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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Partner

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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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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Current State of the AI VC Financing Market

MoFo Tech Law Summit – Tech and Antitrust: Policy Shifts in the Second Trump Administration

MoFo Tech Law Summit – Responsible Tech: Policy Becomes Practice

AI Dynamics: Regulatory Landscape and Industry Insights

Introducing the MoFo Tech Law Summit – Innovation Under Regulation

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The machines are taking over.  Not always apparent, but Artificial Intelligence (AI) and machine learning (ML) are finding footholds in numerous industries.  One area in which AI is rapidly on the rise relates to employment practices and employee control.  But before embracing the future, employers should be well informed about the risks inherent with ceding control to robots. Black Box Hiring PracticesCompanies are only as good as their employees.  With the current strong job market, employers of all sizes struggle to identify, recruit, and hire talent.  As companies grow, many have turned to novel technologies to assist, guide, and (in some instances) take over the talent recruitment and selection process.  According to a December 2016 Harvard Business Review article, in some cases AI screening software systems used by businesses eliminate more than 70 percent of job applicants without any human interaction.[1]Companies tout the efficiencies offered by AI hiring algorithms.  They can save time and money, and (in theory) help identify the candidates best-suited for open positions.  But, what happens when an apparently-qualified job seeker is summarily rejected because a computer algorithm decided she was not a good fit?  The candidate may ask for more details—specifically what in her resume eliminated her from consideration?  Was it the school the candidate attended?  Maybe the computer rejected her because it thinks she had an unacceptable  gap between jobs based on the summer she spent in the Middle East for what appears (to the computer program) to be a religious pilgrimage.  Perhaps the candidate’s professed interest in baking set off silent alarms in the AI framework because it has seen too many data sets that confirm bakers are almost always poor people managers.  Or maybe the candidate submitted her application very late at night, and the AI has determined candidates emailing after business hours more than likely procrastinators and underperformers. Of course, some of these connections appear absurd.  Whether a company is reviewing 10 resumes for a position or 1,000, challenging decisions—not always cleanly tied to concrete data points—must be made when screening or eliminating potential job applicants. The benefit of AI and ML is they (for better or worse) make connections using data points humans never would consider.  This benefit can have an unsavory side.  Where it may save the company time and money in the hunt to identify top talent, unrefined AI may have hidden biases that create illegal hiring decisions without company personnel even realizing it.  Because “algorithms mimic human decision making,” they can “learn from past successes, which may embed existing bias.”[2]  Take the example of a company dominated by white male leaders that has successfully run for years, consistently posting profits with above-average employee satisfaction.  The AI is likely to be programmed with a goal of maintaining that level of success, and the company may inadvertently input data points that provide a bias towards preferring candidates with profiles that are largely white men.  Even if the company intends to eliminate bias from AI selections, the AI making the initial screening decisions is unlikely to worry about discriminatory outcomes.  Using AI and ML systems with such latent biases risks excluding applicants based on protected classes (like age, race, gender, or disability) and exposing the company to discrimination claims. Although such concerns may encourage employers to limit the use of AI and ML, companies can still benefit from the use of such technology if they implement appropriate data input, human oversight, and reasonable guardrails to minimize biases in these programs.  Companies should regularly observe data sets used by the AI system, with a particular focus on how the AI is incorporating, connecting, and utilizing the data provided.  An example of an appropriate guardrail is adding a coded instruction limiting the weight AI applies to certain factors likely to result in biased decisions (like gender, country of origin, and religious affiliation).[3]  Employers should also consider avoid limiting or prohibiting AI from analyzing names or other data points about an applicant that may expose the AI to learning a candidate’s protected characteristics (e.g., ethnicity).  