should not, under defined conditions, lead to a state in which human life, health, property, or the environment is endangered. (ISO/IEC TS 5723:2022.)
  3. Secure and resilient
    1. Secure: AI systems should maintain confidentiality, integrity, and availability through protection mechanisms that prevent unauthorized access and use. (NIST Cybersecurity Framework and Risk Management Framework.)
    2. Resilient: AI systems, as well as the ecosystems in which they are deployed, should withstand unexpected adverse events or unexpected changes in their environment or use — or if they can maintain their functions and structure in the face of internal and external change and degrade safely and gracefully when this is necessary. (ISO/IEC TS 5723:2022.)
  4. Accountable and transparent
    1. Transparent: Information about an AI system and its outputs should be available to individuals interacting with such a system, regardless of whether they are even aware that they are doing so, and be tailored to the role or knowledge of AI actors or individuals interacting with or using the AI system.
    2. Accountable: AI systems should incorporate actionable redressability related to AI system outputs that are incorrect or otherwise lead to negative impacts.
  5. Explainable and interpretable
    1. Explainable: The AI system should describe how the AI system functions, with descriptions tailored to individual differences such as the user’s role, knowledge, and skill level.
    2. Interpretable: The AI system should communicate a description of why an AI system made a particular prediction or recommendation. (“Four Principles of Explainable Artificial Intelligence” and “Psychological Foundations of Explainability and Interpretability in Artificial Intelligence.”[21])
  6. Privacy-enhanced
    1. Privacy values such as anonymity, confidentiality, and control should guide choices for AI system design, development, and deployment, but privacy-enhancing technologies (PETs) may be needed to support privacy-enhanced AI design.
  7. Fair — with harmful bias managed
    1. Fair: AI systems should incorporate equality and equity by addressing issues such as harmful bias and discrimination, which includes taking into consideration cultural context and demographic differences.
    2. With harmful bias managed: AI systems should consider and manage three major categories of AI bias:
      1. Systemic: Bias found in the AI datasets, the organizational norms, practices, and processes across the AI lifecycle, and the broader society that uses the AI system.
      2. Computational and statistical: Bias found in AI datasets and algorithmic processes, and often stems from systematic errors due to non-representative samples.
      3. Human-cognitive: Bias relating to how an individual or group perceives AI system information to make a decision or fill in missing information, or how humans think about purposes and functions of an AI system.

Practical benefit of complying with the AI RMF

As a voluntary framework, the AI RMF does not mandate compliance with its principles; however, as the NIST Cybersecurity Framework demonstrates, voluntary compliance may help shield an organization from legal risks. The NIST Cybersecurity Framework offers a risk-based approach to cybersecurity and a methodology for developing a comprehensive information security program. Like the AI RMF, the Cybersecurity Framework is voluntary, and therefore does not form the basis for any regulatory action. Yet, if a cybersecurity incident occurs, an organization that has implemented the Cybersecurity Framework can use their adherence in their favor. For example, if a regulator alleges the organization was negligent in its cybersecurity practices, the organization can rebut the allegations by demonstrating that its program was designed in accordance with the Cybersecurity Framework and therefore was reasonably designed to counter foreseeable risks. Compliance with the AI RMF may produce similar benefits, given that NIST created the AI RMF for AI industry stakeholders to “cultivate trust in the design, development, use, and evaluation of AI technologies and systems in ways that enhance economic security and improve qualify of life.”[22]
[1] https://www.nist.gov/itl/ai-risk-management-framework [2] https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf [3] https://pages.nist.gov/AIRMF/ [4] https://www.nist.gov/video/introduction-nist-ai-risk-management-framework-ai-rmf-10-explainer-video [5] https://www.nist.gov/itl/ai-risk-management-framework/roadmap-nist-artificial-intelligence-risk-management-framework-ai [6] https://www.nist.gov/itl/ai-risk-management-framework/crosswalks-nist-artificial-intelligence-risk-management-framework [7] https://www.nist.gov/itl/ai-risk-management-framework/perspectives-about-nist-artificial-intelligence-risk-management [8] https://obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf [9] https://www.govinfo.gov/content/pkg/COMPS-15483/uslm/COMPS-15483.xml; https://www.nscai.gov/ [10] https://trumpwhitehouse.archives.gov/presidential-actions/executive-order-maintaining-american-leadership-artificial-intelligence/ [11] https://www.whitehouse.gov/wp-content/uploads/2020/01/Draft-OMB-Memo-on-Regulation-of-AI-1-7-19.pdf [12] https://www.congress.gov/bill/116th-congress/house-bill/6216 [13] https://www.federalregister.gov/documents/2021/07/29/2021-16176/artificial-intelligence-risk-management-framework [14] https://www.nist.gov/system/files/documents/2021/10/15/AI%20RMF_RFI%20Summary%20Report.pdf [15] https://www.nist.gov/system/files/documents/2021/12/14/AI%20RMF%20Concept%20Paper_13Dec2021_posted.pdf [16] https://www.nist.gov/system/files/documents/2022/03/17/AI-RMF-1stdraft.pdf [17] https://www.nist.gov/news-events/events/2022/03/building-nist-ai-risk-management-framework-workshop-2 [18] https://www.nist.gov/system/files/documents/2022/08/18/AI_RMF_2nd_draft.pdf [19] https://www.nist.gov/news-events/events/2022/10/building-nist-ai-risk-management-framework-workshop-3 [20] https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf [21] https://www.nist.gov/artificial-intelligence/ai-fundamental-research-explainability [22] https://www.nist.gov/itl/ai-risk-management-framework/ai-risk-management-framework-faqs
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Picture of the word "AI" surrounded by stars.

