New York City Task Force to Consider Algorithmic Harm

One might hear discussions about backpropagation, activation functions, and gradient descent when visiting an artificial intelligence company. But more recently, terms like bias and harm associated with AI models and products have entered tech’s vernacular. These issues also have the attention of many outside of the tech world following reports of AI systems performing better for some users than for others when making life-altering decisions about prison sentences, creditworthiness, and job hiring, among others.

Considering the recent number of accepted conference papers about algorithmic bias, AI technologists, ethicists, and lawyers seems to be proactively addressing the issue by sharing with each other various technical and other solutions. At the same time, at least one legislative body–the New York City Council–has decided to explore ways to regulate AI technology with an unstated goal of rooting out bias (or at least revealing its presence) by making AI systems more transparent.

New York City’s passage of the “Automated decision systems used by agencies” law (NYC Local Law No. 49 of 2018, effective January 11, 2018), creates a task force under the aegis of Mayor de Blasio’s office. The task force will convene no later than early May 2018 for the purpose of identifying automated decision systems used by New York City government agencies, developing procedures for identifying and remedying harm, developing a process for public review, and assessing the feasibility of archiving automated decision systems and relevant data.

The law defines an “automated decision system” as:

“a computerized implementations of algorithms, including those derived from machine learning or other data processing or artificial intelligence techniques, which are used to make or assist in making decisions.”

The law defines an “agency automated decision system” as:

“an automated decision system used by an agency to make or assist in making decisions concerning rules, policies or actions implemented that impact the public.”

While the law does not specifically call out bias, the source of algorithmic unfairness and harm can be traced in large part to biases in the data used to train algorithmic models. Data can be inherently biased when it reflects the implicit values of a limited number of people involved in its collection and labelling, or when the data chosen for a project does not represent a full cross-section of society (which is partly the result of copyright and other restrictions on access to proprietary data sets, and the ease of access to older or limited data sets where groups of people may be unrepresented or underrepresented). A machine algorithm trained on this data will “learn” the biases, and can perpetuate bias when it is asked to make decisions.

Some argue that making algorithmic black boxes more transparent is key to understanding whether an algorithm is perpetuating bias. The New York City task force could recommend that software companies that provide automated decision systems to New York City agencies make their systems transparent by disclosing details about their models (including source code) and producing the data used to create their models.

Several stakeholders have already expressed concerns about disclosing algorithms and data to regulators. What local agency, for example, would have the resources to evaluate complex AI software systems? And how will source code and data, which may embody trade secrets and include personal information, be safeguarded from inadvertent public disclosure? And what recourse will model developers have before agencies turn over algorithms (and the underlying source code and data) in response to Freedom of Information requests and court-issued subpoenas?

Others have expressed concerns that regulating at the local level may lead to disparate and varying standards and requirements, placing a huge burden on companies. For example, New York City may impose standards different from those imposed by other local governments. Already, companies are having to deal with different state regulations governing AI-infused autonomous vehicles, and will soon have to contend with European Union regulations concerning algorithmic data (GDPR Art. 22; effective May 2018) that may be different than those imposed locally.

Before their job is done, New York City’s task force will likely hear from many stakeholders, each with their own special interests. In the end, the task force’s recommendations, especially those on how to remedy harm, will receive careful scrutiny, and not just by local stakeholders, but also by policymakers far removed from New York City, because as AI technology impacts on society grow, the pressure to regulate AI systems on a national basis is likely to grow.

Information and/or references used for this post came from the following:

NYC Local Law No. 49 of 2018 (available at here) and various hearing transcripts

Letter to Mayor Bill de Blasio, Jan. 22, 2018, from AI Now and others (available here)

EU General Data Protection Regulations (GDPR), Art. 22 (“Automated Individual Decision-Making, Including Profiling”), effective May 2018.

Dixon et. al “Measuring and Mitigating Unintended Bias in Text Classification”; AAAI 2018 (accepted paper).

W. Wallach and G. Marchant, “An Agile Ethical/Legal Model for the International and National Governance of AI and Robotics”; AAAI 2018 (accepted paper).

D. Tobey, “Software Malpractice in the Age of AI: A Guide for the Wary Tech Company”; AAAI 2018 (accepted paper).

When It’s Your Data But Another’s Stack, Who Owns The Trained AI Model?

Cloud-based machine learning algorithms, made available as a service, have opened up the world of artificial intelligence to companies without the resources to organically develop their own AI models. Tech companies that provide these services promise to help companies extract insights from the company’s unique customer, employee, product, business process, and other data, and to use those insights to improve decisions, recommendations, and predictions without the company having an army of data scientists and full stack developers. Simply open an account, provide data to the service’s algorithms, train and test an algorithm, and then incorporate the final model into the company’s toolbox.

