Collaborating Authors


Artificial Intelligence Activity On The Enforcement Front - Technology - Canada


Artificial Intelligence ("AI") is clearly on the horizon of the regulatory landscape. Alongside the use of technology to assist with navigating the regulatory process, regulators are now digitizing their enforcement efforts. The Canadian Securities Administrators ("CSA")1 have approached this challenge head-on. In 2018, the CSA put the capital markets on notice that they were strengthening their technological capabilities to assist in fighting securities misconduct.2 The CSA confirmed they would rely on AI technology to analyze large data sets, allowing them to detect misconduct faster and earlier, through the Market Analysis Platform ("MAP"), an automated centralized solution that the CSA believed could handle the size of the current market practices.

To Build Less-Biased AI, Hire a More-Diverse Team


We've seen no shortage of scandals when it comes to AI. In 2016, Microsoft Tay, an AI bot built to learn in real time from social media content turned into a misogynist, racist troll within 24 hours of launch. A ProPublica report claimed that an algorithm -- built by a private contractor -- was more likely to rate black parole candidates as higher risk. A landmark U.S. government study reported that more than 200 facial recognition algorithms -- comprising a majority in the industry -- had a harder time distinguishing non-white faces. The bias in our human-built AI likely owes something to the lack of diversity in the humans who built them.

ServiceNow to Acquire Element AI - ServiceNow Press


SANTA CLARA, CALIF., Nov. 30, 2020 – ServiceNow (NYSE: NOW) today announced it has signed an agreement to acquire Element AI, a leading artificial intelligence (AI) company with deep AI capabilities and some of the world's brightest AI minds. Element AI will significantly enhance ServiceNow's commitment to build the world's most intelligent workflow platform, enabling employees to work smarter and faster, streamline business decisions, and unlock new levels of productivity. A pioneer in the AI industry, Element AI has world‑class scientists and practitioners who will bring expertise in applying modern AI to text and language, chat, images, search, question response, and summarization and will accelerate AI innovation natively in the Now Platform. Element AI Co‑founder and Lead Fellow, Dr. Yoshua Bengio, a winner of the 2018 ACM A.M. Turing Award for his pioneering contributions to modern AI, will serve as a technical advisor for ServiceNow. With the acquisition of Element AI, ServiceNow will create an AI Innovation Hub in Canada to accelerate customer‑focused AI innovation in the Now Platform. The new investment deepens ServiceNow's commitment to the Canadian market, which has long been a leader in AI research and represents one of the world's most significant locations for AI talent.

If You Aren't Using AI, You're Falling Behind According To The U.S. Patent And Trademark Office


In a new report released on October 27 by the United States Patent and Trademark Office (USPTO), more than 42% of all technology areas in 2018 incorporate Artificial Intelligence (AI) in their new inventions. The majority of these improvements come in knowledge processing and planning/control, which involve analyzing information to gain new insights and using those insights to manage a business process. CIOs continue to talk about how vital AI technologies are, but this new report confirms that if companies aren't already putting that talk into action, they are behind the curve. The danger of falling behind is even greater for companies that haven't started adoption since the statistics only cover till the end of 2018. In the last 18 months, the percentage of technologies that include AI has undoubtedly continued to increase. The report also confirms an increased interest by the office in this technology and a higher willingness to consider new applications that include them.

Facebook Will Pay $650 Million to Illinois Residents - Legal Reader


Facebook allegedly violated Illinois state law by using consumers' facial features to improve its photo-tagging software. Nearly one and a half million Illinois residents have filed claims to part of a $650 million privacy settlement offered by Facebook. According to NBC Chicago, the law firm responsible for the social media lawsuit said that 1.42 million Illinois residents have already filed claims. Eligible claimants could receive awards ranging between $200 and $400. The lawsuit, says NBC, alleged that Facebook broke Illinois' "strict biometric privacy law."

"The Suicide of Our Troubles"


Please come out, we'd all love to see you.--Andrea Boyczuk She hadn't driven on 75 since before Christmas. There were lots of cars and self-driving trucks on the road, and in MR the sky had sprouted thousands of virtual signs, labels, and guides. It seemed a lot was going on. Eventually the silence made her edgy and she said, "So you're Lake Erie. How long have you been awake?" "I've been a legal person since 2017." The lake had a smooth, masculine voice, with none of the artificiality she'd heard in Mercury's on those occasions when she'd spoken to it directly and not through Donna. "I was made one so that the citizens of Ohio could litigate on my behalf. But I have a lot more resources since I have the actants' network attached to me."

UN warns law enforcement against using 'big data' to discriminate


Police and border guards must combat racial profiling and ensure that their use of "big data" collected via artificial intelligence does not reinforce biases against minorities, United Nations experts said on Thursday. Companies that sell algorithmic profiling systems to public entities and private companies, often used in screening job applicants, must be regulated to prevent misuse of personal data that perpetuates prejudices, they said. "It's a rapidly developing technological means used by law enforcement to determine, using big data, who is likely to do what. And that's the danger of it," Verene Shepherd, a member of the UN Committee on the Elimination of Racial Discrimination, told Reuters. "We've heard about companies using these algorithmic methods to discriminate on the basis of skin colour," she added, speaking from Jamaica.

