Technocultural Pluralism

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A "Clash of Civilizations" in Technology?
Preamble
At the end of the Cold War, the renowned political scientist, Samuel Huntington, argued that future conflicts were more likely to stem from cultural frictions– ideologies, social norms, and political systems– rather than political or economic frictions.1 Huntington focused his concern on the future of geopolitics in a rapidly shrinking world. But his argument applies as forcefully (if not more) to the interaction of technocultures.
By technocultures, I mean the stitched global patchwork of interacting technological ecosystems we currently live in. For an intuitive illustration of these distinct ecosystems, observe variations in these popular platform choices circa 2017 (Figure 1.). Given the proliferation of global tech platforms (e.g. Facebook, WhatsApp, LINE, etc.), these variations can give noisy hints about where technocultural fault-lines lie. I argue that ecosystems are characterized not just by local consensus or concordance in tech adoption, but also in culture, policies, tech innovation, and deployment priorities.
Figure 1: Mapping Out Dominant Social Media Platform Popularity Across the Globe as of 2017. Interestingly, observed platform-choice clusterings or signatures align quite closely to the cultural fault-lines Huntington outlined almost 30 years ago. We can roughly make out Western-Europe-and-USA-and-Australia, China, Eastern Europe, Japan, and Islamic-Hindu spheres of influence. (Data Courtesy of GlobalWebIndex.net, Cluster Analysis Courtesy of Joshua S. Mendelsohn)
The main hypothesis is two-fold.

[Technocultural Frictions]: an AI "technocultural cold war" is more likely if not already in progress. This refers to a state of ongoing regulatory friction among multiple technocultures or governance regimes, forced to interact because effective geographic proximity, political necessity, and/or economic advantage. The focus here is on competitive or adversarial frictions.2 Put differently, technocultural friction refers to friction due to the necessary interaction between technology policy spheres of influence.3
[Technocultural Pluralism]: the prospect of a global monolithic AI technoculture emerging in the near-future is implausible. Persistent pluralism is more likely. By pluralism, I mean a persistent diversity in the global technoculture. These hypotheses are not necessarily AI-specific. But the current efflorescence of data-hungry machine learning innovation in AI heightens the salience of cultural differences.

This piece has two aims. The first task is descriptive (like most of Huntington's original 1993 discussion). I aim to describe underlying factors and dynamics that foster the development of differentiated technocultures. I build up key concepts (not least of which is a clearer depiction of technoculture). This descriptive exploration also serves persuasive function. Technocultures are easier to track once we observe how the warp and weft of technology innovation, deployment, culture-specific norms, and regulation "conspire" to differentiate our global technology environment. The second task aims to go beyond description to highlight dynamics and implications of technocultural pluralism. It is worth highlighting specifically the important implications of data privacy policies, data localization, and population size as mechanisms for differentiation in the evolution of technocultures around the world.
Part of the motivation for this discussion is to counter a specific perspective (admittedly a strawman perspective and often an inchoate one when held). This tempting perspective anticipates a future regulatory scenario featuring a monolithic global technology ecosystem with little to no geographic cultural variation. Although this is a strawman position, elements of this position arise in some technology policy conversations. What can we say about the prospects of such a monolithic technocultural world? If this homogeneous outcome is unlikely, what are the regulatory and governance implications?  Hopefully this exploration starts us off with basic tools to gain more insight into these types of questions.
Technoculture… What is that?
First there is a question of what we mean by a technoculture.
The term technoculture here refers specifically to the combination of a technology4 regime and the culture5 in which it is embedded. The concept of a technoculture forces an engagement with questions of how cultural contexts affect, influence, or determine the evolution, deployment, and adoption of technological artifacts. This will include questions about the controlling innovation culture, the prioritization of problems for technological innovation, expected modes of deployment, etc.
Is this (or any) conception of technoculture useful?
At first blush, the concept of technoculture may seem paradoxical; technology is often supposed to be this objective or value-neutral fruit of dispassionate scientific analysis and design. But even under the debatable assumption of a perfectly value-neutral design process, the choice of problems on which to apply technological innovation is subject to cultural influence. As a recent anecdotal6 illustration, take the polarized response to the demonstration of the use of machine learning models to infer criminality from face images.7 The authors (of Chinese origin) maintain that this is perfectly acceptable while many American tech commentators strenuously objected.
Even the assumption of value-neutral scientific design wilts under light scrutiny. The constraints of ML development processes mean that designers make myriad non-negotiable design choices that will affect users,8 including users with unexpected characteristics. Some of these design choices include impositions of norms and values (e.g. concerning fairness/equity, transparency). The Nymwars of 2012 gives a concrete case in point:9 social media platform designers decided to impose and enforce the norm of only allowing profiles with real names. That decision stood in opposition to prior established norms of online pseudonymy in certain communities.
It is now less controversial to assert that technology is inherently cultural given these observations. Technological artifacts are not free of cultural or ethical values (implicit or explicit).10 Cultural values infuse the innovation, design, and use of technology. Winner11 recounts numerous examples of conscious and unconscious deployment of technology artifacts that either imposed or fostered political preferences (e.g. decisions in town-planning and bridge architecture in Long Island, NY explicitly designed to enforce extralegal segregationist preferences).
This is especially true of modern AI. Modern AI depends primarily on data. Data ecosystems are comprehensive records of cultural values and norms – neutral, good, or bad. Current conversations about data-diet vulnerabilities in AI and biases in algorithms highlights this point more emphatically.12 Modern data-driven ML systems learn patterns (e.g. language behaviors and biases) present in their training data.
Furthermore, the contours of existing and future data ecosystems are strongly determined by operating data privacy regulations. Questions of privacy are (at least) as cultural as they are technological. On the cultural dimension, cross-national survey studies of attitudes towards privacy and cultural influences on privacy show significant relationships between privacy behaviors and quantified cultural factors13 especially pragmatism, individualism, and country.14 These relationships are found to hold even after controlling for population experience with/exposure to technology.
Besides the cultural dimension, both privacy enhancing technologies and privacy policies15 determine how much and what kinds of data are available to train AI systems. Privacy enhancing technologies (PETs) highlight the outer physical limits of privacy preservation.16 Privacy policies occupy a space between cultural factors and technology. These policies allocate rights and specify incentives to govern the behavior of data sources and sinks. Cultural and consensual norms influence the overall balance of such of rights and incentives. The EU's GDPR sets a precedent asserting the rights of users as primary individual controllers of their data (control but not necessarily rights to compensation for use). Chinese governance culture includes a current precedent of asserting communal control of individual data to address public welfare (e.g. to control public information consumption or to enable public reputation scoring). And technology deployment in lower income countries (e.g. Aadhar deployment in India) has been found to be less subject to privacy concerns.17
Why Do Technocultures Matter? Is a Universal Technoculture Plausible?
Back to Samuel Huntington's post-cold-war observation and its adaptation to technocultures. If the discussion in the previous section is compelling enough, then we are led to concede the following:

AI technology (and any technology) is subject to the influence of its cultural context.
There is a global diversity of technocultural contexts — even if the geopolitical boundaries or fault-lines are fuzzily defined at best.
Cultural values inform tech evolution, tech policy, and tech regulation — especially concerning data and AI.