While it seems reasonable to assume a company owns a model it develops with its own data–even one based on an algorithm residing on another’s platform–the practice across the industry is not universal. Why this matters is simple: a company’s model (characterized in part by model parameters, network architecture, and architecture-specific hyperparameters associated with the model) may provide the company with an advantage over competitors. For instance, the company may have unique and proprietary data that its competitors do not have. If a company wants to extract the most value from its data, it should take steps to not only protect its valuable data, but also the models created based on that data.

How does a company know if it has not given away any rights to its own data uploaded to another’s cloud server, and that it owns the models it created based on its data? Conversely, how can a company confirm the cloud-based machine learning service has not reserved any rights to the model and data for its own use? The answer, of course, is likely embedded in multiple terms of service, privacy, and user license agreements that apply to the use of the service. If important provisions are missing, vague, or otherwise unfavorable, a company may want to look at alternative cloud-based platforms.

Consider the following example. Suppose a company wants to develop an AI model to improve an internal production process, one the company has enhanced over the years and that gives it a competitive advantage over others. Maybe its unique data set derives from a trade secret process or reflects expertise that its competitors could not easily replicate. With data in hand, the company enters into an agreement with a cloud-based machine learning service, uploads its data, and builds a unique model from the service’s many AI technologies, such as natural language processing (NLP), computer vision classifiers, and supervised learning tools. Once the best algorithms are selected, the data is used to train them and a model is created. The model can then be used in the company’s operations to improve efficiency and cut costs.

Now let us assume the cloud service provider’s terms of service (TOS) states something like the following hypothetical:

“This agreement does not impliedly or otherwise grant either party any rights in or to the other’s content, or in or to any of the other’s trade secret or rights under intellectual property laws. The parties acknowledge and agree that Company owns all of its existing and future intellectual property and other rights in and concerning its data, the applications or models Company creates using the services, and Company’s project information provided as part of using the service, and Service owns all of its existing and future intellectual property and other rights in and to the services and software downloaded by Company to access the services. Service will not access nor use Company’s data, except as necessary to provide the services to Company.”

These terms would appear to generally protect certain of the company’s rights and interest in its data and any models created using the company’s data, and further the terms indicate the machine learning service will not use the company’s data and the model trained using the data, except to provide the service. That last part–the exception–needs careful attention, because how a company defines the services it performs can be stated broadly.

Now consider the following additional hypothetical TOS:

“Company acknowledges that Service may access Company’s data submitted to the service for the purpose of developing and improving the service, and any other of Service’s current, future, similar, or related services, and Company agrees to grant Service, its licensees, affiliates, assigns, and agents an irrevocable, perpetual right and permission to use Company’s data, because without those rights and permission Service cannot provide or offer the services to Company.”

The company may not be comfortable agreeing to those terms, unless the terms are superseded with other, more favorable terms in another applicable agreement related to using the cloud-based service.

So while AI may be “the new electricity” powering large portions of the tech sector today, data is an important commodity all on its own, and so are the models behind an AI company’s products. So don’t forget to review the fine print before uploading company data to a cloud-based machine learning service.

Not Could, But Should Intelligent Machines Have Rights?

The question of whether intelligent machines of the future could own rights to their creations has been raised in the last few years, including last year at an international conference. Often, the question of rights for machines arises in the context of recognizing pictorial copyrights in images auto-generated by algorithms, but also in the context of invention (patent) rights. The progeny of these intellectual property (IP)-specific machine-rights questions suggests another, broader query: once artificial general intelligence (AGI) is achieved, should there be legal recognition of rights for intelligent machines?

In the context of IP, US laws recognize IP rights as arising from human creativity in the sciences and useful arts as a means to incentivize continuing research and creativity. Incentives flow in part from the economic nature of IP rights. Owning copyrights, for example, gives the owner the right to make and distribute copies of a creative work and to permit others to do so conditioned upon payment of a licensing fee, royalty, or some other consideration. Similarly, patent rights give a patent owner the right to exclude others from using one’s invention unless permission is granted, which again may be conditioned upon payment of a fee. In the US, employees who create copyrightable works or patentable invention are typically obligated to assign all their IP rights to their employer without further compensation beyond a regular salary. In such cases, an employer ultimately owns and benefits from its employee’s creations and discoveries.