Continual monitoring of AI and ML systems will also increase the likelihood of them remaining free of unlawful biases.Big DataIn a similar vein, most major companies are using large datasets known as “Big Data” to better manage and track inventory, automatically identify and service equipment failures and security breaches, create safer work environments, and improve employee efficiency.  Big Data can also be replete with hidden liabilities for the unaware.  If a company, for example, is harvesting biometric information for employees, such as fingerprints, handprints, or optical scans, for security access to facilities or for time-tracking systems, the company could face increased litigation risks. For example, Illinois state courts have recently experienced a significant increase in privacy class actions under the Illinois Biometric Information Privacy Act (BIPA), which requires employers to provide written notice and obtain consent from employees and customers prior to collecting or storing biometric data.  Although many states do not currently have the same protections as Illinois, other states like Texas and Washington have proposed laws governing employer use of biometric information. The data some employers are capturing for seemingly legitimate reasons could run afoul of various privacy protections.  For example, a company may desire to improve safety for workers by embedding connected sensors in employee uniforms to detect toxins or chemicals in the workplace or monitor employee vitals when performing physically demanding tasks.  If those same sensors inadvertently monitor employee movement patterns or identify legally protected medical conditions, absent an employer having a legitimate justification for collecting that data and appropriate notice to the employees, it could expose the employer to liability in some jurisdictions.  Likewise, the employer could use data collected on employee movements in the workplace to inadvertently make unlawful employment decision.  For example, if an employee takes frequent breaks during the workday, the company may discipline the employee, believing the employee is violating company policy or failing to meet performance standards.  But, in reality, the employee may be taking increased breaks due to a protected medical condition that causes him to use the restroom frequently.   While using Big Data in the workplace can make good business sense, employers should keep in mind the following considerations when relying on Big Data.<ul><li>Know what data is being collected and why it is being collected.  Indiscriminately collecting any and all data available is not advised.  Companies should be mindful of what types of data they are collecting, and why they are collecting it.  Generally, when deciding what information to collect, document the strong business case for collecting that data to show how its impact on employee privacy interests will be limited.  Excessive data collection is harder to defend.  Working with experienced in-house or external professionals and attorneys to review and audit data collection can help identify potential liabilities and put appropriate safeguards in place to limit exposure. </li><li>Give proper notice to employees.  Even in jurisdictions where notice of data collection is not required, companies should be transparent with their employees regarding data collection that might infringe on employee privacy.  Such transparency can reduce the possibility of litigation and related bad press.</li><li>Be thoughtful about how long the data is stored.  Data storage is continually getting cheaper, but that does not mean companies have free rein to store gargantuan amounts of information on employees.  The longer companies store data, the more potential for legal risk.  If maintaining data for long periods is needed for a legitimate business reason, companies should consider aggregating the data in a manner that removes personalized employee information.</li></ul>[1] “Hiring Algorithms Are Not Neutral,” Harvard Business Review, G. Mann and C. O’Neil, December 9, 2016, available at <a href="https://hbr.org/2016/12/hiring-algorithms-are-not-neutral">https://hbr.org/2016/12/hiring-algorithms-are-not-neutral</a>.[2] Id.[3] Morrison & Foerster’s senior of counsel Lokke Moerel claims that bias does not result because “there is an error in the algorithm, but because the data use to train the algorithm are ‘biased.’”  She further argues that the practice of “blinding” algorithms to certain data points does not promote equality or fairness.  Rather, the solution is to allow the algorithm access to, but actively train the algorithm against overreliance on, those factors.   “Algorithms Can Reduce Discrimination, but Only with Proper Data,” 2018, available at <a href="https://iapp.org/news/a/algorithms-can-reduce-discrimination-but-only-with-proper-data/">https://iapp.org/news/a/algorithms-can-reduce-discrimination-but-only-with-proper-data/</a>