Interaction between technocultures is unavoidable in our rapidly shrinking world. And differences in policy and regulation can lead to friction in interaction. This leads to the aforementioned two-fold hypothesis about technocultural pluralism and frictions. The interplay of the highlighted technocultural factors hint at the idea that the global AI technology ecosystem is likely to fracture along the culture-specific lines telegraphed in these data ecosystems. And AI's intense data dependence means privacy policy18 is likely a key lever in technocultural divergence.
The technocultural friction point is somewhat supportable given:

recent discussions of "AI arms races,"
the flurry of AI strategy statements from different countries19, and
recent geopolitical squabbles over commercial data localization20 and/or foreign investment in sensitive tech sectors.

The technocultural pluralism point is harder to support fully since it is a statement about the future evolution of technocultures.21 In the context of data-driven AI tech, the cultural-specificity of available or accessible training data (either due to local norms in data behavior or due to local data privacy policies), may lead to persistent fracturing the evolution of AI tech. In the more general technology context, observable cultural differences in tech use, innovation, and regulation suggests persistent differentiation.
The pluralism hypothesis is admittedly a less-than-ironclad forecast. But it is a forecast based on the observation that we have yet to see global cultural convergence in the long (short?) history of civilization. Cultural differences (e.g. in language use) persist in spite of long interaction. A technocultural monolithic future is as unlikely as a culturally monolithic future. Sure, this is a conservative prediction. But it is likely a more reasonable one given the historical record.
Pluralism… Now what?
What are the strategic implications of these hypotheses? A persistently pluralist technocultural future raises some hard questions like: Are technocultural differences truly unresolvable in the long-term? If they are not resolvable, what are the possible equilibria in the long-run? Can a multipolar technocultural world be stable? Are technocultures inherently "winner take all"? Is there an alternative to technocultural dominance? In the short run, how do we understand the space of potential technocultural frictions and conflicts? What are the evolutionarily stable strategies22 for playing the game of technocultural thrones?
All useful questions. Probably. Rather than give definite answers to these questions,23 I instead explore a characterization of features of an inhomogeneous tech ecosystem and an examination of plausible future scenarios that arise under the pluralism hypothesis.
It is worth highlighting that pluralism is not necessarily a negative. The ability of local domains to determine local technoculture can be very empowering e.g. the ability of poorer nations to adopt technologies and deploy them to solve pressing local problems.
A Pluralist World: Useful Levers & Interesting Dynamics
It is useful to explore how the actions of aggregate agents (government, populations, commercial entities) can influence the evolution of AI technology and the global technoculture more generally. Here is a non-exhaustive exploration in broad-strokes:
Data Localization Policies
Data localization is an emerging trend in data privacy and technology regulation. Data localization refers to restrictions or prohibitions on exporting data about local citizens or data originating from local sources. Notable examples of such regulations include EU's GDPR Article #45,24 China's Cybersecurity Law Article #3725 and Russia's Federal Law no.242-FZ.26 GDPR's Article #45.2(a), for example, requires an assessment of the normative "adequacy" of foreign jurisdictions before certifying the outward transfer of EU data. Article #37 of China's Cybersecurity Law articulates similar constraints on outward data flows. Exceptions would require extensive security vetting.
There are justifiable reasons for imposing localization regulations:

[Security Constraints] Data localization can help prevent foreign intelligence breaches. Information traffic about domestic affairs flowing in foreign jurisdictions is often easier to intercept both physically and legally. Forcing local processing and storage (sometimes even transmission) reduces the risk of interception.27 Furthermore, data localization makes information relevant to domestic security and safety more readily accessible within the jurisdiction. Technocultures as different in values as the EU and China both agree on the occasional need to breach privacy in pursuit of security or safety.
[Normative Constraints] Data localization helps preserve the contextual integrity of citizen's data. Privacy norms are value- and culture-dependent. One conception of privacy is of privacy as a form of contextual integrity.28 Under this conception, privacy preservation is tied to the (explicit or implicit) norms of the specific context and jurisdiction. Non-local data handling increases the exposure of subjects' data to inappropriate contexts with privacy norms that are insufficiently aligned with local norms. There is thus a higher risk of violating contextual integrity and/or locally-acceptable privacy norms.
[Self-Interest] Data localization helps foster the local technology ecosystem. This point is especially central for the pluralism hypothesis as related to AI. Localization will often foster the development of local technical competence with data technologies. This competence is foundational for enabling innovations in AI and developing AI solutions tailored to local problems.