As Cities Curb Surveillance, Baltimore Police Took to the Air


In August 2016, a Bloomberg report revealed a secret aerial surveillance program in Baltimore led by the city's police department. Over eight months, planes equipped with cameras collected over 300 hours of footage, used by the police to investigate alleged crimes. Hardly anyone outside police department leadership and the vendor, Persistent Surveillance Systems, knew. Baltimore's police commissioner at the time, Kevin Davis, defended both the planes and the secrecy. The city's murder rate was spiking, the stretched police department was responding to thousands of calls per day, and footage from the planes was helping police find suspects.

An eye on better AI: what important steps we must take today for a brighter digital future


See also our related columns The Turning Point, Techie Tuesdays, and Storybites. Though artificial intelligence (AI) may not surpass human intelligence for at least a few more decades, it opens up opportunities and challenges that we must address today in order to shape a better world for us all. A call to action for business leaders, entrepreneurs, academics, and policymakers is effectively made in Toby Walsh's new book, 2062: The World that AI Made. The rise of AI poses serious philosophical, economic and social questions for all of us, and more vision and collaboration are urgently called for. How many jobs will AI take away or create?

Protecting consumers from collusive prices due to AI


The efficacy of a market system is rooted in competition. In striving to attract customers, firms are led to charge lower prices and deliver better products and services. Nothing more fundamentally undermines this process than collusion, when firms agree not to compete with one another and consequently consumers are harmed by higher prices. Collusion is generally condemned by economists and policy-makers and is unlawful in almost all countries. But the increasing delegation of price-setting to algorithms ([ 1 ][1]) has the potential for opening a back door through which firms could collude lawfully ([ 2 ][2]). Such algorithmic collusion can occur when artificial intelligence (AI) algorithms learn to adopt collusive pricing rules without human intervention, oversight, or even knowledge. This possibility poses a challenge for policy. To meet this challenge, we propose a direction for policy change and call for computer scientists, economists, and legal scholars to act in concert to operationalize the proposed change. Collusion among humans typically involves three stages (see the table). First, firms' employees with price-setting authority communicate with the intent of agreeing on a collusive rule of conduct. This rule encompasses a higher price and an arrangement to incentivize firms to comply with that higher price rather than undercut it in order to pick up more market share. For example, in 1995 the CEOs of Christie's and Sotheby's hatched their plans in a limo at Kennedy International Airport, and in 1994 the U.S. Federal Bureau of Investigation secretly taped the lysine cartel as they conspired in a Maui hotel room. At those meetings, they spoke about charging higher prices and how to enforce them. Second, successful communication results in the mutual adoption of a collusive rule of conduct, which commonly takes the form of a collusive pricing rule. A crucial component of this pricing rule is retaliatory pricing: Each firm raises its price and maintains that higher price under the threat of a “punishment,” such as a temporary price war, should it cheat and deviate from the higher price ([ 3 ][3]). It is this threat that sustains higher prices than would arise under competition. Third, firms set the higher prices that are the consequence of having adopted those collusive pricing rules. ![Figure][4] The process that produces higher prices To determine whether firms are colluding, one could look for evidence at any of the three stages. However, evidence related to the last two stages—pricing rules and higher prices—is generally regarded as insufficient to achieve the requisite level of confidence in the judicial realm. Economists know how to calculate competitive prices given demand, costs, and other relevant market conditions. But many of these factors are difficult to observe and, when observable, are challenging to measure with precision. Consequently, courts do not use the competitive price level as a benchmark to identify collusion. Likewise, it is difficult to assess whether the firms' rules of conduct are collusive because such rules are latent, residing in employees' heads. In practice, we may never observe the retaliatory lower prices from a firm that cheated, even though that response is there in the minds of the employees and it is the anticipation of such a response that sustains higher prices. In other words, we might lack the events that produce the data that could identify the collusive pricing rules. Furthermore, even if one could observe what looks like a price war, it would be difficult to rule out innocent explanations (such as a decrease in the firms' costs or a fall in demand). Given the latency of collusive pricing rules and the difficulty of determining whether prices are collusive or competitive, antitrust law and its enforcement have focused on the first stage: communications. Firms are found to be in violation of the law when communications (perhaps supplemented by other evidence) are sufficient to establish that firms have a “meeting of minds,” a “concurrence of wills,” or a “conscious commitment” that they will not compete ([ 4 ][5]). In the United States, more specifically, there must be evidence that one firm invited a competitor to collude and that the competitor accepted that invitation. The risk of false positives (i.e., wrongly finding firms guilty of collusion) has led courts to avoid basing their judgments on evidence of collusive pricing rules or collusive prices and instead to rely on evidence of communications. Although the use of pricing algorithms has a long history—airline companies, for instance, have been using revenue management software for decades—concerns regarding algorithmic