AI’s Problems Attract More Congressional Attention

As contentious political issues continue to distract Congress before the November midterm elections, federal legislative proposals aimed at governing artificial intelligence (AI) have largely stalled in the Senate and House.  Since December 2017, nine AI-focused bills, such as the AI Reporting Act of 2018 (AIR Act) and the AI in Government Act of 2018, have been waiting for congressional committee attention.  Even so, there has been a noticeable uptick in the number of individual federal lawmakers looking at AI’s problems, a sign that the pendulum may be swinging in the direction favoring regulation of AI technologies.

Those lawmakers taking a serious look at AI recently include Mark Warner (D-VA) and Kamala Harris (D-CA) in the Senate, and Will Hurd (R-TX) and Robin Kelly (D-IL) in the House.  Along with others in Congress, they are meeting with AI experts, issuing new policy proposals, publishing reports, and pressing federal officials for information about how government agencies are addressing AI problems, especially in hot topic areas like AI model bias, privacy, and malicious uses of AI.

Sen. Warner, for example, the Senate Intelligence Committee Vice Chairman, is examining how AI technologies power disinformation.  In a draft white paper first obtained by Axios, Warner’s “Potential Policy Proposals for Regulation of Social Media and Technology Firms” raises concerns about machine learning and data collection, mentioning “deep fake” disinformation tools as one example.  Deep fakes are neural network models that can take images and video of people containing one type of content and superimpose them over different images and videos of other (or the same) people in a way that changes the original’s content and meaning.  To the viewer, the altered images and videos look like the real thing, and many who view them may be fooled into accepting the false content’s message as truth.

Warner’s “suite of options” for regulating AI include one that would require platforms to provide notice when users engage with AI-based digital conversational assistants (chatbots) or visit a website the publishes content provided by content-amplification algorithms like those used during the 2016 elections.  Another Warner proposal includes modifying the Communications Decency Act’s safe harbor provisions that currently protects social media platforms who publish offending third-party content, including the aforementioned deep fakes.  This proposal would allow private rights of action against platforms who fail to take steps, after notice from victims, that prevent offending content from reappearing on their sites.

Another proposal would require certain platforms to make their customer’s activity data (sufficiently anonymized) available to public interest researchers as a way to generate insight from the data that could “inform actions by regulators and Congress.”  An area of concern is the commercial use, by private tech companies, of their user’s behavior-based data (online habits) without using proper research controls.  The suggestion is that public interest researchers would evaluate a platform’s behavioral data in a way that is not driven by an underlying for-profit business model.

Warner’s privacy-centered proposals include granting the Federal Trade Commission with rulemaking authority, adopting GDPR-like regulations recently implemented across the European Union states, and setting mandatory standards for algorithmic transparency (auditability and fairness).

Repeating a theme in Warner’s white paper, Representatives Hurd and Kelly conclude that, even if AI technologies are immature, they have the potential to disrupt every sector of society in both anticipated and unanticipated ways.  In their “Rise of the Machines: Artificial Intelligence and its Growing Impact on U.S. Policy” report, the co-chairs of the House Oversight and Government Reform Committee make several observations and recommendations, including the need for political leadership from both Congress and the White House to achieve US global dominance in AI, the need for increased federal spending on AI research and development, means to address algorithmic accountability and transparency to remove bias in AI models, and examining whether existing regulations can address public safety and consumer risks from AI.  The challenges facing society, the lawmakers found, include the potential for job loss due to automation, privacy, model bias, and malicious use of AI technologies.

Separately, Representatives Adam Schiff (D-CA), Stephanie Murphy (D-FL), and Carlos Curbelo (R-FL), in a September 13, 2018, letter to the Director of National Intelligence, are requesting the Director of National Intelligence provide Congress with a report on the spread of deep fakes (aka “hyper-realistic digital forgeries”), which they contend are allowing “malicious actors” to create depictions of individuals doing or saying things they never did, without those individuals’ consent or knowledge.  They want the intelligence agency’s report to touch on everything from assessing how foreign governments could use the technology to harm US national interests, what sort of counter-measures could be deployed to detect and deter actors from disseminating deep fakes, and if the agency needs additional legal authority to combat the problem.

In a September 17, 2018, letter to the Equal Employment Opportunity Commission, Senators Harris, Patty Murray (D-WA), and Elizabeth Warren (D-MA) ask the EEOC Director to address the potentially discriminatory impacts of facial analysis technologies in the enforcement of workplace anti-discrimination laws.  As reported on this website and elsewhere, machine learning models behind facial recognition may perform poorly if they have been trained on data that is unrepresentative of data that the model sees in the wild.  For example, if training data for a facial recognition model contains primarily white male faces, the model may perform well when it sees new white male faces, but may perform poorly when it sees non-white male faces.  The Senators want to know if such technologies amplify bias in race, gender, disadvantaged, and vulnerable groups, and they have tasked the EEOC with developing guidelines for employers concerning fair uses of facial analysis technologies in the workplace.

Also on September 17, 2018, Senators Harris, Richard Blumenthal (D-CT), Cory Booker (D-NJ), and Ron Wyden (D-OR), sent a similar letter to the Federal Trade Commission, expressing concerns that the bias in facial analysis technologies could be considered unfair or deceptive practices under the Federal Trade Commission Act.  Stating that “we cannot wait any longer to have a serious conversation about how we can create sound policy to address these concerns,” the Senators urge the FTC to commit to developing a set of best practices for the lawful, fair, and transparent use of facial analysis.

Senators Harris and Booker, joined by Senator Cedric Richmond (D-LA), also sent a letter on September 17, 2018, to FBI Director Christopher Wray asking for the status of the FBI’s response to a 2016 General Accounting Office (GAO) comprehensive report detailing the FBI’s use of face recognition technology.

The increasing attention directed toward AI by individual federal lawmakers in 2018 may merely reflect the politics of the moment rather than signal a momentum shift toward substantive federal command and control-style regulations.  But as more states join those states that have begun enacting, in the absence of federal rules, their own laws addressing AI technology use cases, federal action may inevitably follow, especially if more reports of malicious uses of AI, like election disinformation, reach more receptive ears in Congress.

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.

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.