The combination of these factors incentivizes the trend towards a more fractured global technoculture. Increased data localization fosters siloed technocultures.
"Attractive" Populations: Power in Numbers
Regulatory levers like data localization have the effect of placing a cognitive burden on interested multinational firms. They need some familiarity with local norms if they intend to operate profitably and legally within foreign jurisdictions.29 Ideally there is a benefit for shouldering that burden. That benefit comes from the economic power of a population-base. We can use the term "attractiveness" to refer to the influence that populations can exert on technocultures just by being sizeable sources of profit. The magnitude of a target populations's influence is somewhat proportional to its size.
Large populations attract economic attention as markets or sinks for economic goods. Jurisdictions with large population bases present a large pool of potential consumers. Firms that are able to survive regulatory and operational challenges qualify to play for larger potential (or actual) profits. In this scenario, regulatory barriers may operate as mechanisms for depriving competitors who are unwilling/unable to satisfy local norms of market share. Regulation and policy-making can thus be construed as acts of collective bargaining on behalf of a jurisdiction's population.
The past demises of Apple, Google, Uber, and Facebook operations in China are useful illustrations. Recent Apple and Google overtures to resume some operations in China also illustrate the strength of the attractiveness of that user-base.
As a lever in technocultural evolution, population size has a couple of modes of use. Countries with large populations can use their influence to extract concessions or compromises. This can be an explicit interaction e.g. China sanctioning firms that do not provide state access to collected user data.30 The opportunity cost for a multinational firm closing down operations because of some regulatory barrier is higher for larger countries than for smaller. Influence can also be exerted via implicit negotiation, e.g. the EU using the weight of its population-base to shift international data privacy discourse and practice via ambitious regulation.
Populations also attract attention as sources of technical expertise or human capital at advantageous price points. This is useful to highlight because human capital comes equipped with value systems that can sharply affect the evolution of tech innovation and deployment. The moral aversion to defense-related uses of AI recently expressed by significant portions of Silicon Valley technical work-force offers a case in point.
Winners and First-Movers
There has historically been a form of first-mover's advantage in technology innovation. Intellectual property (IP) rights actually aim to strengthen this advantage as a way of incentivizing innovation. In recent history, for instance, the USA enjoyed unparalleled technocultural dominance. Current Internet technology still bears some reminders of its US-centric early development (e.g. USA's network centrality in internet routing and other vestiges of US-led tech standards formation). The migration of talent to the USA during WW2 helped cultivate this advantage. As did the relative depression of Chinese and Russian innovation due to experiments with versions of Communism.
There is also a strong bias towards survivors of technology arms-races: a winner-take-all dynamic or close to it. As a first approximation, effective tech innovations spread and drive less effective innovations to extinction (practical performance as the fitness metric). But the memetic resonance of modern information technology platforms may not be as fully determined by practical performance e.g. the geographic differences in adoption of international platforms like facebook and vKontakte is likely not just a function of differences in technical performance. But the dynamics of network effects and preferential attachment to popular platforms leads to cumulative survival advantages that approximate winner-take-all behavior.
These trends, first-mover's advantage and winner-take-all, may mediate local economic advantages as well as a technoculture's influence on future policy. But these trends are not "unchallenged laws of nature." MySpace gave way to Facebook in spite of precedence. As did Yahoo to Google in search technology. And the fracturing of the modern social media ecosystem suggests that network effects are not irreversible.
Paths of Evolution: Local Norms with Global Reach?
There is a deliberate analogy between ecology of technocultures and the ecology of biological ecosystem. Species in an ecosystem interact (cooperatively or competitively) and evolve in response to their environmental context. Similarly, technologies, platforms, firms, governments interact and co-evolve in response their specific cultural context.
The analogy suggests a vulnerability. Geographical distance may have served as a barrier against the transmission of technocultural cultural influence in the past. But distance is no longer a strong barrier. Technocultures now evolve in a crowded international space. One technoculture might foster a specific innovation in tech use, development, or regulation. Such innovative mutations may now be more easily transmitted across technocultures. And such mutations may find stronger resonance in non-native contexts. Such cross-technocultural transmissions may be beneficial or virulent.31
Examples of beneficial cross-technocultural transmissions32 include:

the transmission of key aspects of ICT innovations (outwards from the USA)
the transmission of AI innovations (esp. facial recognition AI outwards from the USA to prominent use in China)
The transmission of sericulture [outwards from China]
In tech regulation, the spread of GDPR concepts from the EU into Californian privacy regulation

Examples of virulent cross-technocultural transmissions include:

The repurposing of social media platforms for propaganda or psychometric targeting in political elections [outward from the UK or from Eastern Europe].

We can also play with the prospect of convergent evolution in technology e.g. the convergent evolution of printing technology in the East and the West, or the convergent evolution of flight and photography. Intense global interaction may mean it becomes easier to adopt foreign innovations rather than innovate locally (thus reducing the likelihood or opportunities for convergent evolution). The key theme here is of local norms and actions having unprecedented global reach.
Innovations in AI tech also change the balance of influence in international relations. Nation-states naturally develop the abilities necessary to pursue their interest in cyberspace. It is reasonable to expect this trend to continue. But the context is slightly shifted somewhat… Now smaller anti-social non-state actors with some AI expertise have an expanded ability to project influence and hold larger actors hostage. Especially if there are no trusted referees to mediate disputes.
Again, the theme: local norms, global reach.
Conclusion: The Fruits of a Pluralist Framing
The purpose of this piece was to encourage us to take seriously the prospect of unresolvable cultural schisms in the global technology landscape. Culturally-mediated fault-lines are particularly salient when dealing with data-driven AI technologies which make-up the bulk of modern AI technology. This is because culture-dependent privacy norms circumscribe what data is available/accessible/permissible for training AI systems. The general interaction of culture and technology is what we have termed a technoculture. The point of introducing this concept is to provide a fruitful lens for examining the evolution of technology.
I have referred to the fractured state of the global technology ecosystem as Technocultural Pluralism. In a sense, this pluralist conception has been the historic norm. Our multicultural history is not a history of globally uniform patterns in tech innovation and deployment. The key assertion in this piece is that pluralism is likely a more permanent state than one might perhaps think — globalization, disruptive AI innovation, and (potentially/supposedly?) impending singularity notwithstanding. We can take language use as an informative precedent. Language is one of humanity's oldest culture-infused tech innovations.33 Yet it still retains a level of cultural specificity that is unlikely to fade away soon. Why expect anything else for AI on a shorter time-scale?
Taking pluralism seriously does not mean assuming a permanent Hobbesian state of "War of All Against All." There is certainly bound to be friction as technocultures negotiate their shared existence on a smaller global stage, under diverse, sometimes diametrically opposed value systems (technocultural clashes, to use Huntington's term). It also does not mean a constant arms-race or drive towards domination. The arms-race perspective is well-suited to discussions of defense in which the controlling objective was about survival and actions are centrally directed. In any given modern technoculture, there will be multiple preferences, utilities, or objectives in play. And the aggregate behavior of the technoculture is an impenetrable function of millions or billions of sub-agents' choices.
Taking pluralism seriously means spending more time exploring the features and dynamics of our global technocultural ecosystem. This piece represents one such exploration.
What strategic implications does a technoculturally pluralist framing highlight? One key implication would be the pivotal role of data localization and privacy policies in deepening schisms between technocultures in the age of AI since localization undermines uniformity in what data exists or is accessible in different jurisdictions for training local AI/ML solutions. There is a more positive take on this implication: data localization and local privacy policies can help foster more culturally-sensitive local deployment and innovation of AI technologies.
Questions remain. For example: What are the merits of a technocultural equivalent of the "Contact Hypothesis"? i.e. does more contact between technocultures lead to better long-term accommodation? Or to heated frictions and virulent cross-infections? What mechanisms are effective for improving the health and resistance of domestic technocultures from negative foreign infections? What are effective strategies and compromises in a technoculturally pluralist world?
Whatever insights remain, they will require a deeper engagement with the cultural foundations of our technologies and a clearer-eyed examination of the values/assumptions embedded within our technologies.
Acknowledgements
This discussion is a side-effect of numerous conversations. I am particularly grateful to Kathryn "Casey" Bouskill for many insightful discussions on the nuances of culture.
References
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By Osonde Osoba, Professor, Pardee RAND Graduate School, RAND CorporationThe post Technocultural Pluralism first appeared on AI Pulse.