But if we’re talking about intelligent machines, the debate about machine rights would have to take into consideration whether an intelligent machine could or would “care” about the economic benefits or have other reasons to innovate and create in the first instance. Aside from the prospect of ensuring an intelligent machine’s rights flow to its owner and are not lost, what purpose would be served by legally recognizing a future intelligent machine has rights at all? And if machine IP or other rights were recognized, what consideration would be appropriate to give future intelligent machines in a bargained-for exchange for their recognized rights, assuming the rights have value?

Some have suggested that the very notion of what it is to have “intelligence” will be protected by recognizing that intelligent machines are capable of owning rights. Put another way, if we do not recognize rights attached to the creative output of an intelligent machine, we undermine or diminish the very same rights associated with natural intelligence-generated creations. So the rights a photographer has in a photograph, or an inventor has to a new drug discovery, would be lessened if the same rights were not recognized in an intelligent machine’s similar endeavors. It seems doubtful, though, that an intelligent machine would care that it was given legally cognizable rights only because natural intelligent beings felt it important to do so to avoid diminishing the value they place on their own “intelligence.”

Despite recent advances in natural language processing (NLP), reinforcement learning (RL), and other artificial intelligence technologies, such as those in the areas of reading comprehension accuracy, computer vision object detection, and others, achieving AGI may be years if not decades away. Thus, machine rights is not a question that is especially pressing right now. On the other hand, one cannot predict when someone will announce a major breakthrough in AI and place this question front and center. So the debate about future intelligent machine rights is likely to continue. Hopefully, the debate will not distract from present-day efforts by many to move the needle toward legally recognizing important individual rights that protect people in the course of their daily interactions with existing and future AI technologies.

Evaluating and Valuing an AI Business: Don’t Forget the IP

After record-breaking funding and deals involving artificial intelligence startups in 2017, it may be tempting to invest in the next AI business or business idea without a close look beyond a company’s data, products, user-base, and talent. Indeed, big tech companies seem willing to acquire, and investors seem happy to invest in, AI startups even before the founders have built anything. Defensible business valuations, however, involve many more factors, all of which need careful consideration during early planning of a new AI business or investing in one. One factor that should never be overlooked is a company’s actual or potential intellectual property rights underpinning its products.

Last year, Andrew Ng (of Coursera and Stanford; formerly Baidu and Google Brain) spoke about a Data-Product-Users model for evaluating whether an AI business is “defensible.” In this model, data holds a prominent position because information extracted from data drives development of products, which involve algorithms and networks trained using the data. Products in turn attract users who engage with the products and generate even more data.

While an AI startup’s data, and its ability to accumulate data, will remain a key valuation factor for investors, excellent products and product ideas are crucial for long-term data generation and growth. Thus, for an AI business to be defensible in today’s hot AI market, its products, more than its data, need to be defensible. One way to accomplish that is through patents.

It can be a challenge, though, to obtain patents for certain AI technologies. That’s partly due to application stack developers and network architects relying on open source software and in-licensed third-party hardware tools with known utilities. Publicly-disclosing information about products too early, and publishing novel problem-solutions related to their development, including describing algorithms and networks and their performance and accuracy, also can hinder a company’s ability to protect product-specific IP rights around the world. US federal court decisions and US Patent and Trademark Office proceedings can also be obstacles to obtaining and defending software-related patents (as discussed here). Even so, seeking patents (as well as carefully conceived brands and associated trademarks for products) is one of the best options for demonstrating to potential investors that a company’s products or product ideas are defensible and can survive in a competitive market.

Patents of course are not just important for AI startups, but also for established tech companies that acquire startups. IBM, for example, reportedly obtained or acquired about 1,400 patents in artificial intelligence in 2017. Amazon, Cisco, Google, and Microsoft were also among the top companies receiving machine learning patents in 2017 (as discussed here).

Patents may never generate direct revenues for an AI business like a company’s products can (unless a company can find willing licensees for its patents). But protecting the IP aspects of a product’s core technology can pay dividends in other ways, and thus adds value. So when brainstorming ideas for your company’s next AI product or considering possible investment targets involving AI technologies, don’t forget to consider whether the idea or investment opportunity has any IP associated with the AI.

Recognizing Individual Rights: A Step Toward Regulating Artificial Intelligence Technologies

In the movie Marjorie | Prime (August 2017), John Hamm plays an artificial intelligence version of Marjorie’s deceased husband, visible to Marjorie as a holographic projection in her beachfront home. As Marjorie (played by Lois Smith) interacts with Hamm’s Prime through a series of one-on-one conversations, the AI improves its cognition by observing and processing Marjorie’s emotional expressions, movements, and speech. The AI also learns from interactions with Marjorie’s son-in-law (Tim Robbins) and daughter (Geena Davis), as they recount highly personal and painful episodes of their lives. Through these interactions, Prime ends up possessing a collective knowledge greater and more personal and intimate than Marjorie’s original husband ever had.

