Now more than ever, technology is influencing the way we do business, communicate, move through and around the world, access and manage our homes, receive and administer healthcare, make purchases, secure information, and protect our most valuable property. Morrison Foerster keeps pace with this rapidly evolving landscape across our internationally recognized corporate, finance, intellectual property, and litigation practices. MoFo Tech brings together our insights relating to fields such as blockchain and distributed ledger, cloud and as-a-service technologies, driverless cars, drones, fintech, computer hardware and software, the Internet of Things, semiconductors, and telecommunications. The MoFo Tech blog offers real-time, in-depth analyses of the trends and complex issues shaping the global technology industry.<h2>ABOUT MORRISON FOERSTER</h2>Morrison Foerster is a leading global law firm that transforms complexity into advantage for its clients. Our clients include some of the largest financial institutions, banks, consulting and accounting firms, and Fortune 100, technology, and life sciences companies. Highlighting the firm’s commitment to client service, leadership in market-changing deals and impact litigation, and values-based culture, Morrison Foerster has been repeatedly recognized as one of the top 10 firms on The American Lawyer’s A-List. Year after year, the firm receives significant recognition from Chambers and The Legal 500 across their various guides, including Global, USA, Asia‑Pacific, Europe, UK, Latin America, and FinTech Legal. Our lawyers passionately care about delivering legal excellence while living our values. Morrison Foerster has a long-standing commitment to creating a culture that respects and celebrates differences, while providing an inclusive environment. The firm has achieved Mansfield Certification Plus since 2018 as a result of successfully reaching at least 30 percent women, communities of color, and LGBTQ+ lawyer representation in a notable number of current leadership roles and committees. The firm also has a long history of commitment to the community and society through providing pro bono legal services, including litigating for civil rights and civil liberties, improving public education and fostering the wellbeing of children, advocating for veterans, promoting international human rights, enforcing the right to asylum, and safeguarding the environment.

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Scott Llewellyn’s practice focuses on complex litigation in federal and state courts around the country, with an emphasis on patent and trade secret disputes. Over the past decade, he has worked on some of the most significant, high–profile IP cases involving technology for televisions, smartphones, and self–driving cars, relying on 40 years of experience with computer hardware and software. Scott also has significant experience with business divorce cases before courts and arbitrators, involving a mix of trade secret, patent, tort, and contract claims relating to technology development and implementation.Scott also has successfully tried general commercial arbitration and agency matters, briefed and argued various appeals in federal appellate court, and tried more than 20 cases for the Denver City Attorney’s office, most of them jury trials.Scott also has an active pro bono practice, having worked on a variety of criminal and immigration appeals, and having represented Colorado criminal defense and mental health organizations in high–profile suits against the state of Colorado for violations of the constitutional rights of defendants.At MoFo, Scott serves on the board of the Morrison & Foerster Foundation. In the broader legal community, he serves on the boards of the Colorado Lawyers Committee and Drop in the Bucket (a charity that builds wells and sanitation systems in sub–Saharan Africa).After law school, Scott clerked for the Honorable Clarence A. Brimmer, District of Wyoming, and the Honorable Diarmuid F. O’Scannlain, Ninth Circuit. He was also previously an adjunct professor at the Georgetown University Law Center.

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Scott Llewellyn’s practice involves complex litigation before state and federal courts around the country, with an emphasis on patent and trade secret disputes for high-tech companies, to which he brings more than 35 years of experience with computer hardware and software, in particular relating to the processing and transmission of audio, video, voice, and other types of data. Mr. Llewellyn has also tried commercial arbitration and agency matters; briefed and argued various appeals in federal appellate court; and tried more than 20 cases for the Denver city attorney’s office, most of them jury trials.

Scott F. Llewellyn

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MoFo Tech brings together legal insights and in-depth analyses on trends and complex issues shaping the global technology industry, covering fields such as blockchain and distributed ledger, cloud and SaaS technologies, driverless cars, drones, FinTech, computer hardware and software, the IoT, semiconductors, and telecommunications.