Technocultural Pluralism


One Shot Learning in AI Innovation

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Natalie Ram1
Modern algorithmic design far exceeds the limits of human cognition in many ways. Armed with large data sets, programmers promise that their algorithms can better predict which prisoners are most likely to recidivate2and where future crimes are likely to occur.3 Software designers further hope to use large data sets to uncover relationships between genes and disease that would take human researchers much longer to identify.4
But modern machine learning still cannot effectively match human cognition in at least one crucial respect: learning from small data sets. Young children, for example, master new concepts with startling rapidity and fluency. "[G]iven 2 or 3 images of an animal you have never seen before, you can usually recognize it reliably later on."5 Similarly, "a person only needs to see one Segway to acquire the concept and be able to discriminate future Segways from other vehicles like scooters and unicycles."6 And as any parent of a toddler can attest, "children can acquire a new word from one encounter."7
Psychologists believe that, by the time we reach six years of age, "we recognize more than 104 categories of objects."8 By contrast, traditional algorithmic design typically requires many more training examples. "[L]earning one object category requires a batch process involving thousands or tens of thousands of training examples."9 Researchers describe the human method of learning new categories, and objects within categories, from one or a handful of examples "one shot learning."10
In a relatively recent body of work, researchers are beginning to take aim at cracking this insight of human learning—and teaching algorithmic systems to learn in the same way. This work is still developing,11 with early examples demonstrating that one shot learning algorithms can correctly categorize, for example, human faces, motorbikes, airplanes, and spotted cats on par with big data algorithms while using only fifteen training examples.12 While differentiating between such wildly disparate categories may seem a long ways away from facial recognition or other big data applications, research into such uses is already underway. Using a variety of algorithmic approaches, researchers have already demonstrated that one shot learning may enhance algorithmic applications in fields including facial recognition,13 hand writing identification,14 and shoe tread analysis.15
Yet, to date, legal academics have overlooked these efforts. Indeed, the phrase "one shot learning" does not appear in any law review article searchable in Westlaw.16 This article seeks to remedy that gap in the literature, introducing the concept and language of one shot learning, and warning that enabling computer systems to successfully perform one shot learning is likely to exacerbate problems of insufficient transparency and of hidden bias that already beset the use of algorithmic systems, particularly in the criminal justice context.
Introducing One Shot Learning
One shot learning builds on, and learns from, traditional big data algorithmic design. In traditional big data algorithms, developers train a machine learning system to recognize and accurately distinguish a particular category by feeding the system thousands, and often tens of thousands, of examples of the relevant category.17 These training data often derive from records of past human conduct or from data coded by humans, which makes their development "a tedious and expensive task."18 Moreover, new categories must be learned afresh, with many thousands of training examples for each new category.19
One shot learning, by contrast, attempts to replicate the human ability to apply past knowledge to new learning.20 As one set of authors has explained, "[t]he key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be."21 That is, once a machine learning system has learned a few categories "the hard way"—based on thousands of training examples—some "general knowledge" can be extracted and applied to new, previously unknown categories.22 Thus, one shot learning algorithms are designed to make inferential leaps and to extract knowledge learned about one category to aid in identification of future categories.23 In this way, one shot learning models aim to encode algorithmic systems with the power to learn how to learn.
Transparency and Understanding in One Shot Learning
The promise of one shot learning to enable algorithmic systems to learn new categories more cheaply and efficiently than in the past is enormous; but there is also significant risk that developments in one shot learning will exacerbate some of the most persistent difficulties in AI markets, including transparency.
Transparency—and the related problem of understanding—is a challenge for machine learning models in at least two ways. First, the nature of much machine learning is opaque.24It may be formally opaque in instances where it is "actually impossible to state how the algorithm classifies observations once it has been developed."25 In other instances, an algorithmic pattern will be functionally opaque, as where algorithmically identified relationships are "so complicated that they defy explicit understanding."26 In both senses, machine-learning models act as a "black box,"27in which known data goes in, answers are produced, but the process by which data is transformed into answers is unknown and potentially unknowable. Under such circumstances, effective transparency and understanding may be difficult to achieve.
Second, these difficulties of transparency and understanding are exacerbated by the outsized role that trade secrecy occupies for complex algorithmic systems. In recent years, the Supreme Court has reinforced that mathematical processes are patent-ineligible "abstract ideas," at least insofar as those ideas are not inventively applied in some real-world application.28Machine learning models, as fundamentally mathematical processes, typically are excluded from patent protection.29 In the absence of such protection, trade secrecy has become a primary method for maintaining competitive advantage.30Unfortunately, trade secret protection, by definition, depends on continued secrecy; public disclosure destroys it.31
Reliance on trade secrecy, and opacity about how a particular machine-learning model reaches decisions, is likely to pervade one shot learning to an even greater extent than traditional big data algorithms. Because one shot learning extrapolates the skill of "learning how to learn" from underlying categories that may themselves teach unexplainable relationships, it builds opacity upon opacity.32 Moreover, one shot learning is likely to multiply the sources of information about an algorithmic system that must be known to replicate or understand its workings, making trade secrecy a more powerful tool for competitive advantage and a greater foil to transparency and understanding. Some algorithmic systems are explainable upon examination of source code.33 But because machine learning models are often a "black box," examining source code may provide insufficient insight into their validity or reliability. Instead, adequate understanding of the algorithmic system may depend on having access to both source code and training data.34Accordingly, when algorithmic developers have invoked trade secrecy to withhold training data, as well as information about algorithmic design, the non-disclosure of either renders obscure the system as a whole.35
Creators of one shot learning models, in turn, may well be able to stymie transparency and understanding even if they disclose both source code and the limited training examples used for new learning categories. Again, because these models depend on prior learning in the traditional "big data" way, the absence of the more remote training data that armed a one shot learning model with its "general knowledge" may make understanding that model difficult, if not impossible. By proliferating the sources of information that may be withheld to maintain competitive advantage, one shot learning may pose a greater threat to transparency and understanding than even traditional "black box" big data models.
These failures of disclosure and transparency are likely to impose significant practical barriers to developing sufficiently accurate and reliable algorithms.36Secret code is often less good code, as secrecy may obstruct effective oversight of the reliability and validity of algorithmic tools.37 This difficulty is particularly likely to arise in a burgeoning field like one shot learning algorithms, where different researchers have adopted different approaches to solving the one shot learning problem.38In other contexts in which programmers use somewhat different mathematical models (or code for the same model differently), the consequence is that, in attempting to do the same thing, these competing tools sometimes yield different results from identical inputs.39