What the EU’s Artificial Intelligence Act will mean for the global AI industry

AI & Algorithms, EEA & UK, GDPR
On April 21, 2021, the European Commission proposed the Artificial Intelligence Act (AIA), a regulatory and legal framework for artificial intelligence systems.[1] On December 5, 2022, the Council of the European Union adopted its general approach to the AIA, which incorporated changes to the regulation.[2][3] Germany announced support for the AIA, but “sees some need for improvements.”[4] In similar fashion, the Federal Trade Commission (FTC) published an article on April 19, 2021, calling for the integration of truth, fairness, and equity into the use of AI.[5] Later that year, the FTC announced its consideration to initiate rulemaking to, in part, ensure that algorithmic decision-making does not result in unlawful discrimination.[6] Given this growing international interest in regulating AI systems, it is important to note that the AIA was drafted to have an extraterritorial effect much like the EU’s General Data Protection Regulation (GDPR). The GDPR became a global model for data protection laws across the world, including the California Consumer Privacy Act (CCPA), and the AIA could similarly establish a worldwide standard for AI regulation – especially if the FTC is considering initiating rulemaking for algorithmic systems. So, while the draft AIA will likely see more changes, the proposed regulation appears sufficiently settled for analysis of its requirements and potential global effects. This article provides an overview of the major legal takeaways from the AIA, including which AI systems are outright prohibited and which are less regulated. Background As early as 2017, the European Council called for a “sense of urgency to address emerging trends,” including “issues such as artificial intelligence . . ., while at the same time ensuring a high level of data protection, digital rights and ethical standards.”[7] On July 16, 2019, Ursula von der Leyen, then-candidate for President of the European Commission, announced her political guidelines for the 2019-2024 Commission, in which she called for legislation for a coordinated EU approach on the human and ethical implications of AI. [8] Following this announcement, the Commission published a white paper on AI, “A European approach to excellence and trust.”[9] This paper sets out policy options for how to achieve the goal of promoting AI adoption while also addressing the risks associated with certain uses of AI. The AIA draft proposed on April 21, 2021, delivers on now-President von der Leyen’s political commitments announced in 2019 and the white paper’s stated objectives. The result is a legal framework presenting a balanced, proportionate regulatory approach that seeks to address the risks and problems with AI, without unduly constraining or hindering AI development on the market. To whom does the AIA apply? Like the GDPR’s territorial scope in Article 3, the AIA’s scope in Article 2 covers providers that place AI systems on the EU market, or put them into service in the EU, irrespective of whether those providers are established within the EU. A “provider” is any natural or legal person, public authority, agency, or other body that develops an AI system or that has an AI system developed. An AI is “put into service” by a provider when the provider supplies an AI system for first use directly to a user or for the provider’s own use on the EU market. An AI is placed “on the market” by a provider when the AI is distributed or used on the EU market in the course of a commercial activity, whether in return for payment or free of charge. Taken together, this means that the AIA does not apply to private, non-professional use, but anyone supplying, using, or distributing, AI systems on the EU market to users or for their own purposes may fall within the regulation’s scope. Can the EU’s proposed AI regulation apply to AI creators and companies outside of the EU? Yes. The AIA applies to AI systems used by natural or legal persons, including public authorities, agencies, or other bodies, who are physically present or established within the Union. However, the regulation’s reach extends beyond the EU’s borders. The AIA covers natural or legal persons, including public authorities, agencies, or other bodies, who are physically present or established “in a third country, where the output produced by the system is used in the Union.” The AIA also applies to any natural or legal person that makes an AI system available on the EU market. This extraterritorial scope, like the GDPR’s, means companies outside of the EU should take care in considering how and where their AI systems are used. Does the EU’s proposed AI regulation provide data subjects with additional rights? No. The AIA does not provide additional rights to data subjects. Instead, as a piece of product regulation, the AIA takes aim at the AI systems themselves, by either prohibiting a particular AI system or requiring it to conform to a list of obligations. That said, the AIA recognizes the need for new AI technologies to be “developed and functioning according to Union values, fundamental rights, and principles.” This includes rights provided under the GDPR, such as an individual’s right to restrict processing (Article 18) and the right of deletion / erasure (Article 17). Furthermore, a controller using an AIA-covered AI system must satisfy their GDPR notice obligations to data subjects (Articles 12 – 14). Does the EU’s proposed AI regulation cover all algorithm-based systems? No. The AIA draft proposed on April 21, 2021, defined “AI system” so broadly that it seemed to encompass most software, which prompted EU Member States to propose a narrower definition.[10] The version adopted by the Council of the EU on December 5, 2022, recognizes the need to more narrowly define “AI system” to “provide sufficiently clear criteria for distinguishing AI from more classical software systems.” Thus, the current AIA draft defines “AI system” to target systems developed through machine learning and logic- and knowledge-based approaches. In addition, an AI system using one of these approaches must operate with elements of autonomy and, based on machine and/or human-provided data and inputs, infer how to achieve a given set of objectives. This definition is recognized by the Council as a “compromise” between those calling for a broader definition and those calling for a narrower one, and as such, it remains subject to change. Does the proposed AI regulation treat all covered AI systems equally? No. The regulation uses a risk-based approach to separate covered AI systems into four categories: Unacceptable Risk The AIA contains a limited list of particularly harmful AI systems found to contravene EU values. Because the risk of harm is unacceptably high, these AI systems are prohibited under the regulation. This list includes:
  1. An AI system that subliminally manipulates a person, thereby materially distorting the person’s behavior in a manner that causes or is reasonably likely to cause physical or psychological harm.
  2. An AI system that exploits the vulnerabilities of individuals due to age, disability, or socioeconomic status, resulting in physical or psychological harm.
  3. An AI system that analyzes individuals to create a social score, which leads to detrimental or unfavorable treatment unrelated to the contexts in which the data was originally generated or collected.
  4. Some uses of remote biometric identification for law enforcement purposes in publicly accessible spaces (e.g., facial recognition technology).
A full list of prohibited AI systems can be found in Article 5 of the AIA. High-Risk Most of the AIA’s legal obligations and burdens fall on AI systems deemed to be “high-risk” under the regulation. An AI system is considered “high-risk” under the AIA if the AI system is itself a product or is intended to be used as a safety component of a product, and the product is subject to an existing third-party conformity assessment (e.g., medical devices, machinery, engine-powered vehicles, certain stand-alone AI systems in employment, education, and immigration, etc.). A high-risk AI system can only be used in the EU or put on the EU market if the AI system complies with the AIA’s legal obligations. This includes:
  1. A risk management system (Article 9).
  2. Adherence of training, validation, and testing data to quality criteria (Article 10).
  3. Technical documentation describing how the AI system complies with applicable rules, including law enforcement purposes (Article 11).
  4. Record-keeping requirements to ensure traceability of the AI system’s functions (Article 12).
  5. Transparency requirements to enable users to understand the system’s output and use (Article 13).
  6. Providing adequate human oversight of the AI system’s operations (Article 14).
  7. Ensuring the AI system achieves appropriate levels of accuracy, robustness, and cybersecurity (Article 15).
The AIA provides further obligations on AI system developers, which include:
  1. Maintaining a quality management system (Article 17).
  2. Ensuring the system undergoes a conformity assessment procedure (Article 19).
  3. Maintaining automatically generated logs (Article 20).
  4. Taking corrective actions if the system is found not to conform with the AIA (Article 21).
  5. A duty to notify serious incidents or malfunctions to national competent authorities (Article 22).
Limited Risk Title IV of the AIA creates new transparency obligations for certain AI systems. For example, users of emotion recognition systems or biometric categorization systems must be informed of the operation of the system. In addition, users of an AI system that generates deep fake images or content must be informed that the content has been artificially generated or manipulated. Similar to the GDPR, these disclosures must be provided to the user in a clear and distinguishable manner no later than the user’s first interaction or exposure to the AI system. Minimal / No Risk If an AI system does not fall into one of the above categories, then it can be developed and used in the EU subject to existing regulation without any additional legal obligations under the AIA. That said, the Council encourages developers of AI systems in this category to “create codes of conduct intended to foster the voluntary application of the requirements applicable to high-risk AI systems, adapted in light of the intended purpose of the systems and the lower risk involved.”[11] Are the enforcement penalties harsher than the GDPR? Yes. Non-compliance with the AIA’s list of prohibited AI systems in Article 5 could be subject to an administrative fine of up to €30 million or, if the offender is a company, up to 6% of its worldwide annual turnover for the preceding financial year, whichever is higher. By contrast, serious GDPR violations can result in a fine of up to €20 million or 4% of a company’s worldwide annual revenue from the preceding financial year, whichever is higher. For less serious infringements under the AIA, the offender could be subject to administrative fines of up to €20 million or, if the offender is a company, up to 4% of its total worldwide annual turnover for the preceding financial year, whichever is higher. This too is higher than the GDPR’s fines for less severe infringements. What are the next steps for the AIA? The Parliament is scheduled to vote on the current draft of the AIA by the end of March 2023. After this, Member States, the Parliament, and the European Commission will begin discussions of the AIA in April 2023. This timeline could lead to an adoption of the AIA by the end of 2023.
[1] https://artificialintelligenceact.eu/wp-content/uploads/2022/05/AIA-COM-Proposal-21-April-21.pdf [2] https://www.consilium.europa.eu/en/press/press-releases/2022/12/06/artificial-intelligence-act-council-calls-for-promoting-safe-ai-that-respects-fundamental-rights/ [3] https://data.consilium.europa.eu/doc/document/ST-14954-2022-INIT/en/pdf [4] https://data.consilium.europa.eu/doc/document/ST-14954-2022-ADD-1/en/pdf [5] https://www.ftc.gov/business-guidance/blog/2021/04/aiming-truth-fairness-equity-your-companys-use-ai [6] https://www.reginfo.gov/public/do/eAgendaViewRule?pubId=202210&RIN=3084-AB69 [7] https://www.consilium.europa.eu/media/21620/19-euco-final-conclusions-en.pdf [8] https://op.europa.eu/en/publication-detail/-/publication/43a17056-ebf1-11e9-9c4e-01aa75ed71a1 [9] https://commission.europa.eu/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en [10] https://www.wired.com/story/artificial-intelligence-regulation-european-union [11] https://artificialintelligenceact.eu/wp-content/uploads/2022/05/AIA-COM-Proposal-21-April-21.pdf
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A minimalistic picture of a human brain being digitized into technological lines that also look like a human brain.