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AI Transparency in Healthcare: Navigating a Changing Regulatory Landscape

Recentive: Federal Circuit’s Patent-Eligibility Guideposts for Machine-Learning Inventions

Japan’s Approach to AI Regulation in 2025

Federal Circuit Clarifies Requirements for Prior Art Under Pre-AIA 35 U.S.C. § 102(e)

AI Agent as a Service in Medtech - Key Considerations for Commercial Contracts

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New York City (NYC) is the first U.S. jurisdiction to pass an expansive law regulating the use of artificial intelligence (AI) in hiring. Local Law 144 of 2021 (LL144) prohibits employers and employment agencies from using an automated employment decision tool (AEDT) to make employment decisions for job candidates in New York City, unless:<ul><li>the tool has been subject to a “bias audit” within one year of using the tool;</li><li>information about the bias audit is made publicly available; and</li><li>certain notices are provided to employees or job candidates. </li></ul>LL144 is on many stakeholders’ radars, as the law carries some potentially hefty civil penalties, including up to $500 for the first violation and each additional violation occurring on the same day, and a penalty between $500 and $1,500 for each subsequent violation. Each day the AEDT is used in violation of the law constitutes a separate violation.Due to the outstanding open questions and practical concerns regarding the law’s requirements, the New York City Department of Consumer and Worker Protection (DCWP) postponed enforcing the law until July 2023.<a href="https://www.hr.com/en/magazines/all_articles/new-york-city-ai-law-trials-tribulations-and-uncer_lh7b9put.html" target="_blank">Read</a> our piece on HR.com.

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Few lawyers in the world have Miriam Wugmeister’s breadth and understanding of privacy and data security laws, obligations, and practices. 

Miriam H. Wugmeister | Privacy and Data Security Lawyer

Few lawyers in the world have Miriam Wugmeister's breadth and understanding of privacy and data security laws, obligations, and practices. In the words of her clients, she is “extremely practical and phenomenally smart. Just about one of the best privacy advisers there is” (Chambers USA). Co-chair of Morrison & Foerster’s market-leading Global Privacy and Data Security Group and ranked among the top in the profession by all major directories, Ms. Wugmeister is regularly called upon by some of the world’s largest and most complex multinational organizations to confront their most difficult U.S. and international privacy challenges. “Tremendous at helping you come up with practical solutions to real problems” (Chambers USA), she develops cutting-edge solutions for clients that marry legal compliance with business realities.

Miriam Wugmeister

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Michael Schulman is a partner in the firm’s Global Employment and Labor Group and co-head of the firm’s Washington, D.C. Litigation Department.

Michael Schulman

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New York City AI Law: Trials, Tribulations, And Uncertainties

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New York City (NYC) is the first U.S. jurisdiction to pass an expansive law regulating the use of artificial intelligence (AI) in hiring. Local Law 144 of 2021 (LL144) prohibits employers and employment agencies from using an automated employment decision tool (AEDT) to make employment decisions for job candidates in New York City, unless:

New York City AI Law: Trials, Tribulations, And Uncertainties

On April 3, 2023, FDA released the draft guidance titled “<a href="https://www.fda.gov/media/166704/download" target="_blank">Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions</a>” (hereinafter “draft guidance”) proposing an approach to support iterative improvement through modifications to a machine learning device software function (ML-DSF) while continuing to provide reasonable assurance of device safety and effectiveness. FDA is accepting <a href="https://www.regulations.gov/docket/FDA-2022-D-2628" target="_blank">comments</a> on the draft guidance until July 3, 2023.Background:Historically, changes to an approved or cleared medical device that could significantly affect the safety or effectiveness of the device or that constitute a major change or modification in the intended use of the device required submission of a new 510(k). This framework is at odds with the use of artificial intelligence and machine learning in medical devices, which enable and encourage continuous improvements and modifications to the device based on information gathered during use. FDA has been engaged in the process of developing a regulatory approach tailored to AI/ML-enabled devices that allows for safe and rapid modifications in response to new data while ensuring safety and effectiveness.The idea of using a “predetermined change control plan” (PCCP) was first mentioned by FDA in its 2019 discussion paper entitled “<a href="https://www.fda.gov/media/122535/download" target="_blank">Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)</a>” and has been widely discussed in the industry. In FDA’s <a href="https://www.fda.gov/media/145022/download" target="_blank">Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan</a> published in January 2021, FDA committed to publishing PCCP guidance. In addition, <a href="https://www.congress.gov/117/bills/hr2617/BILLS-117hr2617enr.pdf" target="_blank">Section 3308 of the Consolidated Appropriations Act</a>, also referred to as the Food and Drug Omnibus Reform Act (FDORA), added section 515C to the Food, Drug and Cosmetic Act, granting FDA authority to approve a PCCP “submitted in an application . . . that describes planned changes that may be made to the device . . . if the device remains safe and effective without any change.” In addition to FDA’s discussion of AI/ML-enabled devices, the White House released a <a href="https://www.whitehouse.gov/ostp/ai-bill-of-rights/" target="_blank">Blueprint for an AI Bill of Rights</a> on October 4, 2022, which discusses principles regarding the design, use, and deployment of automated systems.The draft guidance is applicable to ML-DSFs that the manufacturer “intends to modify over time” including those “for which modifications to the ML model are implemented automatically . . . as well as for ML-DSFs for which modifications to the ML model are implemented manually.”[1] The term “PCCP,” as used in the guidance, “refers to a plan that includes device modifications that would otherwise require a premarket approval supplement, De Novo submission, or a new premarket notification.”[2] Proactively pre-specifying and seeking market authorization for intended modifications to an ML-DSF in a PCCP removes the need to submit additional marketing submissions for each future modification that is delineated and implemented in accordance with the PCCP. Modifications to an ML-DSF that “could significantly affect, or that could affect, the safety or effectiveness of the device”[3] require premarket authorization, unless those modifications are covered by a PCCP. In addition, the modifications cannot change the intended use of the device or the benefit-risk profile.Elements of a PCCP:A PCCP is to be composed of three elements: (i) a Description of Modifications; (ii) a Modification Protocol; and (iii) an Impact Assessment.1. Description of ModificationsA PCCP must contain a detailed description of the specific, planned device modifications. The description of modifications includes three sub-elements: (i) an enumerated list of individual proposed device modifications; (ii) a specific rationale for the change to each part of the ML-DSF; and (iii) reference to the labeling changes associated with each modification. FDA recommends that a PCCP include specific modifications that can be verified and validated. These modifications should be “presented at a level of detail that permits understanding of the specific modifications that will be made to the ML-DSF.”[4] The Description of Modifications should clearly indicate whether the planned modifications will be implemented automatically or manually. FDA recognizes this is an evolving area and is proposing to consider PCCPs for ML-DSFs where modifications are implemented automatically to the extent FDA can “properly review them for substantial equivalence to the predicate or a reasonable assurance of safety and effectiveness.”[5] FDA reiterates that this subset of ML-DSFs is complex and will consider the benefit-risk assessment when reviewing PCCPs with automatically implemented modifications.Not all modifications may be appropriate for inclusion within a PCCP. Types of modifications that are acceptable for inclusion in a PCCP include:<ol><li>Modifications related to quantitative measures of ML-DSF performance specifications (e.g., improvements to analytical performance resulting from re-training the ML model using new data within the intended use population from the same type and range of input signal);</li><li>Modifications related to device inputs to the ML-DSF (e.g., expanding the algorithm to include new sources of the same type of signal); and</li><li>Limited modifications related to the device’s use and performance (e.g., for use within a specific subpopulation).</li></ol>FDA expects that modifications included in a PCCP should maintain the device within both the device’s intended use and indications for use.2. Modification ProtocolThe Modification Protocol describes the methodology used to develop, validate, and implement those modifications in a way that ensures the continued safety and effectiveness of the devices across relevant patient populations. FDA states that the draft guidance is consistent with the principles in the <a href="https://www.whitehouse.gov/ostp/ai-bill-of-rights/">Blueprint for an AI Bill of Rights</a>. The Modification Protocol should have four primary content elements, each with their own requirements:1. Data Management Practices – The Modification Protocol should include an outline of how data to support the proposed modifications will be collected, annotated, stored, retained, controlled, and used. The Modification Protocol should also clarify the relationship between the Modification Protocol data and data that is used to train and test both the initial and following versions of the ML-DSF. This is called the “data sequestration strategy” by FDA to ensure that training data sets and testing data sets are separated. Training data and testing data are sequestered to prevent overfitting and misquotes of test performance.The Modification Protocol should also include a description of the control methods used to prevent data or performance information leaking into the modification development process. In essence, the Modification Protocol should provide information that describes the protocols put in place to prevent access to performance testing data