Although not directly explored in the movie’s arc, the futuristic story touches on an important present-day debate about the fate of private personal data being uploaded to commercial and government AI systems, data that theoretically could persist in a memory device long after the end of the human lives from which the data originated, for as long as its owner chooses to keep it. It also raises questions about the fate of knowledge collected by other technologies perceiving other people’s lives, and to what extent these percepts, combined with people’s demographic, psychographic, and behavioristic characteristics, would be used to create sharply detailed personality profiles that companies and governments might abuse.

These are not entirely hypothetical issues to be addressed years down the road. Companies today provide the ability to create digital doppelgangers, or human digital twins, using AI technologies. And collecting personal information from people on a daily basis as they interact with digital assistants and other connected devices is not new. But as Marjorie|Prime and several non-cinematic AI technologies available today illustrate, AI systems allow the companies who build them unprecedented means for receiving, processing, storing, and taking actions based on some of the most personal information about people, including information about their present, past, and trending or future emotional states, which marketers for years have been suggesting are the keys to optimizing advertising content.

Congress recently acknowledged that “AI technologies are rapidly evolving in capability and application throughout society,” but the US currently has no federal policy towards AI and no part of the federal government has ownership of the advancement of AI technologies. Left unchecked in an unregulated market, as is largely the case today, AI technological advancements may trend in a direction that may be inconsistent with collective values and goals.

Identifying individual rights

One of the first questions those tasked with developing laws, regulations, and policies directed toward AI should ask is, what are the basic individual rights–rights that arise in the course of people interacting with AI technologies–that should be recognized? Answering that question will be key to ensuring that enacted laws and promulgated regulations achieve one of Congress’s recently stated goals: ensuring AI technologies benefit society. Answering that question now will be key to ensuring that policymakers have the necessary foundation in front of them and will not be unduly swayed by influential stakeholders as they take up the task of deciding how and/or when to regulate AI technologies.

Identify individual rights leads to their recognition, which leads to basic legal protections, whether in the form of legislation or regulation, or, initially, as common law from judges deciding if and how to remedy a harm to a person or property caused by an AI system. Fortunately, identifying individual rights is not a formidable task. The belief that people have a right to be let alone in their private lives, for example, established the basic premise for privacy laws in the US. Those same concerns about intrusion into personal lives ought to be among the first considerations by those tasked with formulating and developing AI legislation and regulations. The notion that people have a right to be let alone has led to the identification of other individual rights that could protect people in their interactions with AI systems. These include the right of transparency and explanation, the right of audit (with the objective to reveal bias, discrimination, and content filtering, and thus maintain accountability), the right to know when you are dealing with an AI system and not a human, and the right to be forgotten (that is, mandatory deletion of one’s personal data), among others.

Addressing individual rights, however, may not persuade everyone to trust AI systems, especially when AI creators cannot explain precisely the basis for certain actions taken by trained AI technologies. People want to trust that owners and developers of AI systems that use private personal data will employ the best safeguards to protect that data. Trust, but verify, may need to play a role in policy-making efforts even if policies appear to comprehensively address individual rights. Trust might be addressed by imposing specific reporting and disclosure requirements, such as those suggested by federal lawmakers in pending federal autonomous driving legislation.

In the end, however, laws and regulations developed with privacy and other individual rights in mind, that address data security and other concerns people have about trusting their data to AI companies, will invariably include gaps, omissions, and incomplete definitions. The result may be unregulated commercial AI systems, and AI businesses finding workarounds. In such instances, people may have limited options other than to fully opt out, or accept that individual AI technology developers’ work was motivated by ethical considerations and a desire to make something that benefits society. The pressure within many tech companies and startups to push new products out to the world every day, however, could make prioritizing ethical considerations a challenge. Many organizations focused on AI technologies, some of which are listed below, are working to make sure that doesn’t happen.

Rights, trust, and ethical considerations in commercial endeavors can get overshadowed by financial interests and the subjective interests and tastes of individuals. It doesn’t help that companies and policymakers may also feel that winning the race for AI dominance is a factor to be considered (which is not to say that such a consideration is antithetical to protecting individual rights). This underscores the need for thoughtful analysis, sooner rather than later, of the need for laws and regulations directed toward AI technologies.

To learn more about some of these issues, visit the websites of the following organizations, who are active in AI policy research: Access Now, AI Now, and Future of Life.