Guidance on Artificial Intelligence and Data Protection

AI & Algorithms, EEA & UK, Resource, United States
[Updated: March 7, 2024] For many of us, Artificial Intelligence (“AI”) represents innovation, opportunities, and potential value to society. For data protection professionals, however, AI also represents a range of risks involved in the use of technologies that shift processing of personal data to complex computer systems with often opaque processes and algorithms. Data protection and information security authorities as well as governmental agencies around the world have been issuing guidelines and practical frameworks to offer guidance in developing AI technologies that will meet the leading data protection standards. Below, we have compiled a list* of official guidance recently published by authorities around the globe. Canada
  • 1/17/2022 – Government of Ontario, “Beta principles for the ethical use of AI and data enhanced technologies in Ontario” https://www.ontario.ca/page/beta-principles-ethical-use-ai-and-data-enhanced-technologies-ontario The Government of Ontario released six beta principles for the ethical use of AI and data enhanced technologies in Ontario. In particular, the principles set out objectives to align the use of data enhanced technologies within the government processes, programs, and services with ethical considerations being prioritized.
China
  • 3/1/2023 – National Information Security Standardization Technical Committee, Technical Document on Basic Requirements for Security of Generative Artificial Intelligence https://www.tc260.org.cn/upload/2024-03-01/1709282398070082466.pdf (in Chinese) The Technical Document provides security requirements for the use of generative AI services. These requirements include conducting a security assessment before collecting data for a generative AI model, entering legally binding contracts with generative AI service providers, and acquiring consent for certain use cases of generative AI services.
  • 12/12/2022 – Cyberspace Administration of China, Regulations on the Administration of Deep Synthesis of Internet Information Services http://www.cac.gov.cn/2022-12/11/c_1672221949354811.htm (in Chinese) and http://www.cac.gov.cn/2022-12/11/c_1672221949570926.htm (in Chinese) The Regulations target deep synthesis technology, which are synthetic algorithms that produce text, audio, video, virtual scenes, and other network information. The accompanying Regulations FAQs state that providers of deep synthesis technology must provide safe and controllable safeguards and conform with data protection obligations.
  • 9/26/2021 – Ministry of Science and Technology (“MOST”), New Generation of Artificial Intelligence Ethics Code http://www.most.gov.cn/kjbgz/202109/t20210926_177063.html (in Chinese) The Code aims to integrate ethics and morals into the full life cycle of AI systems, promote fairness, justice, harmony, and safety, and avoid problems such as prejudice, discrimination, privacy, and information leakage. The Code provides for specific ethical requirements in AI technology design, maintenance, and design.
  • 1/5/2021 – National Information Security Standardisation Technical Committee of China (“TC260”), Cybersecurity practice guide on AI ethical security risk prevention https://www.tc260.org.cn/upload/2021-01-05/1609818449720076535.pdf (in Chinese) The guide highlights ethical risks associated with AI, and provides basic requirements for AI ethical security risk prevention.
Denmark:
  • 3/5/2024 – Datatilsynet, New regulatory sandbox for AI https://www.datatilsynet.dk/presse-og-nyheder/nyhedsarkiv/2024/mar/ny-regulatorisk-sandkasse-for-ai The Danish Data Protection Authority, in collaboration with the Danish Agency for Digitalisation, established a regulatory sandbox for AI, where companies and entities can access relevant expertise and GDPR guidance when they develop or use AI.
  • 2/29/2024 – Danish Ministry of Business and Industry, Recommendations on tech development and use of artificial intelligence https://em.dk/Media/638447961317309808/Tech-ekspertgruppens%20anbefalinger.pdf (in Danish) The Danish Government’s expert group announced recommendations on tech giants’ development and use of AI, which aims to explore the potential of AI while negating its potential harmful effects. The recommendations focus on regulation of unauthorized use of copyrighted material, imposing responsibility on tech giants for the credibility of information, and default standards for chatbots.
E.U.:
  • 2/21/2024 – European Commission, Creation of AI Office https://digital-strategy.ec.europa.eu/en/policies/ai-office The European Commission announced the creation of the European AI Office, which is established within the Commission and will play a key role in implementing the EU’s AI Act. The AI Office will work with public and private entities to promote cooperation and adoption of the EU’s AI Act.
  • 1/20/2022 – European Institute of Innovations & Technology (“EIT”), AI Maturity Tool https://ai.eitcommunity.eu/ai-maturity-tool/ The EIT published a web-based AI maturity tool which allows businesses to assess how prepared they are for the use of AI, and which will allow businesses to compare their maturity level to that of other organizations in the future.
  • European Telecommunication Standards Institute (“ETSI”) Industry Specification Group Securing Artificial Intelligence (“ISG SAI”) https://www.etsi.org/committee/1640-sai The ISG SAI has published standards to preserve and improve the security of AI. The works focus on using AI to enhance security, mitigating against attacks that leverage AI, and securing AI itself from attack.
  • 7/14/2021 – European Commission’s Joint Research Center (“JRC”), Report https://publications.jrc.ec.europa.eu/repository/handle/JRC125952 Most recently, the JRC published this report on the AI standardization landscape. The report describes the ongoing standardization efforts on AI and aims to contribute to the definition of a European standardization roadmap.
  • 4/21/2021 – European Commission, “Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts” https://ec.europa.eu/newsroom/dae/document.cfm?doc_id=75788 The EU Commission proposed a new AI Regulation – a set of flexible and proportionate rules that will address the specific risks posed by AI systems, intending to set the highest global standard. As an EU regulation, the rules would apply directly across all European Member States. The regulation proposal follows a risk-based approach and calls for the creation of a European enforcement agency.
France: Germany: Hong Kong:
  • 8/18/2021 – Office of the Privacy Commissioner for Personal Data (“PCPD”), “Guidance on the Ethical Development and Use of Artificial Intelligence” https://www.pcpd.org.hk/english/resources_centre/publications/files/guidance_ethical_e.pdf This guidance discusses ethical principles for AI development and management while also highlighting recent development in AI governance around the globe. The guidance further includes a helpful self-assessment checklist in its appendix concerning businesses’ AI strategy and governance, risk assessment and human oversight, development and management of AI systems as well as communication and engagement with stakeholders.