during the training and tuning process.Examples of the types of information to be included in a Modification Protocol describing data management practices are provided in Appendix A of the draft guidance.2. Re-training Practices – The Modification Protocol should include information about re-training practices, including the objective of re-training, a description of the ML model, the device components that may be modified, and triggers for re-training.3. Performance Evaluation – The Modification Protocol should discuss how the performance of the ML-DSF will be evaluated, including the triggers for evaluation, performance metrics to be computed, the statistical analysis plans that will be used to test performance for each modification, and confirmation that failures will be recorded and screened from implementation.4. Update Procedures – The Modification Protocol must include a description of how the devices will be updated to implement the modifications. This includes confirmation that either the verification and validation plans have not changed or that any changes are justified. Differences in performance and testing methods will be communicated to users. The PCCP should include a description of any labeling changes that will result, and the available labeling must include adequate directions for use and reflect information about the currently available versions of the ML-DSF, including information on site-specific modifications. Labeling should not reflect information on modifications that have not yet been implemented in the current version, as it could cause confusion and would be deemed misbranded. Examples of the types of information that should be submitted describing update procedures are also provided in Appendix A of the draft guidance.The PCCP should also clearly delineate which parts of the Modification Protocol are applicable to each modification. For a PCCP with multiple modifications, this may be achieved through a traceability table. The draft guidance includes a sample traceability table that provides an example of how a manufacturer can depict the traceability between the Description of Modifications and the Modification Protocol.3. Impact AssessmentThe draft guidance refers to the Impact Assessment as an assessment of the benefits and risks of the planned modifications and risk mitigations. There are five elements of an Impact Assessment:<ol><li>Comparison of the modified device to the unmodified version of the device;</li><li>Benefits-risk assessment for each individual modification;</li><li>Continued assurance of the safety and effectiveness of the device;</li><li>Impact of one modification on the implementation of another; and</li><li>Collective impact of implementing all modifications.</li></ol>Modifications to an authorized PCCP will generally constitute changes to the ML-DSF that require a new marketing submission for the device, including a modified PCCP. This is because modifications described in the PCCP include device changes that would typically require a PMA supplement, De Novo submission, or 510(k) premarket notification. FDA expects that the modified PCCP will need to be reviewed as part of the premarket review of the modified device because the modification will generally significantly affect the safety or effectiveness of the device. Where modifications to the PCCP are the only significant modifications since the prior device and PCCP authorization, FDA intends to focus review on the proposed PCCP.The draft guidance includes a decision tree[6] for determining whether a modification is within the scope of an authorized PCCP or if a new submission is required:<div data-reactroot=""><img src="https://media2.mofo.com/v3/images/blt5775cc69c999c255/blt4f5734e3eb37046b/6449a8834205ff10fc2438e4/230426-fda-ai_image.png?format=auto&amp;quality=60" alt="FDA "/></div>Referencing a PCCP in a Submission:FDA considers the PCCP to be part of the technological characteristics of the device. For devices subject to 510(k) requirements, the determination of substantial equivalence where the predicate device was authorized with a PCCP will be determined based on a comparison to the version of the predicate device cleared or approved prior to changes made under the PCCP.Example Scenarios of ML-DSFs That Employ PCCPs:Appendix B to the draft guidance provides examples that “illustrate different ML-DSF scenarios where a PCCP could be employed.”[7] For example, a particular ML-DSF analyzes images of skin lesions by identifying and characterizing features to aid in diagnosis. This ML-DSF was validated with a specific camera and is intended for use by a primary healthcare provider. The device was authorized with a PCCP. The pre-specified modification was for extension for use on additional general-purpose computing platforms, like smartphones and tablets. The general-purpose computing platform must include a 2D-camera that meets the minimum specifications in the PCCP, and the updated device must achieve a minimum performance defined in the Modification Protocol. In a scenario where the manufacturer implements a modification that was already specified in the PCCP and in conformance with the PCCP, the modification would not require a new marketing submission. On the other hand, if the manufacturer wanted to deploy a modified ML model that uses images captured by a thermographic camera, which was not specified in the PCCP, then a new marketing submission would be required because the modification could significantly affect the safety or effectiveness of the device and was not included in the PCCP.Appendix B also provides examples for patient monitoring software, ventilator settings software, an image acquisition assistance device, and feeding tube placement radiograph analysis software, with various hypothetical modification scenarios for each.Takeaways:This draft guidance is a significant step forward in FDA’s ability to effectively regulate AI/ML-enabled medical devices. The PCCP approach has the potential to save manufacturers from continuous submissions and to reduce the uncertainty around what support is required before making changes to devices with AI/ML components. However, as the draft guidance details, there are extensive requirements that manufacturers will need to satisfy in order to utilize the PCCP process. Depending on feedback from stakeholders, the final version of the guidance may be different from the current draft version.Device manufacturers developing ML-DSFs or incorporating them into their medical devices should consider submitting comments to the draft guidance. FDA recommends that the public submit comments to the draft guidance (<a href="https://www.regulations.gov/document/FDA-2022-D-2628-0002" target="_blank">Docket No. FDA-2022-D-2628</a>) within 90 days of publication in the Federal Register, by July 3, 2023. FDA hosted a public webinar for medical device manufacturers and other stakeholders to discuss the draft guidance on April 13, 2023. Webinar materials, including the presentation slides and transcript, are available <a href="https://www.fda.gov/training-and-continuing-education/cdrh-learn" target="_blank">here</a>.Morrison Foerster continues to closely monitor developments regarding AI/ML and FDA’s regulation of medical devices. For questions or assistance, please contact the authors.[1] Draft guidance at 5.[2] Id. at 6.[3] Id. (citations omitted).[4] Id. at 16.[5] Id.[6] Id. at 15.[7] Id. at 8.

