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.

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.

How Privacy Law’s Beginnings May Suggest An Approach For Regulating Artificial Intelligence

A survey conducted in April 2017 by Morning Consult suggests most Americans are in favor of regulating artificial intelligence technologies. Of 2,200 American adults surveyed, 71% said they strongly or somewhat agreed that there should be national regulation of AI, while only 14% strongly or somewhat disagreed (15% did not express a view).

Technology and business leaders speaking out on whether to regulate AI fall into one of two camps: those who generally favor an ex post, case-by-case, common law approach, and those who prefer establishing a statutory and regulatory framework that, ex ante, sets forth clear do’s and don’ts and penalties for violations. (If you’re interested in learning about the challenges of ex post and ex ante approaches to regulation, check out Matt Scherer’s excellent article, “Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies,” published in the Harvard Journal of Law and Technology (2016)).

Advocates for a proactive regulatory approach caution that the alternative is fraught with predictable danger. Elon Musk for one, notes that, “[b]y the time we’re reactive in A.I., regulation’s too late.” Others, including leaders of some of the biggest AI technology companies in the industry, backed by lobbying organizations like the Information Technology Industry Council (ITI), feel that the hype surrounding AI does not justify quick Congressional action at this time.

Musk criticized this wait-and-see approach. “Normally, the way regulation’s set up,” he said, “a whole bunch of bad things happen, there’s a public outcry, and then after many years, a regulatory agency is set up to regulate that industry. There’s a bunch of opposition from companies who don’t like being told what to do by regulators, and it takes forever. That in the past has been bad but not something which represented a fundamental risk to the existence of civilization.”

Assuming AI regulation is inevitable, how should regulators (and legislators) approach such a formidable task? After all, AI technologies come in many forms, and their uses extend across multiple industries, including some already burdened with regulation. The history of privacy law may provide the answer.

Without question, privacy concerns, and privacy laws, touch on AI technology use and development. That’s because so much of today’s human-machine interactions involving AI are powered by user-provided or user-mined data. Search histories, images people appear in on social media, purchasing habits, home ownership details, political affiliations, and many other data points are well-known to marketers and others whose products and services rely on characterizing potential customers using, for example, machine learning algorithms, convolutional neural networks, and other AI tools. In the field of affective computing, human-robot and human-chatbot interactions are driven by a person’s voice, facial features, heart rate, and other physiological features, which are the percepts that the AI system collects, processes, stores, and uses when deciding actions to take, such as responding to user queries.

Privacy laws evolved from a period during late nineteenth century America when journalists were unrestrained in publishing sensational pieces for newspapers or magazines, basically the “fake news” of the time. This Yellow Journalism, as it was called, prompted legal scholars to express a view that people had a “right to be let alone,” setting in motion the development of a new body of law involving privacy. The key to regulating AI, as it was in the development of regulations governing privacy, may be the recognition of a specific personal right that is, or is expected to be, infringed by AI systems.

In the case of privacy, attorneys Samuel Warren and Louis Brandeis (later, Justice Brandeis) were the first to articulate a personal privacy right. In The Right of Privacy, published in the Harvard Law Review in 1890, Warren and Brandeis observed that “the press is overstepping in every direction the obvious bounds of propriety and of decency. Gossip…has become a trade.” They contended that “for years there has been a feeling that the law must afford some remedy for the unauthorized circulation of portraits of private persons.” They argued that a right of privacy was entitled to recognition because “in every [] case the individual is entitled to decide whether that which is his shall be given to the public.” A violation of the person’s right of privacy, they wrote, should be actionable.

Soon after, courts began recognizing the right of privacy in civil cases. By 1960, in his seminal review article entitled Privacy (48 Cal.L.Rev 383), William Prosser wrote, “In one form or another,” the right of privacy “was declared to exist by the overwhelming majority of the American courts.” That led to uniform standards. Some states enacted limited or sweeping state-specific statutes, replacing the common law with statutory provisions and penalties. Federal appeals courts weighed in when conflicts between state law arose. This slow progression from initial recognition of a personal privacy right in 1890, to today’s modern statutes and expansive development of common law, won’t appeal to those pushing for regulation of AI now.

Even so, the process has to begin somewhere, and it could very well start with an assessment of the personal rights that should be recognized arising from interactions with or the use of AI technologies. Already, personal rights recognized by courts and embodied in statutes apply to AI technologies. But there is one personal right, potentially unique to AI technologies, that has been suggested: the right to know why (or how) an AI technology took a particular action (or made a decision) affecting a person.

Take, for example, an adverse credit decision by a bank that relies on machine learning algorithms to decide whether a customer should be given credit. Should that customer have the right to know why (or how) the system made the credit-worthiness decision? FastCompany writer Cliff Kuang explored this proposition in his recent article, “Can A.I. Be Taught to Explain Itself?” published in the New York Times (November 21, 2017).

If AI could explain itself, the banking customer might want to ask it what kind of training data was used and whether the data was biased, or whether there was an errant line of python coding to blame, or whether the AI gave the appropriate weight to the customer’s credit history. Given the nature of AI technologies, some of these questions, and even more general ones, may only be answered by opening the AI black box. But even then it may be impossible to pinpoint how the AI technology made its decision. In Europe, “tell me why/how” regulations are expected to become effective in May 2018. As I will discuss in a future post, many practical obstacles face those wishing to build a statute or regulatory framework around the right of consumers to demand from businesses that their AI explain why it made or took a particular adverse action.

Regulation of AI will likely happen. In fact, we are already seeing the beginning of direct legislative/regulatory efforts aimed at the autonomous driving industry. Whether interest in expanding those efforts to other AI technologies grows or lags may depend at least in part on whether people believe they have personal rights at stake in AI, and whether those rights are being protected by current laws and regulations.