India:
  • 9/28/2021 – INDIAai, “Mitigating Bias in AI – A Handbook For Startups” https://indiaai.s3.ap-south-1.amazonaws.com/docs/AI+Handbook_27-09-2021.pdf INDIAai, a government-based initiative, published this formalized framework for startups. The handbook identifies different risk factors that may lead to bias in AI.
  • 7/15/2021 – Data Security Council of India (“DCSI”), “Handbook on Data Protection and Privacy for Developers of Artificial Intelligence in India” https://www.dsci.in/sites/default/files/documents/resource_centre/AI%20Handbook.pdf The handbook establishes guidelines for responsible and ethical AI development in line with the applicable legal data protection framework. While the handbook does not provide technical solution but instead focuses on the ethical and legal objectives to pursue when designing AI systems, it does provide for a checklist of questions and good practices which developers shall keep in mind while in the design process.
  • 2/24/2021 – National Institution for Transforming India (“NITI Aayog”), “Responsible AI” http://www.niti.gov.in/sites/default/files/2021-02/Responsible-AI-22022021.pdf In this paper, the Government think tank highlights the ethical and legal framework for AI technology management. The paper further includes a self-assessment guide for AI usage in its annex.
International:
  • 3/5/2024 – Organisation for Economic Co-operation and Development (“OECD”), Explanatory Memorandum on the Updated OECD Definition of an AI System https://www.oecd-ilibrary.org/docserver/623da898-en.pdf?expires=1709828184&id=id&accname=guest&checksum=B906C1E98329EC8C1E539374B37DF045 The OECD published a memorandum that revisits the definition of an artificial intelligence system contained within the 2019 OECD Recommendation on AI, by redefining and expanding the term. However, the memorandum recognizes that the new definition, even though it is broader, nonetheless may require additional criteria to tailor the definition to a specific use case or context.
  • 9/28/2023 – OECD, Catalogue of Tools & Metrics for Trustworthy AI https://oecd.ai/en/ The OECD published a catalogue of tools and metrics for building and deploying trustworthy AI systems. This catalogue provides users with a one-stop-shop for tools that can mitigate bias, measure performance, audit systems, and create procedural processes to oversee the system.
  • 8/1/2023 – Future of Privacy Forum (“FPF”), Generative AI for Organizational Use: Internal Policy Checklist https://fpf.org/wp-content/uploads/2023/07/Generative-AI-Checklist.pdf To help organizations initialize the process of regulating the use of generative AI, FPF released a checklist to help organizations revise policies and procedures governing generative AI. The checklist provides a non-exhaustive list of topics to consider when revising such policies and procedures.
  • 5/31/2023 – EU-US Terminology and Taxonomy for Artificial Intelligence https://digital-strategy.ec.europa.eu/en/library/eu-us-terminology-and-taxonomy-artificial-intelligence To align EU and US risk-based approaches to regulating AI, a group of experts created this document to provide a unified approach to AI terminologies and taxonomies. A total number of 65 terms were identified with reference to key documents from the EU and US.
  • International Organization for Standardization (“ISO”) – ISO/IEC 23894:2023 Information technology — Artificial intelligence — Guidance on risk management https://www.iso.org/standard/77304.html This document provides guidance on how organizations that develop, produce, deploy or use products, systems and services that utilize AI can manage risk specifically related to AI. The guidance also aims to assist organizations to integrate risk management into their AI-related activities and functions. It moreover describes processes for the effective implementation and integration of AI risk management.
  • ISO – ISO/IEC 38507:2022 https://www.iso.org/standard/56641.html Together with the International Electrotechnical Commission (“IEC”), ISO has published a number of AI standards in recent years. The newest standards published in April 2022, called “Governance implications of the use of artificial intelligence by organizations”, provides guidance for the governing body of organizations regarding the use and implications of AI.
  • ISO – ISO/IEC JTC 1/SC 42  Standards https://www.iso.org/committee/6794475/x/catalogue/p/1/u/0/w/0/d/0 These standards published in March of 2021 provide background about existing methods to assess the robustness of neural networks. Additional AI standards are currently under development.
  • 9/15/2022 – Information Technology Industry Council (“ITI”), Policy Principles for Enabling Transparency of AI Systems https://www.itic.org/documents/artificial-intelligence/ITIsPolicyPrinciplesforEnablingTransparencyofAISystems2022.pdf The ITI published guidance for policymakers, emphasizing the need for transparency as a critical part of developing accountable and trustworthy AI systems.
  • 2/22/2022 – Organization for Economic Co-operation and Development (‘OECD’), Framework for the Classification of AI Systems https://www.oecd-ilibrary.org/science-and-technology/oecd-framework-for-the-classification-of-ai-systems_cb6d9eca-en;jsessionid=lWU_vM8LQfX-wAZgVIjj31FS.ip-10-240-5-181 In the Framework, the OECD has developed a tool to evaluate AI systems from a policy perspective, by providing a baseline to characterize the application of an AI system deployed in specific contexts. The Framework contributed to the OECDS “AI in Work, Innovation, Productivity, and Skills” (“AI-WIPS”) program.
  • 1/26/2022 – Information Technology Industry Council (“ITI”), Recommendations on NIST AI Risk Management Framework https://www.itic.org/documents/artificial-intelligence/ITICommentsonAIRMFConceptPaperFINAL.pdf In response to the AI Risk Management Framework concept paper released by NIST, the ITI has published a series of recommendations in order to improve the framework and encourage NIST to align the framework with prior works as well as standards that are currently under development in international standards bodies.
  • 1/18/2022 – Information Technology Industry Council (“ITI”), Recommendations on AI-enabled Biometric Technologies https://www.itic.org/documents/artificial-intelligence/ITICommentsBiometricTechRFIFINAL.pdf ITI released a series of recommendations addressed to the U.S. Government regarding the use of AI and biometric technologies, elaborating on governance programs and practices that may be useful to consider in the context of biometric technologies, including with regard to performance auditing and post-deployment impact assessment.
Japan:
  • 4/8/2022 – Ministry of Economy, Trade, and Industry (“METI”), Artificial Intelligence Introduction Guidebook for Small and Medium Sized Companies https://www.meti.go.jp/policy/it_policy/jinzai/AIutilization.html (in Japanese) The Guidebook provides SMEs with guidance on how to prepare for and begin utilization of AI in their enterprises, providing practical steps for decision-making.