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Employment Practices and Future Technologies - Taking the Human Out of Human Resources

Clashing With Second Circuit, Court Orders Google to Turn Over Foreign-Stored Data

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The machines are taking over.  Not always apparent, but Artificial Intelligence (AI) and machine learning (ML) are finding footholds in numerous industries.  On

Employment Practices and Future Technologies - Taking the Human Out of Human Resources

By all accounts 2019 will bring significant expansion in the use and development of artificial intelligence (AI) by the U.S. federal government.  Notwithstanding some <a href="https://www.axios.com/power-shift-artificial-intelligence-funding-government-51f9a28a-0da5-4787-b84f-a69c9b9c92da.html">well-publicized rejections by industry</a> of the use of their AI technologies for offensive military and surveillance applications, the federal government remains a major supporter of and customer for AI technologies.  Below we outline some of the current federal government players in the AI space and their plans for the coming year.DARPAThe Defense Advanced Research Projects Agency (DARPA) is the research arm of the Department of Defense (DoD), initially developed to compete with the Soviet Union’s technological developments in the Cold War era.  Through a combination of innovative contracting methods and cooperative efforts, DARPA aims to include non-traditional defense contractors in the development of technologies with potential military applications. DARPA’s involvement in AI began in the 1960s, with a focus on rule-based systems performing narrowly defined tasks.  In the 1990s, AI expanded to include machine learning, which enabled analysis of large amounts of data, leading to important commercial and government applications. In fact, some of the first successes in AI were funded by DARPA, including the development of expert systems and search capabilities and advances in machine learning algorithms.In July 2018, DARPA announced the establishment of its <a href="https://www.darpa.mil/news-events/2018-07-20a">Artificial Intelligence Exploration (AIE) program</a>, which will utilize unique funding opportunities and streamlined contracting procedures to explore the feasibility of new AI concepts within 18 months with a goal of spurring major AI breakthroughs.  AIE awards of up to $1 million will be described in the issued AIE opportunities to be published on the FedBizOpps website under Program Announcement DARPA-PA-18-02.DARPA also announced a multi-year investment in its new “<a href="https://www.darpa.mil/work-with-us/ai-next-campaign">AI Next</a>” campaign, which will set aside more than $2 billion to address AI needs.  Among the goals of AI Next are automation of critical DoD business processes including security clearance vetting and software accreditation, enhancing machine learning security and reliability, and developing the next generation “third wave” of AI.  With AI Next, DARPA has announced its intention to move beyond “first wave” (rule-based) and “second wave” (statistical learning-based) AI to a new “third wave” of AI theory and applications that involve contextual adaptation utilizing models to explain and drive decision-making.  This third wave of AI will address some of the limitations of the prior wave technologies and enable AI to adapt to changing situations.In March 2019, DARPA will host an <a href="https://www.darpa.mil/news-events/artificial-intelligence-colloquium">AI Colloquium</a> that will bring together leaders in DoD and across industry to explore how AI technologies can apply to military missions.IARPAThe Intelligence Advanced Research Projects Activity (IARPA) is planning two upcoming AI-related programs. The first, called Secure, Assured, Intelligent Learning Systems (SAILS), is designed to explore ways to prevent attackers from revealing personally identifiable information contained in training data and statistical information used to train AI systems.  With SAILS, IARPA seeks ways to protect personal privacy by making machine learning algorithms less vulnerable to reverse engineering that reveals underlying data.  A second IARPA project, called TrojAI, aims to combat “Trojan horse” attacks, in which an attacker attempts to confuse or disrupt an intelligent system by injecting false data into a training data set.  For example, if an attacker injects fraudulent or distorted images of stop signs into the algorithms that teach self-driving vehicles to recognize stop signs using images containing the signs, the algorithm may fail to recognize certain stop signs in the real world, potentially putting lives at risk. TrojAI aims to develop software that can scan the output of an algorithm to determine whether the algorithm has been attacked in this manner.National Science FoundationThe National Science Foundation (NSF), a federal agency that supports fundamental research and education in all the non-medical fields of science and engineering, has a long history of supporting AI research.  The NSF is uniquely positioned to bring together various disciplines, including mathematics, computer science, engineering, cognitive science, psychology and linguistics to coordinate in the enhancement of AI capabilities.  The NSF has a number of <a href="https://www.nsf.gov/funding/pgm_list.jsp?ord=rcnt&org=nsf">funding opportunities</a> to support AI research.  For example, the NSF is currently supporting <a href="https://www.nsf.gov/pubs/2019/nsf19018/nsf19018.jsp">EArly-concept Grants for Exploratory Research</a> (EAGERs) to examine and understand the social challenges arising from AI technology and ways to overcome those challenges.  NSF recognizes that AI is only useful to the extent that the public embraces and accepts the technology.The White HouseIn late 2016, the Obama administration published the first <a href="https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf">U.S. strategic plan for AI research and development</a>, which outlined funding priorities for the federal government.  Following this lead, the Trump administration has taken additional steps to develop an AI strategy.  The White House hosted a May 2018 conference of government officials, industry leaders and academia at which the attendees discussed “the promise of AI” and identified the policies needed to encourage AI innovation in the United States.  Industry sectors represented at the summit included food and agriculture, energy and manufacturing, financial services, healthcare, and transportation and logistics.  President Trump’s FY2019 Budget Request was the first to designate artificial intelligence and autonomous and unmanned systems as Administration R&D priorities. In mid-February 2019, President Trump issued a new Executive Order on maintaining U.S. leadership in the field of AI.  The Order recognizes the essential role of the federal government in advancing AI research and development, promoting the trust of the American people in AI, training the U.S. workforce in AI, and protecting U.S. companies’ AI technology from competitors and nation states seeking to access technology by improper means.  To accomplish these goals, the Order announced the plan for a coordinated federal government strategy, the American AI Initiative.  The Initiative is to be coordinated by the National Science and Technology Council Select Committee on Artificial Intelligence, and implemented by those federal agencies (like DARPA, IARPA, and the NSF) that conduct foundational AI R&D, develop and deploy applications of AI technologies, provide educational grants, and regulate and provide guidance for applications of AI technologies.In sum, the foundational principles are in place to encourage a major expansion in federal AI-related R&D and adoption of AI technologies.  It remains to be seen precisely how readily a shift to AI will take place.  In the near term, companies seeking to further develop AI technologies should consider partnering with the federal government to take advantage of the availability of federal funding and the current buzz around all things AI.