Brigid Bondoc

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Regulatory Analyst

Brandy Guarda is a regulatory analyst supporting the FDA + Healthcare Regulatory and Compliance practice in Morrison Foerster’s Washington, D.C. office. Brandy predominantly focuses on researching and tracking FDA and interagency regulatory guidance, proposed and final rules, legislation, and enforcement actions.

Brandy A. Guarda

Brandy Guarda

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FDA Publishes Draft Guidance for Predetermined Change Control Plan for AI/ML-Enabled Device Software Functions

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On April 3, 2023, FDA released the draft guidance titled “Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions” (hereinafter “draft guidance”) proposing an approach to support iterative improvement through modifications to a machine learning device software function (ML-DSF) while continuing to provide reasonable assurance of device safety and effectiveness. FDA is accepting comments on the draft guidance until July 3, 2023.

FDA Publishes Draft Guidance for Predetermined Change Control Plan for AI/ML-Enabled Device Software Functions

Stephanie Sharron, Annalisa Cooper, and Wolfgang Schoenig, with assistance from Robert Grohmann, authored an article for Law360 explaining the top ten considerations when licensing AI for drug discovery.The ten considerations are:<ol><li>Consider the nature of the services being provided.</li><li>Be mindful of IP ownership.</li><li>Consider quality and privacy issues for training data.</li><li>Consider exclusivity.</li><li>Account for infringement risk.</li><li>Be reasonable with royalties.</li><li>Evaluate confidentiality and competitive use.</li><li>Plan for uncertainty.</li><li>Scrutinize contractual risk allocation.</li><li>Beware of an evolving regulatory landscape.</li></ol>Read the <a href="https://media2.mofo.com/v3/assets/blt5775cc69c999c255/bltc9412c34b10121da/64499b26d57e320df0d8163a/230425-considerations-licensing-ai-drug-discovery.pdf" target="_blank">full article</a>.

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Stephanie assists companies with technology and life sciences transactions, representing both private and public companies, from emerging growth through the Fortune 50. In particular, Stephanie focuses on helping companies that leverage data through technology in structuring their business and commercial transactions.