  • 2/15/2022 – Ministry of Internal Affairs and Communications (“MIC”), Guidebook on Cloud Services Using AI https://www.soumu.go.jp/main_content/000792669.pdf (in Japanese) The Guidebook summarizes the steps to keep in mind when developing and providing AI cloud services while gaining the trust of users and considering data collection requirements.
  • 1/28/2022 – METI, Governance Guidelines for Implementation of AI Principles https://www.meti.go.jp/shingikai/mono_info_service/ai_shakai_jisso/pdf/20220128_2.pdf The METI has released an updated version of its Guidelines for the Practice of Artificial Intelligence Principles, outlining AI governance rules which include risk analysis, systems design, implementation and evaluation, along with providing practical examples.
  • 8/4/2021 – MIC, AI Network Society Promotion Council Report https://www.soumu.go.jp/main_content/000761967.pdf (in Japanese) The report highlights recent trends in AI utilization as well as efforts to promote secure and reliable social implementation of AI.
Jordan
  • 8/5/2022 – Ministry of Digital Economy and Entrepreneurship, National Charter of Ethics for Artificial Intelligence https://tinyurl.com/w4e3acdy The charter provides an ethical baseline to regulate the development of AI technologies. The charter includes a set of principles that include accountability, transparency, impartiality, respect for privacy, promotion of human values, and other such principles that promote democratic values, human rights, and diversity.
Mexico
  • 6/1/2022 – National Institute for Access to Information and Protection of Personal Data (“INAI”), Recommendations for the Processing of Personal Data derived from the Use of Artificial Intelligence https://home.inai.org.mx/wp-content/documentos/DocumentosSectorPublico/RecomendacionesPDP-IA.pdf (in Spanish) The INAI released its recommendations concerning regulation of personal data and AI technology. In particular, the recommendations focus on such topics as AI and its implication in public security, AI in the education sector, AI and privacy by design, AI and cloud computing, and more.
Saudi Arabia:
  • 4/27/2022 – Saudi Food and Drug Authority (‘SFDA’), “Guidance on Review and Approval of AI and Big Data based Medical Devices” https://beta.sfda.gov.sa/sites/default/files/2021-04/SFDAArtificial%20IntelligenceEn.pdf The Guidance sets out the requirements for obtaining a Medical Devices Marketing Authorization for AI-based medical devices within the KSA. It applies to the standalone software type of medical devices, which diagnose, manage, or predict diseases by analyzing medical Big Data using AI, as well as to AI software that is configured with hardware.
Senegal: Singapore: South Korea: Spain: Sweden:
  • 2/28/2024 – Swedish Authority for Privacy Protection, Guidance on the GDPR and AI https://www.imy.se/verksamhet/dataskydd/innovationsportalen/vagledning-om-gdpr-och-ai/ (in Swedish) The guidance discusses artificial intelligence from two viewpoints: technical and legal. The technical portion includes explanations of AI, machine learning, and deep leaning, along with professional insights into AI training models. The legal portion focuses on how to determine when the GDPR applies to the development and use of AI.
Turkey: U.K.:
  • 2/26/2024 – Information Commissioner’s Office (“ICO”), Generative AI second call for evidence: Purpose limitation in the generative AI lifecycle https://ico.org.uk/about-the-ico/what-we-do/our-work-on-artificial-intelligence/generative-ai-second-call-for-evidence/ The ICO launched a consultation series on generative AI, which, in part, focuses on how the data protection principle of purpose limitation should be applied at different stages in the generative AI life cycle. The consultation highlights the importance for AI developers to sufficiently set out clear purposes for each stage of the AI and to explain what personal data is processed in each stage.
  • 6/7/2023 –  Department for Science, Innovation and Technology (“DSIT”), “Find out about artificial intelligence (AI) assurance techniques” https://www.gov.uk/ai-assurance-techniques Following up on the UK government’s AI Regulation White Paper (see next bullet), DSIT created a portfolio of use cases illustrating various AI assurance techniques being used in the real-world to support the development of trustworthy AI. The portfolio includes case studies from across multiple sectors and features a range of technical, procedural, and educational approaches to promote responsible AI.
  • 3/29/2023 –  DSIT, “A pro-innovation approach to AI regulation” https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper The DSIT published a white paper introducing an AI regulation framework underpinned by five principles: Safety, security, and robustness; appropriate transparency and explainability; fairness; accountability and governance; and contestability and redress. Rather than recommend specific AI legislation, the white paper recommends that existing regulators incorporate these principles into their enforcement efforts.
  • 3/15/2023 – ICO, Guidance on AI and data protection https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-on-ai-and-data-protection/ The ICO published guidance clarifying requirements for fairness in AI. The document includes guidance on solely automated decision-making and technical approaches to mitigating algorithmic bias.
  • 5/4/2022 – ICO, AI and Data Protection Risk Toolkit https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/guidance-on-ai-and-data-protection/ai-and-data-protection-risk-toolkit/ ICO recently launched its updated AI and Data Protection Risk Toolkit, which contains risk statements to help organizations using AI to correctly assess the risk of their processing practices. The toolkit provides suggestions and practical steps for technical and organizational measures used to mitigate risks and demonstrate compliance with applicable data protection laws. It further includes references to other core resources.
  • 1/12/2022 – Department for Digital, Culture, Media & Sports (“DCMS”) and Office for Artificial Intelligence (“OAI”), AI Standards Hub Pilot https://www.gov.uk/government/news/new-uk-initiative-to-shape-global-standards-for-artificial-intelligence The DCMS and OAI announced the pilot of a new AI Standards Hub as part of the UK’s National AI Strategy. In its pilot phase, the Hub will focus on creating tools and guidance for education, training, and professional development to help businesses engage with creating AI technical standards, and bringing the AI community together through workshops, events, and a new online platform to encourage more coordinated engagement in the development of standards around the world.
  • 9/22/2021 – UK Secretary of State for Digital, Culture, Media & Sport (“DCMS”), “National AI Strategy” https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1020402/National_AI_Strategy_-_PDF_version.pdf The UK Government announced its National AI Strategy, which aims to invest and plan for the long-term needs of the AI ecosystem, support the transition to an AI-enabled economy, and ensure the UK governs AI effectively.