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Tina is a partner in Morrison & Foerster’s Government Contracts practice. She represents a wide variety of government contractors including information technology, defense, biotechnology, and pharmace

Tina Reynolds

Tina is co-chair of Morrison Foerster’s Government Contracts & Public Procurement practice. She counsels a wide variety of government contractors on compliance with federal acquisition and ethics regulations. She drafts and negotiates contracts on their behalf and has been involved with numerous internal investigations and compliance reviews, and with bid protest, contract claims, and False Claims Act litigation. She also advises on M&A transactions involving government contractors.

Tina D. Reynolds

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Federal Government Expansion and Application of Artificial Intelligence Technologies

Supreme Court Restricts the Extraterritorial Reach of U.S. Patent Law for Exported Goods

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By all accounts 2019 will bring significant expansion in the use and development of artificial intelligence (AI) by the U.S. federal government.  Notwithstandin

Federal Government Expansion and Application of Artificial Intelligence Technologies

The Department of Commerce’s Bureau of Industry and Security (BIS) issued an Advance Notice of Proposed Rulemaking (ANPRM or the “Notice”) on November 19, 2018, concerning the list of “emerging and foundational technologies” that BIS was tasked to identify under the Export Control Reform Act of 2018 (ECRA). The concept of “emerging and foundational technologies” is central not only to ECRA, under which such technologies may be subject to new or additional export licensing requirements, but it is also central to recent reforms to the Committee on Foreign Investment in the United States (CFIUS), under which foreign investments in such technologies may be subject to enhanced requirements.Read our <a href="https://protect-us.mimecast.com/s/vBuuCmZM8kiyWk58UBOyDS">client alert</a>.

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Senior Advisor, National Security

Joseph Benkert is a senior advisor  in Morrison & Foerster’s National Security practice group. He advises  clients on critical national security matters pertaining to the Committee on  Foreign Investm

Joseph Benkert

Joseph A. Benkert

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Of Counsel

Bob Litt, former General Counsel for the Director of National Intelligence, is of counsel and co-chair of Morrison & Foerster’s Global Risk and Crisis Management group. He advises industry-leading org

Robert S. Litt

Robert Litt

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Charles Capito is a partner in both Morrison Foerster’s National Security and Government Contracts + Public Procurement practices.
In the National Security space, he has significant experience couns

Charles L. Capito

Charles Capito

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Opportunity to Comment on Emerging Technologies Subject to Future Export and Investment Restrictions

Biometric Information as Personal Information In A Brave New World of Regulatory Compliance

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The Department of Commerce’s Bureau of Industry and Security (BIS) issued an Advance Notice of Proposed Rulemaking (ANPRM or the “Notice”) on November 19, 2018,

Opportunity to Comment on Emerging Technologies Subject to Future Export and Investment Restrictions

Since the advent of artificial intelligence technology, there have been countless instances of machine learning algorithms yielding discriminatory outcomes. For instance, crime prediction tools frequently assign disproportionately higher risk scores to ethnic minorities. This is not because of an error in the algorithm, but because the historical data used to train the algorithm are “biased” (as the police stopped and searched more ethnic minorities, this group, by extension, also shows more convictions). To solve this, group indicators such as race, gender, and religion are often removed from the training data. The idea is that if the algorithm cannot “see” these elements, the outcome will not be discriminatory.      In her op-ed for IAPP Privacy Perspectives, Morrison & Foerster Senior Of Counsel <a href="https://www.mofo.com/people/lokke-moerel.html">Lokke Moerel</a> explains why this approach is ineffective in combatting algorithmic bias. She argues that only if we know which data subjects belong to vulnerable groups can biases in the historical data be made transparent and algorithms trained properly. The taboo against collecting such sensitive group indicators must be broken if we ever hope to eliminate future discrimination.Read Lokke's <a href="https://iapp.org/news/a/algorithms-can-reduce-discrimination-but-only-with-proper-data/">op-ed</a>.

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Senior Of Counsel

Among the world’s best-known privacy and cybersecurity advisers, Lokke Moerel is regularly called upon by some of the most sophisticated multinational organizations to confront their global privacy and ethical challenges. Lokke routinely offers guidance to clients when implementing new technologies and digital business models and assists them with their global cybersecurity incident response and regulatory investigations.