Legal Tech, Artificial Intelligence, and the Practice of Law in 2018

Due in part to a better understanding of available artificial intelligence legal tech tools, more lawyers will adopt and use AI technologies in 2018 than ever before. Better awareness will also drive creation and marketing of specialized legal practice areas within law firms focused on AI, more lawyers with AI expertise, new business opportunities across multiple practice groups, and the possibly of another round of Associate salary increases as the demand for AI talent both in-house and at law firms escalates in response to the continued expansion of AI in key industries.

The legal services industry is poised to adopt AI technologies at the highest level seen to date. But that doesn’t mean lawyers are currently unfamiliar with AI. In fact, AI technologies are widely used by legal practitioners, such as tech that power case law searches (websites services in which a user’s natural language search query is processed by a machine learning algorithm, and displays a ranked and sorted list of relevant cases), and that are used in electronic discovery of documents (predictive analytics software that finds and tags relevant electronic documents for production during a lawsuit based on a taxonomy of keywords and phrases agreed upon by the parties).

Newer AI-based software solutions, however, from companies like Kira and Ross, among dozens of others now available, may improve the legal services industry’s understanding of AI. These solutions offer increased efficiency, improved client service, and reduced operating costs. Efficiency, measured in terms of the time it takes to respond to client questions and the amount of billable hours expended, can translate into reduced operating costs for in-house counsel, law firm lawyers, judges, and their staffs, which is sure to get attention. AI-powered contract review software, for example, can take an agreement provided by opposing counsel and nearly instantaneously spot problems, a process that used to take an Associate or Partner a half-hour or more to accomplish, depending on the contract’s complexity. In-house counsel are wary of paying biglaw hourly rates for such mundane review work, so software that can perform some of the work seems like a perfect solution. The law firms and their lawyers that become comfortable using the latest AI-powered legal tech will be able to boast of being cutting edge and client-focused.

Lawyers and law firms with AI expertise are beginning to market AI capabilities on their websites to retain existing clients and capture new business, and this should increase in 2018. Firms are focusing efforts on industry segments most active in AI, such as tech, financial services (banks and financial technology companies or “fintech”), computer infrastructure (cloud services and chip makers), and other peripheral sectors, like those that make computer vision sensors and other devices for autonomous vehicles, robots, and consumer products, to name a few. Those same law firms are also looking at opportunities within the ever-expanding software as a service industry, which provides solutions for leveraging information from a company’s own data, such as human resources data, process data, quality assurance data, etc. Law practitioners who understand how these industries are using AI technologies, and AI’s limitations and potential biases, will have an edge when it comes to business development in the above-mentioned industry segments.

The impacts of AI on the legal industry in 2018 may also be reflected in law firm headcounts and salaries. Some reports suggest that the spread of AI legal tech could lead to a decrease in lawyer ranks, though most agree this will happen slowly and over several years.

At the same time, however, the increased attention directed at AI technologies by law firm lawyers and in-house counsel in 2018 may put pressure on law firms to adjust upward Associate salaries, like many did during the dot-com era when demand for new and mid-level lawyers equipped to handle cash-infused Silicon Valley startups’ IPO, intellectual property, and contract issues skyrocketed. A possible Associate salary spike in 2018 may also be a consequence of, and fueled by, huge salaries reportedly being paid in the tech sector, where big tech companies spent billions in 2016 and 2017 acquiring AI start-ups to add talent to their rosters. A recent report suggests annual salary and other incentives in the range of $350,000 to $500,000 being paid for newly-minted PhDs and to those with just a few years of AI experience. At those levels, recent college graduates contemplating law school and a future in the legal profession might opt instead to head to graduate school for a Masters or PhD in an AI field.

Congress Takes Aim at the FUTURE of Artificial Intelligence

As the calendar turns over to 2018, artificial intelligence system developers will need to keep an eye on first of its kind legislation being considered in Congress. The “Fundamentally Understanding The Usability and Realistic Evolution of Artificial Intelligence Act of 2017,” or FUTURE of AI Act, is Congress’s first major step toward comprehensive regulation of the AI tech sector.

Introduced on December 22, 2017, companion bills S.2217 and H.R.4625 touch on a host of AI issues, their stated purposes mirroring concerns raised by many about possible problems facing society as AI technologies becomes ubiquitous. The bills propose to establish a federal advisory committee charged with reporting to the Secretary of Commerce on many of today’s hot button, industry-disrupting AI issues.