  • 5/5/2020 – ICO, “Explaining Decisions Made with AI” https://ico.org.uk/for-organisations/guide-to-data-protection/key-data-protection-themes/explaining-decisions-made-with-ai/ This detailed guidance released by the ICO in cooperation with the lan Turing Institute gives businesses practical advice to explain the legal framework and effects of AI decision-making processes and the necessary considerations for compliance with existing data protection laws.
U.S.:
  • 10/31/2023 – National Institute of Standards and Technology (“NIST”), “Executive Order FAQs” https://www.nist.gov/artificial-intelligence/executive-order-safe-secure-and-trustworthy-artificial-intelligence/executive-order-faqs The Biden Administration’s EO on Safe, Secure, and Trustworthy Artificial Intelligence issued on October 30, 2023, charges multiple agencies – including NIST – with producing guidelines and taking other actions to advance the safe, secure, and trustworthy development and use of artificial intelligence. In response, NIST released a short series of FAQs addressing the agency’s role in developing guidelines under the EO.
  • 10/30/2023 – The White House, “Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence” https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ The Biden Administration issued an Executive Order that establishes new safety and security standards for the use of AI. This whole-of-government approach requires numerous agencies to develop standards for what constitutes “responsible” uses of artificial intelligence.
  • 10/30/2023 – The White House, “FACT SHEET: President Biden Issues Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence” https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial-intelligence/ This is the accompanying Fact Sheet for the Biden Administration’s Executive Order regarding the development of safe, secure, and trustworthy development and use of AI (immediately above).
  • 03/09/2023 – U.S. Chamber of Commerce, “CTEC AI Commission 2023″ https://www.uschamber.com/assets/documents/CTEC_AICommission2023_Report_v5.pdf The U.S. Chamber of Commerce published a report calling for the regulation of AI and outlining five key principles that stakeholders should consider when drafting a regulatory framework. In contrast to the White House Office of Science and Technology Policy’s Blueprint for an AI Bill of Rights, the Chamber’s report seeks to regulate AI without hindering economic development.
  • 1/26/2023 – NIST, “AI Risk Management Framework” https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf On January 26, 2023, the National Institute of Standards and Technology (NIST) released the first version of the Artificial Intelligence Risk Management Framework (AI RMF). The AI RMF is a voluntary resource meant to help organizations manage the many risks of AI and promote trustworthy and responsible development and use of AI systems. As a flexible framework designed to adapt to a wide range of systems, products, and organizations, the AI RMF provides a list of characteristics that must be balanced based on the AI system’s context of use.
  • 10/4/2022 – White House Office of Science and Technology Policy, “Blueprint for an AI Bill of Rights” https://www.whitehouse.gov/wp-content/uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf The White House Office of Science and Technology Policy published a non-binding white paper detailing a list of principles that, if incorporated into the development and use of AI technologies, should protect the American public during the age of artificial intelligence. The document calls upon policymakers to adopt these principles when considering how to regulate AI technologies.
  • 5/13/2022 – Department of Justice Civil Rights Division, “Algorithms, Artificial Intelligence, and Disability Discrimination in Hiring” https://beta.ada.gov/resources/ai-guidance/ The guidance explains how use of algorithms and AI in hiring can lead to disability discrimination and legal consequences. The guidance details how employers can avoid such disability discrimination when using AI technology.
  • 3/16/2022 – National Institute of Standards and Technology (“NIST”), “Towards a Standard for Identifying and Managing Bias in Artificial Intelligence” https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf In this Special Publication, NIST analyzes the challenges of AI bias, aiming to provide some detailed socio-technical guidance for identifying and managing AI bias.
  • 12/14/2021 – NIST, “AI Risk Management Framework Concept Paper” https://www.nist.gov/system/files/documents/2021/12/14/AI%20RMF%20Concept%20Paper_13Dec2021_posted.pdf NIST has developed for public review a concept paper for the Artificial Intelligence Risk Management Framework (“AI RMF”), intended for voluntary use and to address risks in the design, development, use, and evaluation of AI products, services, and systems. NIST stated that it intends to release the AI RMF 1.0 in early 2023.
  • 7/30/2021 – Department of Homeland Security (“DHS”), “Artificial Intelligence and Machine Learning Strategic Plan” https://www.dhs.gov/sites/default/files/publications/21_0730_st_ai_ml_strategic_plan_2021.pdf The strategic plan of DHS’ Science and Technology Directorate (“S&T”) outlines its goals that are committed to ensuring that AI/ML research, development, test, evaluation, and departmental applications comply with statutory and other legal requirements, and sustain privacy protections and civil rights and liberties for individuals. It further advises stakeholders on recent developments in AI/ML and the associated opportunities and risks.
  • 5/5/2021 – Electronic Privacy Information Center (“EPIC”), New National Artificial Intelligence Initiative Office Website. https://www.ai.gov/ The White House launched its new website, AI.gov, featuring policy priorities, reports, and news regarding AI.
  • 4/19/2021 – Federal Trade Commission (“FTC”), “Aiming for Truth, Fairness, and Equity in Your Company’s Use of AI” https://www.ftc.gov/news-events/blogs/business-blog/2021/04/aiming-truth-fairness-equity-your-companys-use-ai In this blog post, the FTC offers guidance for companies in their use of AI, specifically instructing them to show transparency and accountability when employing new algorithms.
  • 4/8/2020 – FTC, “Using Artificial Intelligence and Algorithms” https://www.ftc.gov/news-events/blogs/business-blog/2020/04/using-artificial-intelligence-algorithms In this blog post, the FTC outlines best practices when relying on algorithms and highlights key principles such as transparency, fairness, accuracy, and accountability.
  • 9/9/2019 – NIST, “U.S. Leadership in AI: A Plan for Federal Engagement in Developing Technical Standards and Related Tool” https://www.nist.gov/artificial-intelligence/ai-standards-federal-engagement Following an executive order directing federal agencies to develop international standards to promote and protect innovation and public confidence in AI technologies, NIST published this plan. The plan intends to provide guidance regarding priorities and appropriate levels of engagement in matters of AI standards.
*While extensive, this list is not meant to be exhaustive. We will do our best to update this list from time to time, and add new guidance as it becomes available.
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An image of the logo for LinkedIn, which is black text reading "Linked," followed by white text reading, "In," in a blue bow.