Stephanie Sharron

Stephanie Sharron is partner in the Palo Alto office and a member of the firm’s Technology Transactions Group. Clients turn to Ms. Sharron for advice on how to leverage data through technology and the related issues that apply to their business models in light of the rapid changes in business, technology and law.

Stephanie Lynn Sharron

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Wolfgang advises clients on contentious and non-contentious matters involving their most valuable intellectual property assets in the digital, high-tech, life sciences, and pharmaceutical industries.

Wolfgang Schönig

Wolfgang Schönig is a partner of Morrison Foerster (International) LLP based in our German office in Berlin. His practice specialty is advising on IP aspects relative to transactions in, among others, the life sciences, AgTech and pharmaceuticals industry, as well as legal representation in IP disputes. Wolfgang is specialized in research and development collaborations, complex licensing agreements, technology acquisitions, know-how protection strategies, and employee invention law.

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Counsel

Robert advises companies from the technology and life sciences sectors on a wide range of commercial, intellectual property (IP) and regulatory matters. His clients range from multinational corporations to startups and scaleups.

Robert Grohmann

Robert Grohmann is counsel at Morrison Foerster’s German office in Berlin. As a member of the technology transactions group, he focuses on advising domestic and foreign companies and startups in all areas of technology and IP law, including patent and software licensing, employee inventions law, as well as know-how protection. Moreover, Mr. Grohmann’s expertise encompasses the judicial and extra-judicial representation in matters of trademark law, design law, and competition law.

10 Considerations When Licensing AI For Drug Discovery

Stephanie Sharron, Annalisa Cooper, and Wolfgang Schoenig, with assistance from Robert Grohmann, authored an article for Law360 explaining the top ten considerations when licensing AI for drug discovery.

10 Considerations When Licensing AI For Drug Discovery

Over the past year, we have seen a dramatic increase in the adoption of AI technologies across industries. Because transactions involving AI technologies can resemble those involving traditional software, like SaaS agreements, parties often assume that their expectations from those standard agreements about what is reasonable and “market” should also apply to AI-related technology transactions. And, in some cases, this approach is appropriate. When a vendor supplies hospitals with a hosted medical imaging platform, the fundamental nature of the transaction remains much the same, regardless of whether the platform includes AI-assisted diagnosis functionality. However, it is important not to overlook the unique issues raised by certain AI technology transactions, particularly those that involve generative AI models (“Generative Models”). <a href="https://www.mofo.com/resources/insights/230425-key-issues-in-generative-ai-transactions" target="_blank">This client alert</a> offers an overview of those issues.

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Aaron is the chair of the firm’s Technology Transactions Group and co-chair of the Interactive + Digital Media Group. He advises clients on a wide range of complex transactions involving intellectual property and technology, including structuring and negotiating strategic licensing, development, collaboration, procurement, and distribution deals.

Aaron Rubin

Aaron is the chair of our Technology Transactions Group. He advises clients on a wide range of complex transactions involving intellectual property and technology, including structuring and negotiating strategic licensing, development, collaboration, procurement, and distribution deals. Aaron’s practice focuses on advising both established and emerging companies in a variety of data– and technology–intensive sectors, including software, SaaS, cloud computing, AR/VR, AI, gaming, healthcare, consumer electronics, social media, e–commerce, other online business models, and mobile applications. He also maintains an active practice counseling companies on the intellectual property aspects of mergers, acquisitions, asset spin–offs, and private equity investments.

Aaron P. Rubin

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Key Issues in Generative AI Transactions

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Over the past year, we have seen a dramatic increase in the adoption of AI technologies across industries. Because transactions involving AI technologies can resemble those involving traditional software, like SaaS agreements, parties often assume that their expectations from those standard agreements about what is reasonable and “market” should also apply to AI-related technology transactions.