Definitions

Leaving the definition of “artificial intelligence” open for later modification, both bills take a broad brush at defining, inclusively, what an AI system is, what artificial general intelligence (AGI) means, and what are “narrow” AI systems, which presumably would each be treated differently under future laws and implementing regulations.

Under both measures, AI is generally defined as “artificial systems that perform tasks under varying and unpredictable circumstances, without significant human oversight, or that can learn from their experience and improve their performance,” and encompass systems that “solve tasks requiring human-like perception, cognition, planning, learning, communication, or physical action.” According to the bills’ sponsors, the more “human-like the system within the context of its tasks, the more it can be said to use artificial intelligence.”

While those definitions and descriptions include plenty of ambiguity, characteristic of early legislative efforts, the bills also provide several clarifying examples: AI involves technologies that think like humans, such as cognitive architectures and neural networks; those that act like humans, such as systems that can pass the Turing test or other comparable test via natural language processing, knowledge representation, automated reasoning, and learning; those using sets of techniques, including machine learning, that seek to approximate some cognitive task; and AI technologies that act rationally, such as intelligent software agents and embodied robots that achieve goals via perception, planning, reasoning, learning, communicating, decision making, and acting.

The bills describe AGI as “a notional future AI system exhibiting apparently intelligent behavior at least as advanced as a person across the range of cognitive, emotional, and social behaviors,” which is generally consistent with how many others view the concept of an AGI system.

So-called narrow AI is viewed as an AI system that addresses specific application areas such as playing strategic games, language translation, self-driving vehicles, and image recognition. Plenty of other AI technologies today employ what the sponsors define as narrow AI.

The FUTURE of AI Committee

Both the House and Senate versions would establish a FUTURE of AI advisory committee made up of government and private-sector members tasked with evaluating and reporting on AI issues.

The bills emphasize that the committee should consider accountability and legal rights issues, including identifying where responsibility lies for violations of laws by an AI system, and assessing the compatibility of international regulations involving privacy rights of individuals who are or will be affected by technological innovation relating to AI. The committee will evaluate whether advancements in AI technologies have or will outpace the legal and regulatory regimes implemented to protect consumers, and how existing laws, including those concerning data access and privacy (as discussed here), should be modernized to enable the potential of AI.

The committee will study workforce impacts, including whether and how networked, automated, AI applications and robotic devices will displace or create jobs and how any job-related gains from AI can be maximized. The committee will also evaluate the role ethical issues should take in AI development, including whether and how to incorporate ethical standards in the development and implementation of AI, as suggested by groups such as IEEE’s Global Initiative on Ethics of Autonomous and Intelligent Systems.

The committee will consider issues of machine learning bias through core cultural and societal norms, including how bias can be identified and eliminated in the development of AI and in the algorithms that support AI technologies. The committee will focus on evaluating the selection and processing of data used to train AI, diversity in the development of AI, the ways and places the systems are deployed and the potential harmful outcomes, and how ongoing dialogues and consultations with multi-stakeholder groups can maximize the potential of AI and further development of AI technologies that can benefit everyone inclusively.

The FUTURE of AI committee will also consider issues of competitiveness of the United States, such as how to create a climate for public and private sector investment and innovation in AI, and the possible benefits and effects that the development of AI may have on the economy, workforce, and competitiveness of the United States. The committee will be charged with reviewing AI-related education; open sharing of data and the open sharing of research on AI; international cooperation and competitiveness; opportunities for AI in rural communities (that is, how the Federal Government can encourage technological progress in implementation of AI that benefits the full spectrum of social and economic classes); and government efficiency (that is, how the Federal Government utilizes AI to handle large or complex data sets, how the development of AI can affect cost savings and streamline operations in various areas of government operations, including health care, cybersecurity, infrastructure, and disaster recovery).

Non-profits like AI Now and Future of Life, among others, are also considering many of the same issues. And while those groups primarily rely on private funding, the FUTURE of AI advisory committee will be funded through Congressional appropriations or through contributions “otherwise made available to the Secretary of Commerce,” which may include donation from private persons and non-federal entities that have a stake in AI technology development. The bills limit private donations to less than or equal to 50% of the committee’s total funding from all sources.

The bills’ sponsors says that AI’s evolution can greatly benefit society by powering the information economy, fostering better informed decisions, and helping unlock answers to questions that are presently unanswerable. Their sentiment that fostering the development of AI should be done in a way that maximizes AI’s benefit to society provides a worthy goal for the FUTURE of AI advisory committee’s work. But it also suggests how AI companies may wish to approach AI technology development efforts, especially in the interim period before future legislation becomes law.