hiQ v. LinkedIn: User Agreements in the Age of Data Scraping

Contract, Data / Web Scraping, Social Media, United States
On November 4, 2022, LinkedIn announced a “significant win” for the platform and its members against “personal data scraping.” The win resulted from a 6-year legal battle that asked, in part, whether LinkedIn must allow hiQ Labs to scrape data from the public profiles of LinkedIn members. Last Friday, the U.S. District Court for the Northern District of California answered that question by ruling that LinkedIn’s User Agreement “unambiguously prohibits hiQ’s scraping and unauthorized use of the scraped data.” And as such, hiQ breached LinkedIn’s User Agreement “through its own scraping of LinkedIn’s site and using scraped data.”[1] An Overview of Data Scraping Data scraping is a technique by which a computer program extracts data from another program or source. The technique typically uses scraper bots, which send a request to a specific website and, when the site responds, the bots parse and extract specific data from the site in accordance with their creators’ wishes. Scraper bots can be built for a multitude of purposes, including:
  • Content scraping – pulling content from a site to replicate it elsewhere.
  • Price scraping – extracting prices from a competitor.
  • Contact scraping – compiling email, phone number, and other contact information.
In today’s economy, data is key, and data scraping is an efficient means of acquiring huge amounts of specific data. Yet, this court ruling signals that companies may need to be more cautious about how and where they use data scraping bots. hiQ’s Data Scraping Violates LinkedIn’s User Agreement Founded in 2012 as a “people analytics” company, hiQ Labs provides information to businesses about their workforces. To do this, hiQ extensively relied on using automated software to scrape data from LinkedIn’s public profiles. hiQ then aggregated, analyzed, and summarized that data to create two products, “Keeper” and “Skill Mapper,” which allowed businesses to improve their employee engagement and reduce costs associated with external talent acquisition. However, in 2017, LinkedIn sent a cease-and-desist letter threatening legal action against hiQ, arguing that LinkedIn’s User Agreement prohibits data scraping. Specifically, the User Agreement states: You agree that you will not:
  • Scrape or copy profiles and information of others through any means (including crawlers, browser plugins and add-ons, and any other technology or manual work);
. . .
  • Use manual or automated software, devices, scripts[,] robots, other means or processes to access, ‘scrape,’ ‘crawl’ or ‘spider’ the Services or any related data or information;
  • Use bots or other automated methods to access the Services, add or download contracts, send, or redirect messages.
Court records indicate that hiQ knew about this prohibition since 2015 yet continued scraping data from LinkedIn’s public profiles and even “attempted to reverse engineer LinkedIn’s systems . . . to avoid detection by simulating human site-access behaviors.” Based on these facts, LinkedIn sought a partial summary judgment finding hiQ liable for breach of contract. From hiQ Labs’ perspective, while the above User Agreement language may appear clear, language elsewhere in the User Agreement seemed to provide users and members with a right to scrape data from public profiles. Specifically, the User Agreement provides the following when delineating members’ rights and obligations: 2. Obligations . . . When you share information, others can see, copy and use that information. . . . 3.1 Your License to LinkedIn . . .

c. We will get your consent if we want to give others the right to publish your posts beyond the Service. However, other Members and/or Visitors may access and share your content and information, consistent with your settings and degree of connection with them.