Moreover, "black box" algorithms are likely to be particularly problematic in some of the settings in which one shot learning algorithms are most desired, including law enforcement investigations. As described above, researchers are already working to develop effective one shot learning algorithms to perform facial recognition,40hand writing identification,41and shoe tread analysis.42 While these types of analysis may be useful in multiple contexts, they are likely to be of particular interest to law enforcement. Indeed, one article exploring the implementation of one shot learning methods for shoe tread analysis focuses explicitly on the forensic use of such algorithms, explaining, "We investigate the problem of automatically determining what type (brand/model/size) of shoe left an impression found at a crime scene."43
The use of undisclosed algorithmic models in the criminal justice setting, in turn, is frequently problematic. In addition to practical concerns about the impact of trade secrecy on algorithmic design, secrecy and opacity surrounding criminal justice algorithms can raise significant constitutional concerns.44 I have argued elsewhere that secret criminal justice algorithms are "at least in tension with, if not in violation of, defendants' ability to vindicate their due process interests throughout the criminal justice process, as well as their confrontation rights at trial."45 As one shot learning algorithms perform more complex inferential tasks, access to algorithm design information is likely to be even more critical in the criminal justice field to ensure that the design accurately yields reliable results and functions as intended. Trade secrecy threatens to undermine those goals.
Exacerbating Bias in One Shot Learning
In addition to intensifying reliance on trade secrecy, one shot learning in algorithmic design may also multiply the ways in which algorithmic design encodes bias—and not in a good way. In traditional big data algorithms, because training data often derives from records of past human conduct or from data coded by humans, bias in this human conduct can give rise to biased outputs from the algorithm.46"[T]raining data is often gathered from people who manually inspect thousands of examples and tag each instance according to its category. The algorithm learns how to classify based on the definitions and criteria humans used to produce the training data, potentially introducing human bias into the classifier."47
In the context of one shot learning, if categories learned the "hard way" are tainted with bias, this may similarly infect new categories learned by inference. Indeed, any such bias may be amplified where there are fewer, rather than more, training examples for a new category. Where only a few examples of a new category are available, more inferential leaps in learning are required, and so bias in human selection or coding of examples is likely to be aggravated.
More troubling, in attempting to create more "human" learning, programmers designing one shot learning algorithms may replicate crucial faults in human learning and decision making. Human learning achieves rapid categorization and decision making in part through reliance on cognitive short cuts. These short cuts—called heuristics—operate as "principles by which [human beings] reduce the complex tasks of assessing likelihoods and predicting values to simpler judgmental operations."48Heuristics, in other words, help human beings make inferential leaps from incomplete data. As Tversky and Kahneman have noted, "[i]n general, these heuristics are quite useful, but sometimes they lead to severe and systematic errors."
Among the most significant heuristics of human decision making is the representativeness heuristic. Humans intuitively call on this heuristic when tasked with assessing: "What is the probability that an object A belongs to a class B? What is the probability that event A originates from process B? What is the probability that process A will generate an event B?" Under the representativeness heuristic, these "probabilities are evaluated by the degree to which A is representative of B, i.e.. by the degree of similarity between them." The more similar A is to B, the higher the probability that A belongs to (or originates from) (or will generate) B. Typically, representativeness is a useful heuristic, and its probabilities are usually accurate enough for everyday living.49
But representativeness can also lead decision makers astray. For instance, in assessing whether Mr. X holds a particular occupation, decision makers assess "the similarity of Mr. X to the stereotype of each occupational role, and orders the occupations by the degree to which Mr. X is representative of these stereotypes." (Similarly, in assessing the likelihood that Mr. X will be a repeat criminal offender, decision makers assess how alike Mr. X is to the stereotype of a repeat criminal offender.) Yet stereotypes are, by definition, inexact. Moreover, these determinations of probability are frequently immune to crucial factors like, for instance, the base-rate of each occupation in the general population. Moreover, individuals express confidence in their predictions in accordance with the degree of similarity between Mr. X and their stereotype of a particular profession, "with little or no regard for the factors that limit predictive accuracy."50
One shot learning algorithms appear to attempt to instruct machine systems to make the same sorts of inferential leaps and extrapolation from prior information that give rise to heuristics like representativeness in humans. These algorithms seek to make computer systems more human-like in their capacities for data analysis and recognition. But an unintended consequence of accomplishing this result may be to inflict on computer systems the same kinds of heuristics that render human decision making irrational in systematic ways. If successful, one shot learning may prove to be faster than human judgment, but perhaps not better than it.
Conclusion: Mapping the Solution Space
One shot learning advances machine learning by enabling sophisticated models to learn new categories from only a few examples. So long as the model has gained prior exposure to extensive training data about some categories, a one shot learning model can learn how to learn. That is exciting, as it opens the door to making greater use of more diverse data for machine learning.
But one shot learning also threatens to worsen already persistent problems in machine learning, including the opacity of machine learning models, their reliance on trade secrecy, and the bias they may unwittingly encode. These problems are not straightforward to solve. Moreover, these problems are likely to fester together, in that non-transparent systems shielded from effective external validation are less likely to recognize and correct for their unintended biases.
But trade secrecy, and the understanding and fairness it may threaten, need not dominate innovation in one shot learning algorithms. Alternative mechanisms for innovation policy abound, including prizes, grants, regulatory exclusivities, and tax incentives.51 These tools of innovation policy can help to support the research, development, and sale of effective one shot learning algorithms in place of (or alongside) trade secrecy. In particular, at this early stage in the development of one shot learning algorithms, grants and research-based tax incentives may be well suited to driving the disclosure of early-stage work related to both algorithmic design and training data—and thus to driving a swifter pace of innovation in the field more broadly. Grants and tax incentives effectively infuse investment dollars in research up front, rather than rewarding the successful completion of a commercial product.52 In so doing, these innovation policy levers "may enable more and smaller companies to enter the market," as they reduce "the private capital investments required for innovation."53
Moreover, policymakers may also drive development of valid and reliable one shot learning models by investing in complementary incentives for innovation. In discussing innovation incentives for developing valid and reliable black box algorithms in the health care context, Nicholson Price has suggested that "direct or indirect government intervention could usefully aid the generation of datasets," to be used as common infrastructure for algorithmic development.54 Such a solution to problems of data secrecy and fragmentation is particularly well suited to one shot learning in the criminal justice context, as government will often have a monopoly on the data necessary for training these algorithms at the outset.55 After all, government actors are responsible for the investigation, arrest, prosecution, and incarceration of criminal defendants in the United States, and so have unique access to data about these populations.56 Similarly, government is well positioned to incentivize innovative methods for validating black box algorithms through prizes or grants for outside validation of complex algorithmic models, including those involving one shot learning.57
Complex algorithmic systems, including those deploying one shot learning, hold enormous promise for expanding the range of data from which an algorithmic system can learn and the range of categories it can learn to identify. But this promise is not unfettered. If these complex models are to be deployed, particularly in the criminal justice context, relevant stakeholders—including policymakers, courts, prosecutors, and defense counsel alike—must grasp the ways in which machine learning in general, and one shot learning in particular, may undermine as well as enhance the pursuit of justice and take steps to mitigate those harms.