Patenting Artificial Intelligence: Innovation Spike Follows Broader Market Trend

If you received a US patent for a machine learning invention recently, count yourself among a record number of innovators named on artificial intelligence technology patents issued in 2017. There’s also good chance you worked for one of the top companies earning patents for machine learning, neural network, and other AI technologies, namely IBM, Amazon, Cisco, Google, and Microsoft, according to public patent records (available through mid-December). This year’s increase in the number of issued patents reflects similar record increases in the level of investment dollars flowing to AI start-ups and the number of AI tech sector M&A deals in 2017.

As the chart indicates, US patents directed to “machine learning” jumped over 20% in 2017 compared to 2016, and that follows an even larger estimated 38% annual increase between 2015 and 2016. Even discounting the patents that merely mention machine learning in passing, the numbers are still quite impressive, especially given the US Supreme Court’s 2014 Alice Corp. Pty Ltd. v. CLS Bank decision, which led to the invalidation of many software and business method patents and likely also put the brakes on software-related patent application filings (as explained here) beginning in 2014. So the recent jump in issued patents for “machine learning,” “artificial intelligence,” and “neural network” inventions suggests that specific applications of those technologies remain patentable despite Alice.

A jump in issued patents in a highly competitive, increasingly crowded market segment, could lead to an uptick in patent-related infringement. Already, estimates by some suggest that 35% more companies expect to face IP litigation in 2018 compared to 2017.

Autonomous Vehicles Get a Pass on Federal Statutory Liability, At Least for Now

Consumers may accept “good enough” when it comes to the performance of certain artificial intelligence systems, such as AI-powered Internet search results. But in the case of autonomous vehicles, a recent article in The Economist argues that those same consumers will more likely favor AI-infused vehicles demonstrating the “best” safety record.

If that holds true, a recent Congressional bill directed at autonomous vehicles–the so-called “Safely Ensuring Lives Future Deployment and Research in Vehicle Evolution Act,” or the SELF DRIVE Act (H.R. 3388)–should be well received by safety-conscious consumers. If signed into law, however, H.R. 3388 will require those same consumers to turn to the courts to determine liability and the magnitude of possible damages from vehicle crash events. That’s because the bill as currently written takes a pass on providing a statutory scheme for allocating crash-related liability.

H.R. 3388 passed the House by vote in early September 2017 (a similar bill is working its way in the Senate). Like several earlier proposals made public by the House Energy and Commerce Committee in connection with hearings in June 2017, the resolution is one of the first federal attempts at closely regulating AI systems embodied in a major consumer product (at the state level, at least twenty states have enacted laws regarding some aspect of self-driving vehicles). The stated purpose of the SELF DRIVE Act is to memorialize the Federal role in ensuring the safety of highly automated vehicles as it relates to design, construction, and performance, by encouraging the testing and deployment of such vehicles.

Section 8 of the bill is notable in that it would require future rulemaking to require manufacturers to inform consumers of the capabilities and limitations of a vehicle’s “driving automation system.” The bill would define “automated driving system” as “the hardware and software that are collectively capable of performing the entire dynamic driving task on a sustained basis, regardless of whether such system is limited to a specific operational design domain.” The bill would define “dynamic driving task” as “the real time operational and tactical functions required to operate a vehicle in on-road traffic,” including monitoring the driving environment via object and event detection, recognition, classification, and response preparation and object and event response execution.

Requiring manufacturers to inform consumers of the “capabilities and limitations” of a vehicle’s “driving automation system,” combined with published safety statistics, might steer educated consumers toward a particular make and model, much like other vehicle features like lane departure warning and automatic braking features do. In the case of liability for crashes, however, H.R. 3388 would amend existing federal laws to clarify that “compliance with a motor vehicle safety standard…does not exempt a person from liability at common law” and common law claims are not preempted.

In other words, vehicle manufacturers who meets all of H.R. 3388’s express standards (and future regulatory standards, which the bill mandates be written by the Department of Transportation and other federal agencies) could still be subject to common law causes of action, just as they are today.

Common law refers to the body of law developed over time by judges in the course of applying, to a set of facts and circumstances, relevant legal principles developed in previous court decisions (i.e., precedential decisions). Common law liability considers which party should be held responsible (and thus should pay damages) to another party who alleges some harm. Judicial common law decisions are thus generally viewed as being limited to a case’s specific facts and circumstances. Testifying before the House Committee on June 27, 2017, George Washington University Law School’s Alan Morrison described one of the criticisms lodged against relying solely on common law approaches to regulating autonomous vehicles and assessing liability: common law develops slowly over time.