hiQ argued that the User Agreement’s statements that “Visitors may access and share your content and information consistent with your settings” and that “[w]hen you share information, others can see, copy and use that information” are inconsistent with the prohibition of scraping data. And that, as a user and member of LinkedIn who agreed to the User Agreement, hiQ read this inconsistency to mean that hiQ had the right to scrape data from public profiles. Unfortunately for hiQ, this argument failed. The court concluded that informing users that their data may be copied and used does not contradict LinkedIn’s prohibition against scraping, crawling, or spidering. “The two concepts are not mutually exclusive – a warning to members that a third party may collect their public-facing data is not a blessing for third parties to do so through expressly prohibited means.” Thus, hiQ breached LinkedIn’s User Agreement, which “clear[ly]” prohibits data scraping, by scraping LinkedIn’s site and using that scraped data. LinkedIn May Lose Despite This Victory It is important to note that, although LinkedIn considered this a victory, the court only granted partial summary judgment in favor of LinkedIn on its breach of contract claim. hiQ raised numerous defenses to LinkedIn’s breach of contract claim, including waiver and estoppel, arguing that LinkedIn knew about hiQ’s data scraping as early as 2014 yet failed to act until the cease-and-desist letter in 2017. hiQ’s argument goes, in short, that because LinkedIn knew about hiQ’s data scraping but delayed in taking legal steps to prevent it, LinkedIn either waived its right to enforce the breach of contract claim or should be estopped because hiQ reasonably relied on LinkedIn’s acquiescence to the data scraping. The court concluded that there is at least a genuine dispute of material fact as to whether LinkedIn knew about hiQ’s data scraping as early as 2014, which – if sufficiently proven – could provide grounds for hiQ to raise the defenses of waiver and estoppel. These arguments remain unresolved, and it is not clear at this time whether hiQ and LinkedIn will continue battling in court – especially given that hiQ has gone dormant since 2019 – but we will continue monitoring for further developments. Further Privacy Concerns Lastly, this case brings to mind broader legal issues regarding publicly available personal information. Under the California Consumer Privacy Act of 2018 (CCPA), as amended by the California Privacy Rights Act of 2020 (CPRA), businesses must satisfy numerous obligations when processing personal information. However, the definition of “personal information” does not include “information made available by a person to whom the consumer has disclosed the information if the consumer has not restricted the information to a specific audience.” Similarly, under the EU’s General Data Protection Regulation (GDPR), the law’s prohibition against the processing of special data categories (e.g., race, ethnicity, religion, health, etc.) does not apply if the “processing relates to personal data which are manifestly made public by the data subject.” These exceptions are reminiscent of hiQ’s argument in this case: that LinkedIn’s User Agreement expressly said that “[v]isitors [of LinkedIn] may access and share your content and information consistent with your settings.” Meaning, the users themselves provided their information to LinkedIn and purposefully, via their settings choices, made their information available to the public. Putting aside that LinkedIn’s User Agreement prohibited data scraping, hiQ’s argument raises the question: was hiQ scraping publicly available personal information, as it is understood under the GDPR and CCPA / CPRA? And if so, does that mean that hiQ would not have to comply with some requirements imposed by applicable general data protection laws? The answer will likely depend on a fact-specific inquiry on the circumstances surrounding the user content, such as (i) which data protection law applies to the data subjects in question; (ii) whether privacy settings were readily apparent to users when they initially posted their profiles/content; and (iii) whether users took affirmative actions to publicly post their information. In the meantime, businesses should remain aware that scraping personal information, even publicly available information, requires proper planning and due diligence. Key Takeaways
  1. Data scraping remains a prevalent data collection practice, but individuals and companies may be liable for breach of contract claims stemming from data scraping practices in violation of a User Agreement.
  2. On the other hand, if a business wants to quash a company’s known data scraping practices that violate the User Agreement, waiting too long to take legal steps may result in the business forfeiting a breach of contract claim.
  3. Either way, this ruling indicates that companies must take User Agreements seriously, both their own (if they want to prevent data scraping) and those belonging to others (if they want to scrape data).
  4. Lastly, a question remains as to whether the data in this case was made publicly available, as the term is understood under US and EU data regulation laws.
[1] Note: The court also concluded that hiQ separately breached LinkedIn’s User Agreement by hiring independent contractors to create fake LinkedIn accounts to conduct “quality assurance” while logged into LinkedIn by “viewing and confirming hiQ customers’ employees’ identities manually.” LinkedIn’s User Agreement expressly prohibits creating false identities.
Logo for LinkedIn social media platform.

hiQ v. LinkedIn: User Agreements in the Age of Data Scraping

Privacy Law, Social Media , , , ,

Image by BedexpStock from Pixabay

On November 4, 2022, LinkedIn announced a “significant win” for the platform and its members against “personal data scraping.” The win resulted from a 6-year legal battle that asked, in part, whether LinkedIn must allow hiQ Labs to scrape data from the public profiles of LinkedIn members.

Last Friday, the U.S. District Court for the Northern District of California answered that question by ruling that LinkedIn’s User Agreement “unambiguously prohibits hiQ’s scraping and unauthorized use of the scraped data.” And as such, hiQ breached LinkedIn’s User Agreement “through its own scraping of LinkedIn’s site and using scraped data.”[1]

An Overview of Data Scraping

Data scraping is a technique by which a computer program extracts data from another program or source. The technique typically uses scraper bots, which send a request to a specific website and, when the site responds, the bots parse and extract specific data from the site in accordance with their creators’ wishes.

Scraper bots can be built for a multitude of purposes, including:

  • Content scraping – pulling content from a site to replicate it elsewhere.
  • Price scraping – extracting prices from a competitor.
  • Contact scraping – compiling email, phone number, and other contact information.

In today’s economy, data is key, and data scraping is an efficient means of acquiring huge amounts of specific data. Yet, this court ruling signals that companies may need to be more cautious about how and where they use data scraping bots.

hiQ’s Data Scraping Violates LinkedIn’s User Agreement

Founded in 2012 as a “people analytics” company, hiQ Labs provides information to businesses about their workforces. To do this, hiQ extensively relied on using automated software to scrape data from LinkedIn’s public profiles. hiQ then aggregated, analyzed, and summarized that data to create two products, “Keeper” and “Skill Mapper,” which allowed businesses to improve their employee engagement and reduce costs associated with external talent acquisition.

However, in 2017, LinkedIn sent a cease-and-desist letter threatening legal action against hiQ, arguing that LinkedIn’s User Agreement prohibits data scraping. Specifically, the User Agreement states:

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