By Natalie Ram, Assistant Professor, University of Baltimore School of Law

The post One Shot Learning in AI Innovation first appeared on AI Pulse.

One Shot Learning in AI Innovation


"Soft Law" Governance of Artificial Intelligence

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Gary Marchant
Center for Law, Science & Innovation, Arizona State University
Introduction
On November 26, 2017, Elon Musk tweeted: "Got to regulate AI/robotics like we do food, drugs, aircraft & cars.  Public risks require public oversight.  Getting rid of the FAA wdn't [sic] make flying safer. They're there for good reason."1
In this and other recent pronouncements, Musk is calling for artificial intelligence (AI) to be regulated by traditional regulation, just as we regulate foods, drugs, aircraft and cars.  Putting aside the quibble that food, drugs, aircraft and cars are each regulated very differently, these calls for regulation seem to envision one or more federal regulatory agencies adopting binding regulations to ensure the safety of AI.  Musk is not alone in calling for "regulation" of AI, and some serious AI scholars and policymakers have likewise called for regulation of AI using traditional governmental regulatory approaches.2
But these calls for regulation raise the questions of what aspects of AI should be regulated, how they should be regulated, and by who?  The reality is that at best there will be some sporadic piecemeal traditional regulation of AI over the next few years, notwithstanding the increasing deployment and application of AI in a growing range of applications and industry sectors.  In the interim at least, this "governance gap" for AI will mostly be filled by so-called "soft law" (see Part I, supra).  These "soft law" mechanisms include various types of instruments that set forth substantive expectations but are not directly enforceable by government, and include approaches such as professional guidelines, private standards, codes of conduct, and best practices.  A number of such soft law approaches have already been proposed or are being implemented for AI (see Part II, supra).  While soft law has some serious deficiencies, such as lack of enforceability, there are additional strategies that can help maximize the effectiveness of this second-best approach to governance (see Part III, supra).  For example, the lack of enforceability problem can be solved at least in part by various types of indirect enforcement by entities such as insurance companies, journal publishers, grant funders, and even governmental enforcement programs against unfair or deceptive business practices.  Another problem, the lack of coordination between a potentially large number of overlapping and perhaps even inconsistent soft law programs, is to create what has been described as a Governance Coordinating Committee to help serve a coordinating function.
The Unsuitability of Traditional Regulation for AI
While some piecemeal regulation of specific AI applications and risks using traditional regulatory approaches may be feasible and even called for, AI has many of the characteristics of other emerging technologies that make them refractory to comprehensive regulatory solutions.3  For example, AI involves applications that cross multiple industries, government agency jurisdictions, and stakeholder groups, making a coordinated regulatory response difficulty.  In addition, AI raises a wide range of issues and concerns that go beyond traditional regulatory agency focus on health, safety and environmental risks.  Indeed, many risks created by AI are not within any existing regulatory agency's jurisdiction, including concerns such as technological unemployment, human-machine relationships, biased algorithms, and existential risks from future super-intelligence.
Moreover, the pace of development of AI far exceeds the capability of any traditional regulatory system to keep up, a challenge known as the "pacing problem" that affects many emerging technologies.4 The risks, benefits and trajectories of AI are all highly uncertain, again making traditional preemptory regulatory decision-making difficult.  And finally, national governments are reluctant to impede innovation in an emerging technology by preemptory regulation in an era of intense international competition.
For these reasons, it is safe to say there will be no comprehensive traditional regulation of AI for some time, except perhaps if some disaster occurs that triggers a drastic and no doubt poorly-matched regulatory response.  Again, there may be slivers of the overall AI enterprise that are amenable to traditional regulatory responses, and these should certainly be pursued.  But these isolated regulatory advances will be insufficient alone to deal with the safety, ethical, military, and existential risks posed by AI.  Something more will be needed.
That something more that will be needed to fill the governance gap for AI will, at least in the short term, be within the category of "soft law."  Soft law are instruments that set substantive expectations that are not directly enforceable by government.  They can include private standards, voluntary programs, professional guidelines, codes of conduct, best practices, principles, public-private partnerships and certification programs.  Soft law can even include what Wendell Wallach and I refer to as "process soft law" approaches such as coding machine ethics into AI systems or creating oversight systems within a corporate Board of Directors.5  These types of measures are inherently imperfect, precisely because they are not directly enforceable.
This core weakness results in many other limitations, such that participation is incomplete, with the "good guys" complying and the "bad guys" not.  These soft law measures are sometimes used as "whitewashing" (or "greenwashing") to make it look like a problem is being addressed when it really is not.  And soft law measures are often expressed in vague, general language that is hard to measure compliance with.  Finally, soft law measures generally do not provide the same reassurance to the public as traditional government regulation that the problems presented by a new technology are being adequately managed.  This public reassurance effect is an important secondary function of regulation.
Notwithstanding these significant limitations, soft law has become a necessary and inevitable component of the governance framework for virtually all emerging technologies, including AI.  Traditional regulatory systems cannot cope with the rapid pace, diverse applications, heterogeneous risks and concerns, and inherent uncertainties of emerging technologies.  So although soft law measures are a second best solution, they are often the only game in town, at least initially.  It recalls the quote attributed to Winston Churchill that "democracy is the worst form of government, except for all the others."6
Soft law has important advantages that explain its growing popularity and gap filling role.  Soft law instruments can be adopted and revised relatively quickly, without having to go through the traditional bureaucratic rulemaking process of government.  It is possible to experiment with several different soft law approaches simultaneously, indeed sometimes creating a problem of a proliferation of inconsistent private standards and other soft law instruments.  They can sometimes create a cooperative rather than adversarial relationship among stakeholders.  They are not bound by limited agency delegations of authority, and so can address any and all concerns raised by a technology.  And because they are not adopted by a formal legal authority, they are not restricted to a specific legal jurisdiction, but can have international application.
Existing AI Soft Law Examples
We are already seeing the rapid infusion of soft law initiatives and proposals into the AI governance space.7 Indeed, the likely first ever governance proposal for AI (at that time focused on robotics) was Isaac Asimov's three laws of robotics first published in 1942.8  These "laws" were actually a form of soft law as they had no formal legal authority.  More recently, an early entry into the AI soft law landscape was a "robot ethics charter" that the government of South Korea initiated in 2007, even though no final version of the ethics charter has ever been posted online.
Institute of Electric and Electronic Engineers (IEEE)
Perhaps the most comprehensive soft law initiative for AI was launched in 2016 by the IEEE, one of the world's largest standard-setting and professional engineering societies.9  This initiative, entitled "The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, is intended to "ensure every stakeholder involved in the design and development of autonomous and intelligent systems is educated, trained, and empowered to prioritize ethical considerations so that these technologies are advanced for the benefit of humanity."10  The Initiative has two intended outputs.  The first is a guide known as Ethically Aligned Design, which has now been published as draft versions I and II for public comments.  Version II is a document that exceeds 250 pages and that addresses over 120 policy, legal and ethical issues associated with AI, with recommendations assembled from more than 250 expert participants.11 It seeks to "advance a public discussion about how we can establish ethical and social implementations for intelligent and autonomous systems and technologies, aligning them to defined values and ethical principles that prioritize human well-being in a given cultural context, inspire the creation of Standards (IEEE P7000 series and beyond) and associated certification programs, [and] facilitate the emergence of national and global policies that align with these principles."12  The final version of Ethically Aligned Design is scheduled to be published in 2019.
The second and even more relevant activity by the Initiative is to produce a series of IEEE standards addressing governance and ethical aspects of AI.  The IEEE has given official approval to create the following standards, with standard-setting committees now established to develop each standard:
IEEE P7000 – Model Process for Addressing Ethical Concerns During System Design
IEEE P7001 – Transparency of Autonomous Systems
IEEE P7002 – Data Privacy Process
IEEE P7003 – Algorithmic Bias Considerations
IEEE P7004 – Standard on Child and Student Data Governance
IEEE P7005 – Standard for Transparent Employer Data Governance
IEEE P7006 – Standard for Personal Data Artificial Intelligence (AI) Agent
IEEE P7007 – Ontological Standard for Ethically Driven Robotics and Automation         Systems
IEEE P7008 – Standard for Ethically Driven Nudging for Robotic, Intelligent, and Automation Systems
IEEE P7009 – Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems
IEEE P7010 – Wellbeing Metrics Standard for Ethical Artificial Intelligence and Autonomous Systems
IEEE P7011TM – Standard for the Process of Identifying and Rating the Trustworthiness of News Sources
IEEE P7012TM – Standard for Machine Readable Personal Privacy Terms
IEEE P7013TM – Inclusion and Application Standards for Automated Facial Analysis Technology
These fourteen AI standards are scheduled to be finalized by the end of 2021, and will provide a broad set of governance requirements relating to the governance of AI.  For example, the chair of the working group developing standard IEEE P7006 on personal AI agents has recently written that the standard is being developed to provide "a principled and ethical basis for the development of a personal AI agent that will enable trusted access to personal data and increased human agency, as well as to articulate how data, access and permission can be granted to government, commercial or other actors and allow for technical flexibility, transparency and informed consensus for individuals."13
Partnership on AI