“Traditionally, auto accidents and product liability rules have been matters of state law, generally developed by state courts, on a case by case basis,” Morrison said in prepared remarks for the record during testimony back in June. “Some scholars and others have suggested that [highly autonomous vehicles, HAVs] may be an area, like nuclear power was in the 1950s, in which liability laws, which form the basis for setting insurance premiums, require a uniform national liability answer, especially because HAVs, once they are deployed, will not stay within state boundaries. They argue that, in contrast to common law development, which can progress very slowly and depends on which cases reach the state’s highest court (and when), legislation can be acted on relatively quickly and comprehensively, without having to wait for the ‘right case’ to establish the [common] law.”

For those hoping Congress would use H.R. 3388 as an opportunity to issue targeted statutory schemes containing specific requirements covering the performance and standards for AI-infused autonomous vehicles, which might provide guidance for AI developers in many other industries, the resolution may be viewed as disappointing. H.R. 3388 leaves unanswered questions about who should be liable in cases where complex hardware-software systems contribute to injury or simply fail to work as advertised. Autonomous vehicles rely on sensors for “monitoring the driving environment via object and event detection” and software trained to identify objects from that data (i.e., “object and event…recognition, classification, and response preparation”). Should a sensor manufacturer be held liable if, for example, its sensor sampling rate is too slow and its field of vision too narrow, or the software provider who trained its computer vision algorithm on data from 50,000 vehicle miles traveled instead of 100,000, or the vehicle manufacturer who installed those hardware and software components? What if a manufacturer decides not to inform consumers of those limitations in its statement of “capabilities and limitations” of its “driving automation systems”? Should a federal law even attempt to set such detailed, one size fits all standards? As things stand now, answers to these questions may become apparent only after courts consider them in the course of deciding liability in common law injury and product liability cases.

The Economist authors predict that companies whose AI is behind the fewest autonomous vehicle crashes “will enjoy outsize benefits.” Quantifying those benefits, however, may need to wait until after potential liability issues in AI-related cases become clearer over time.

The AI Summit New York City: Takeaways For the Legal Profession

This week, business, technology, and academic thought leaders in Artificial Intelligence are gathered at The AI Summit in New York City, one of the premier international conferences offered for AI professionals. Below, I consider two of the three takeaways from Summit Day 1, published yesterday by AI Business, from the perspective of lawyers looking for opportunities in the burgeoning AI market.

“1. The tech landscape is changing fast – with big implications for businesses”

If a year from now your law practice has not fielded at least one query from a client about AI technologies, you are probably going out of your way to avoid the subject. It is almost universally accepted that AI technologies in one form or another will impact nearly every industry. Based on recently-published salary data, the industries most active in AI are tech (think Facebook, Amazon, Alphabet, Microsoft, Netflix, and many others), financial services (banks and financial technology companies or “fintech”), and computer infrastructure (Amazon, Nvidia, Intel, IBM, and many others; in areas such as chips for growing computational speed and throughput, and cloud computing for big data storage needs).

Of course, other industries are also seeing plenty of AI development. The automotive industry, for example, has already begun adopting machine learning, computer vision, and other AI technologies for autonomous vehicles. The robotics and chatbot industries have seen great strides lately, both in terms of humanoid robotic development, and consumer-machine interaction products such as stationary and mobile digital assistants (e.g., personal robotic assistants, as well as utility devices like autonomous vacuums). And of course the software as a service industry, which leverages information from a company’s own data, such as human resources data, process data, healthcare data, etc., seems to offers new software solutions to improve efficiencies every day.

All of this will translate into consumer adoption of specific AI technologies, which is reported to already be at 10% and growing. The fast pace of technology development and adoption may translate into new business opportunities for lawyers, especially for those who invest time to learning about AI technologies. After all, as in any area of law, understanding the challenges facing clients is essential for developing appropriate legal strategies, as well as for targeting business development resources.

“2. AI is a disruptive force today, not tomorrow – and business must adapt”

Adapt or be left behind is a cautionary tale, but one with plenty of evidence demonstrating that it holds true in many situations.

Lawyers and law firms as an institution are generally slow to change, often because things that disrupt the status quo are viewed through a cautionary lens. This is not surprising, given that a lawyer’s work often involves thoughtful spotting of potential risks, and finding ways to address those risks. A fast-changing business landscape racing to keep up with the latest in AI technologies may be seen as inherently risky, especially in the absence of targeted laws and regulations providing guidance, as is the case today in the AI industry. Even so, exploring how to adapt one’s law practice to a world filled with AI technologies should be near the top of every lawyer’s list of things to consider for 2018.