Lokke Moerel

Lokke Moerel is among the world’s best-known privacy lawyers. She advises companies on their global information handling governance, strategy, and compliance. “An authority on data protection matters” (Chambers 2014), Lokke is consistently ranked as a leader in data protection law in Chambers Global and Legal 500. She is also professor of global ICT law at Tilburg University, member of the Cyber Security Council of the Netherlands, the advisory body of the Dutch cabinet, and member of the Board of Trustees of WWF International.What do you like best about your work? I enjoy thinking about the data privacy issues and ethical dilemma’s presented by the new technologies, and helping companies decide on how to tune them and implement on a global basis, in an emerging regulatory environment.What do you enjoy doing in your free time? Travel with my husband and three children, the modern arts, but most of all, a good book!What individual most inspired you, and why? In my former EU-based firm, there were no female role- models (I was the first female partner with children) and partners step out at age 55. After moving to MoFo, I met women years older than me and fully at the forefront of their field. What an inspiration! I still have a whole career in front of me.

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Algorithms can reduce discrimination, but only with proper data

Don’t Get Caught by the Phisherman’s Hook This Tax Season

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Since the advent of artificial intelligence technology, there have been countless instances of machine learning algorithms yielding discriminatory outcomes. For

Algorithms can reduce discrimination, but only with proper data

Updating and building on guidance issued one year ago, the U.S. Department of Transportation published a new federal guidance document entitled Preparing for the Future of Transportation: Automated Vehicles 3.0 (“AV 3.0”). AV 3.0, released on October 4, 2018, incorporates extensive stakeholder feedback to provide updated voluntary guidance and policy considerations regarding safety standards and federal agencies’ roles in the development process of automated vehicles.Read our <a href="https://protect-us.mimecast.com/s/jj64CwpR2xSnXRWjT9eJgg">client alert</a>.

Fertility Center Litigation

Fitbit

MicroPort

Leading Healthcare, Pharmaceutical, and Medical Device Company

Lethal Injection Matters – Major Pharmaceutical Company

Pain Pump Matters – Large Pharmaceutical Company

Major Technology Company

Leading Chemical Company

Security Device Company

In re Air Crash Near Peixoto de Azevedo, Brazil

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Erin Bosman is chair of MoFo’s Class Actions and Mass Torts Practice Group and is a founder and co-head of the Artificial Intelligence Group. Clients value her business strategy, seeking her comprehensive counsel throughout the product life cycle, especially in high-stakes litigation and product recalls.

Erin M. Bosman

Erin Bosman is chair of MoFo’s Class Actions and Mass Torts Practice Group and is a founder and co-head of the Artificial Intelligence Group. Clients value her business strategy, seeking her comprehensive counsel throughout the product life cycle, especially in high-stakes litigation and product recalls.

Erin Bosman

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U.S. DOT Revamps Federal Guidance on Automated Vehicle Development and Rollout

California Opens the Road to Fully Driverless Cars; Federal Policymaking Slowly Rolls Forward

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Updating and building on guidance issued one year ago, the U.S. Department of Transportation published a new federal guidance document entitled Preparing for th

U.S. DOT Revamps Federal Guidance on Automated Vehicle Development and Rollout

The European Commission has stated that it wants to establish the EU as a leader in the AI industry in order to, among other things, discontinue non-EU companies’ dominance of the EU tech market. Nevertheless, the Commission’s goals for increasing the use of AI in the EU are vague, the Commission has failed to clarify how the EU should go about achieving tasks that are indispensable to the AI industry’s success, and a “European AI Alliance” intended to encourage relevant stakeholders to share best practices and carry out other activities related to the AI industry’s development isn’t scheduled to take place until July 2019.

Alistair Maughan co-leads Morrison & Foerster's European Technology & Commercial Practice.

Alistair Maughan

Alistair advises on the deployment of new technology platforms involving, for example, the internet of things, AI and machine-learning, and blockchain and distributed ledger technology.

Can the EU become a leader in AI Technology?

The European Commission has stated that it wants to establish the EU as a leader in the AI industry in order to, among other things, discontinue non-EU companie

Can the EU become a leader in AI Technology?

When companies employ artificial intelligence technologies they have to be mindful of the legal and ethical problems those technologies might cause. While the scope of legal concerns surrounding AI technologies’ impact is broad, of particular concern are instances of unlawful bias and discrimination, privacy law violations, and legal liability resulting from harm caused by the AI technology.The good news is that corporate AI policies can help companies to encourage compliance and guide decisions about the acceptable use of AI technologies. This article describes the points that corporate AI policies should address.Read our piece in <a href="http://www.corporatecomplianceinsights.com/managing-corporate-ai/">Corporate Compliance Insights</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.