Another significant "soft law" player in the AI field is the Partnership on AI.  This Partnership was originally started by the big players in the AI space such as Google, Microsoft, Facebook, IBM, Apple and Amazon, but has expanded to include a wide variety of companies, think tanks, academic AI organizations, professional societies, and charitable groups such as the ACLU, Amnesty International, UNICEF and Human Rights Watch.14 One of the stated goals of the Partnership is to develop and share best practices for AI which includes: "Support research, discussions, identification, sharing, and recommendation of best practices in the research, development, testing, and fielding of AI technologies.  Address such areas as fairness and inclusivity, explanation and transparency, security and privacy, values and ethics, collaboration between people and AI systems, interoperability of systems, and of the trustworthiness, reliability, containment, safety, and robustness of the technology."15
The Partnership on AI has published a set of "Tenets" that include:
"We are committed to open research and dialogue on the ethical, social, economic, and legal implications of AI….
We believe that AI research and development efforts need to be actively engaged with and accountable to a broad range of stakeholders….
We will work to maximize the benefits and address the potential challenges of AI technologies, by: Working to protect the privacy and security of individuals….Working to ensure that AI research and engineering communities remain socially responsible, sensitive, and engaged directly with the potential influences of AI technologies on wider society….Ensuring that AI research and technology is robust, reliable, trustworthy, and operates within secure constraints….Opposing development and use of AI technologies that would violate international conventions or human rights, and promoting safeguards and technologies that do no harm.
We believe that it is important for the operation of AI systems to be understandable and interpretable by people, for purposes of explaining the technology.16
It remains to be seen if and how the Partnership will advance beyond these general tenets to produce more specific best practices and guidelines for responsible AI research and applications.
Future of Life Institute
The Future of Life Institute convened a meeting of many leading AI practitioners and experts at the Asilomar conference center in 2017, which is the home of the famous Asilomar Conference on Recombinant DNA held in 1975 which pioneered the soft law governance of technology by agreeing on a set of voluntary guidelines for genetic engineering research.  At the 2017 Asilomar conference, the participants agreed on 23 principles to guide AI research and applications.17  These principles include "Failure Transparency" ("If an AI system causes harm, it should be possible to ascertain why."); "Responsibility" ("Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.") and "Value Alignment" ("Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.").18
Industry groups have adopted their own soft law instruments for AI.  For example, the Information Technology Industry Council (ITI) has developed its own set of AI principles.19  For example, these principles include a commitment to "recognize our responsibility to integrate principles into the design of AI technologies, beyond compliance with existing laws…. As an industry, it is our responsibility to recognize potentials for use and misuse, the implications of such actions, and the responsibility and opportunity to take steps to avoid the reasonably predictable misuse of this technology by committing to ethics by design."20  The statement of principles, itself a form of soft law governance, also states a commitment to soft law principles: "We promote the development of global voluntary, industry-led, consensus-based standards and best practices.  We encourage international collaboration in such activities to help accelerate adoption, promote competition, and enable the cost-effective introduction of AI technologies."21
Company-Specific Soft Law Initiatives
Some individual companies have also adopted their own statement of principles or guidelines for AI.  For example, in June 2018 Google's CEO Sundar Pichai announced a set of seven principles that Google will follow in its AI activities.22  Other major AI companies such as Microsoft23 and IBM24 have also announced their own AI principles that will guide their conduct.
Governmental AI Soft Law Initiatives
Governments have also supported the use of soft law methods to govern AI.  The EU Commission published its strategy paper on AI on April 25, 2018.25  Contrary to what many members of the European Parliament had hoped for and requested,26 the Commission did not propose any new regulatory measures for AI at this time.  Rather, it committed to develop a set of draft guidelines by the end of 2018.27 In December 2018, the Commission published a "Coordinated  Action Plan on AI" that set forth the Commission's objectives and plans for an EU-wide strategy on AI.28  However, the Commission did note that "[w]hile self-regulation can provide a first set of benchmarks against which emerging applications and outcomes can be assessed, public authorities must ensure that the regulatory frameworks for developing and using of AI technologies are in line with these values and fundamental rights.  The Commission will monitor developments and, if necessary, review existing legal frameworks to better adapt them to specific challenges, in particular to ensure the respect of the Union's basic values and fundamental rights."29
Similarly, the UK House of Lords issued a detailed report on AI earlier in April 2018 and likewise recommended an ethical code of conduct for AI rather than any traditional "hard" regulation.30 The report cited testimony on "the possible detrimental effect of premature regulation" such as that "the pace of change in technology means that overly prescriptive or specific legislation struggles to keep pace and can almost be out of date by time it is enacted" and that lessons from regulating previous technologies suggested that a "strict and detailed legal requirements approach is unhelpful".31  Based on such testimony, the House of Lords therefore concluded that "[b]lanket AI-specific regulation, at this stage, would be inappropriate."32
Instead, the House of Lords recommended a soft law strategy at least in the interim: "We recommend that a cross-sector ethical code of conduct, or 'AI code', suitable for implementation across public and private sector organisations which are developing or adopting AI, be drawn up and promoted … with a degree of urgency…. Such a code should include the need to have considered the establishment of ethical advisory boards in companies or organisations which are developing, or using, AI in their work. In time, the AI code could provide the basis for statutory regulation, if and when this is determined to be necessary."33
Evaluation and Moving Forward
A variety of entities from the government, industry and the non-government sectors have proposed or adopted soft law initiatives for the governance of AI.  These soft law instruments include private standards, best practices, codes of conduct, principles and voluntary guidelines.  They are in various states of development and implementation, and individually and collectively provide some initial guidance for the governance of AI.  However, they suffer from major limitations.  One prevalent problem is the generality of most of the provisions in these instruments.  To some degree, this vagueness is inevitable and necessary, given the broad range of AI applications and the rapid pace and uncertain trajectory of its future, making precise requirements difficult if not impossible.  Indeed, this is the very reason why the technology is primarily being governed by soft law rather than traditional hard law approaches at this time.
Two other limitations of the current matrix of soft law programs are however more amenable to progress and improvement.  First, the unenforceability of these soft law provisions is the Achilles' heel of soft law approaches generally.  There is no assurance or requirement that all, or even any, AI developers and users comply with the soft law recommendations.  However, there are a number of mechanisms that can be used to indirectly enforce these soft law provisions.  Any entity with a supervisory role can adopt and monitor compliance with one or more AI soft law programs.  For example, a corporation could create a committee of its Directors or a free-standing ethics committee and task it with ensuring compliance with the appropriate guidelines or codes of conduct adopted by or agreed to by that company.  Universities could use the existing chain of authority, such as through department heads and deans, to require compliance with specified soft law AI provisions as part of the annual evaluation of faculty and staff.  Or universities could create new, or expand the jurisdiction of existing, research oversight committees such as the Institutional Biosafety Committee to ensure adherence with specified AI soft law provisions.
Other actors could also play an important role in indirect enforcement of AI soft law programs.  Certification bodies could create certification programs to certify that a company or other entity is adhering to a particular set of guidelines or principles.  Business partners could require certification with applicable AI soft law programs as a condition of doing business with that company.  Insurers could require the implementation of appropriate AI risk management programs as a condition of liability coverage, just as some did with nanotechnology.34 Granting agencies could condition funding on compliance with specified AI guidelines or codes of conduct.  Professional journals could require compliance with certain best practices or guidelines as a condition of publication.
Even more legal quasi-enforcement approaches could be pursued.  The Federal Trade Commission (FTC), under its general authority to take enforcement actions against deceptive and unfair business practices, could take enforcement action against a company that publicly commits to comply with a certain code of conduct or best practices but then fails to live up to its commitment.  Private standards, especially those adopted by well-known standard setting bodies such as the IEEE, could be used to set a standard of care in tort law, and a company's failure to adhere to such standards, even though they are voluntary, could be evidence of failure to use reasonable care in a product liability or personal injury lawsuit.35
Soft law measures result in experience and field testing that can provide learning for subsequent traditional regulation.  Indeed, soft law can sometimes be seen as a transitionary phase of governance that gradually "hardens" into traditional government regulation.36 We may already be starting to see this hardening process of soft law in the AI space – for example, the State of California recently adopted legislation "expressing support" for the Asilomar AI Principles.37
Second, the confusing proliferation of different AI soft law programs and proposals creates confusion and overlap with regard to AI governance.  It is hard for an actor in the AI space to assess and comply with all these different soft law requirements.  Where do these various soft law programs overlap and duplicate each other?  Where do they contradict each other?  What gaps are not addressed by any of the existing soft law proposals?  Some type of coordination is needed.
Wendell Wallach and I have proposed such a coordinating entity, which we have called a Governance Coordinating Committee (GCC).38  This entity would not seek to duplicate or supplant the many organizations working on developing governance approaches to AI, but rather would provide a coordinating function much like an orchestra conductor in ensuring all the various players were connected with each other and aware of and responsive to each other's proposals, while also identifying gaps and inconsistencies in existing programs.  In a forthcoming publication, we describe the functions of the GCC to include the following coordination functions:

Information clearinghouse, by collecting and reporting in one place all significant programs, proposals, ideas or initiatives for governing AI;
Monitoring and Analysis, such as identifying gaps, overlaps, and inconsistencies with respect to existing and proposed governance programs;
Early Warning System, by noting emerging issues or problems that are not addressed or covered by existing governance programs;
Evaluation Program, which scores various governance programs and efforts for their metrics and compliance with stated goals.
Stakeholder Forum, by providing a space for stake-holders to meet and discuss governance ideas and issues and to produce recommendations, reports, and roadmaps;
Credible Intermediary, serving as a trusted "go-to" source for the media, the public, scholars and stakeholders to obtain information about AI and its governance;
Convener for Solutions, by convening  interested stakeholders on specific issues to meet and try to forge a negotiated partnership program for addressing unaddressed problems or governance needs.39

There are many unanswered questions about how a GCC would function.  Who would fund it?  Who would be its employees and how would they be selected?  What would be its administrative structure?  What would be its precise functions and charter?  How would stakeholders interact with the GCC?  How would the GCC achieve and maintain its credibility as an "honest broker"? Initiatives are currently underway to explore such questions in the context of planning an international conference to  discuss and possibly create  a global GCC for AI governance.
Conclusion
Soft law measures are very imperfect governance tools because of their lack of enforceability and accountability, as well as often being written in very general and self-serving language.  Yet, for a rapidly developing and expansive technology like AI, comprehensive regulation by governments is not feasible, at least in the short term with at best piecemeal regulatory enactments possible.  Accordingly, soft law will be the default approach for most AI governance at the present time.  For that reason, there is a need to explore ways to indirectly enforce and coordinate the proliferation of soft law measures that have already been proposed or enacted for AI.
Gary Marchant, Regent's Professor of Law and Director of the Center for Law, Science and Innovation, Arizona State University Law SchoolThe post "Soft Law" Governance of Artificial Intelligence first appeared on AI Pulse.

“Soft Law” Governance of Artificial Intelligence