Key Issues in Generative AI Transactions

Suz Mac Cormac, Stephanie Sharron, and Oluwabamise Onabanjo authored an article for the Daily Journal explaining how Responsible AI (artificial intelligence) practices in combination with Environmental, Social, and Governance (ESG) frameworks can be applied both to mitigate risk and support a corporation's ESG goals."AI holds tremendous promise for corporate growth and opportunity," the authors wrote. "However, use of AI can also adversely impact trust, reputation, and overall corporate operations and performance. By implementing Responsible AI practices, corporate performance as measured against ESG criteria can be bolstered. However, if corporations design, develop, and deploy AI solutions without adequate governance and risk mitigation through Responsible AI practices, ESG efforts may very well be undermined. Those corporations that establish corporate policies and a holistic approach to integrating Responsible AI and ESG frameworks will be best positioned to take advantage of the exciting potential of AI."Read the <a href="https://media2.mofo.com/v3/assets/blt5775cc69c999c255/blt23d301e583cdf5d4/64401376ea4ed2292f8d3f79/230411-responsible-ai-company-esg-framework.pdf" target="_blank">full article</a>.

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Susan (Suz) Mac Cormac, San Francisco partner, has extensive experience in corporate law, ESG, social enterprise and impact investing, and venture capital.

Susan H. Mac Cormac

Susan H. Mac Cormac | Corporate + ESG Attorney

Susan (“Suz”) co-chairs the ESG + Sustainability and Social Enterprise + Impact Investing practices. Her work focuses on advising innovative sustainable companies and investors, with particular expertise on ensuring investments have positive impact through use of new corporate forms, hybrids, alternative debt and equity instruments and disclosure.

Susan Mac Cormac

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Responsible AI as Part of a Company's ESG Framework

AI Trends For 2022 - Export Control/CFIUS

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Suz Mac Cormac, Stephanie Sharron, and Oluwabamise Onabanjo authored an article for the Daily Journal explaining how Responsible AI (artificial intelligence) practices in combination with Environmental, Social, and Governance (ESG) frameworks can be applied both to mitigate risk and support a corporation's ESG goals.

Responsible AI as Part of a Company's ESG Framework

<div data-reactroot=""><img src="https://media2.mofo.com/v3/images/blt5775cc69c999c255/blta12a2ea9d108acd3/6424af96ba298210fbb5ff43/230329-disruption-logo.jpg?format=auto&amp;quality=60" alt="Disruption Logo"/></div>On March 24, thought leaders in the technology and entertainment industries gathered in Los Angeles and virtually for Creative Commons’ Disruption Conference. The daylong event, which was sponsored by Morrison Foerster, focused on how the rights of content creators are being transformed as generative AI goes mainstream.Throughout the day, attendees heard from a variety of industry leaders, including Morrison Foerster partner Joseph Gratz, who spoke on copyright issues, and associate Heather Whitney, who participated in a fireside chat with science fiction and fantasy author Ken Liu. One highlight of the event was a keynote speech from Che Chang, OpenAI’s deputy general counsel, who shared his thoughts on the potential economic and societal impacts of generative AI tools. Attendees discussed the fundamental dilemmas of how content creators perceive their relationships with AI and how machine-assisted content can augment creativity.The following are a couple key takeaways from the discussions:<ul><li>Artists’ views on the effects of generative AI tools run the gamut. While there is often a focus on artists’ fear of these tools, many artists (like musician Imogen Heap) use them to enhance their creativity.</li><li>The major copyright questions revolve around three clusters of issues: (1) the use of data to train machine learning models, (2) the generation of outputs that may resemble training data, and (3) the copyrightability of outputs produced by the users of generative AI tools.</li></ul>The Disruption Conference was a valuable opportunity for members of the technology and entertainment industries to come together to learn from and connect with leaders in the field. With the rapid advances of generative AI, our attorneys will continue to monitor AI trends, engage with industry leaders, and actively seek out opportunities for learning and collaboration.

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Justin Haan is a partner in the firm’s Technology Transactions Group in the San Francisco office, advising leading technology industry pioneers to help them navigate their most critical IP and technology needs.

Justin H. Haan

Justin Haan is a partner in the firm’s Technology Transactions Group and advises clients on a range of issues in the technology and media industries. He also drafts technology license agreements on behalf of licensors and licensees involving a wide range of technologies, including virtual reality devices, cloud platforms, Internet of Things devices, unmanned aerial vehicles, and artificial intelligence applications. Mr. Haan received his J.D., with a certificate in law and technology, from the University of California, Berkeley.