collusion have only recently arisen for two reasons. First, pricing algorithms had once been based on pricing rules set by programmers but now often rely on AI systems that learn autonomously through active experimentation. After the programmer has set a goal, such as profit maximization, algorithms are capable of autonomously learning rules of conduct that achieve the goal, possibly with no human intervention. The enhanced sophistication of learning algorithms makes it more likely that AI systems will discover profit-enhancing collusive pricing rules, just as they have succeeded in discovering winning strategies in complex board games such as chess and Go ([ 5 ][6]). Second, a feature of online markets is that competitors' prices are available to a firm in real time. Such information is essential to the operation of collusive pricing rules. In order for firms to settle on some common higher price, firms' prices must be observed frequently enough because sustaining those higher prices requires the prospect of punishing a firm that deviates from the collusive agreement. The more quickly the punishment is meted out, the less temptation to cheat. Thus, the emergence and persistence of higher prices through collusion is facilitated by rapid detection of competitors' prices, which is now often possible in online markets. For example, the prices of products listed on Amazon may change several times per day but can be monitored with practically no delay. In light of these developments, concerns regarding the possibility of algorithmic collusion have been raised by government authorities, including the U.S. Federal Trade Commission (FTC) ([ 6 ][7]) and the European Commission ([ 7 ][8]). These concerns are justified, as enough evidence has accumulated that autonomous algorithmic collusion is a real risk. The evidence is both experimental and empirical. On the experimental side, recent research has found the spontaneous emergence of collusion in computer-simulated markets. In these studies, commonly used reinforcement-learning algorithms learned to initiate and sustain collusion in the context of well-accepted economic models of an industry ([ 8 ][9], [ 9 ][10]) (see the figure). Collusion arose with no human intervention other than instructing the AI-enabled learning algorithm to maximize profit (i.e., algorithms were not programmed to collude). Although the extent to which prices were higher in such virtual markets varied, prices were almost always substantially above the competitive level. On the empirical side, a recent study ([ 10 ][11]) has provided possible evidence of algorithmic collusion in Germany's retail gasoline markets. The delegation of pricing to algorithms was found to be associated with a substantial 20 to 30% increase in the markup of stations' prices over cost. Although the evidence is indirect—because the authors of the study could not directly observe the timing of adoption of the pricing algorithms and thus had to infer it from other data—their findings are consistent with the results of computer-simulated market experiments. Algorithmic collusion is as bad as human collusion. Consumers are harmed by the higher prices, irrespective of how firms arrive at charging these prices. However, should algorithmic collusion emerge in a market and be discovered, society lacks an effective defense to stop it. This is because algorithmic collusion does not involve the communications that have been the route to proving unlawful collusion (as distinguished from instances in which firms' employees might communicate and then collude with the assistance of algorithms, as in a recent case involving poster sellers on Amazon Marketplace). And even if alternative evidentiary approaches were to arise, there is no liability unless courts are prepared to conclude that AI has a “mind” or a “will” or is “conscious,” for otherwise there can be no “meeting of minds” with algorithmic collusion. As a result, if algorithmic collusion occurs and is discovered by the authorities, currently it cannot be considered a violation of antitrust or competition law. Society would then have no recourse and consumers would be forced to continue to suffer the harm from algorithmic collusion's higher prices. ![Figure][4] Collusive pricing rules uncovered After the two algorithms have found their way to collusive prices (“learning phase,” left side), an attempt to cheat so as to gain market share is simulated by exogenously forcing Firm 1's algorithm to cut its price (“punishment phase,” right side). From the “shock” period onward, the algorithm regains control of the pricing. Firm 1's deviation is punished by the other algorithm, so firms enter into a price war that lasts for several periods and then gradually ends as the algorithms return to pricing at a collusive level. For better graphical representation, the time scales on the right and left sides of the figure are different. GRAPHIC: N. CARY/ SCIENCE FROM CALVANO ET AL. ([ 8 ][9]) There is an alternative path, which is to target the collusive pricing rules learned by the algorithms that result in higher prices ([ 11 ][12]). These latent rules of conduct may be uncovered when they have been adopted by algorithms. Whereas a court cannot get inside the head of an employee to determine why prices are what they are, firms' pricing algorithms can be audited and tested in controlled environments. One can then simulate all sorts of possible deviations from existing prices and observe the algorithms' reaction in the absence of any confounding factor. In principle, the latent pricing rules can thus be identified precisely. This approach was successfully used by researchers in ([ 8 ][9]) to verify that the pricing algorithms have indeed learned the collusive property of reward (keeping prices high unless a price cut occurs) and punishment (through retaliatory price wars should a price cut occur). To show this, the researchers momentarily overrode the pricing algorithm of one firm, forcing it to set a lower price. As soon as the algorithms regained control of the pricing, they engaged in a temporary price war, where lower prices were charged but then gradually returned to the collusive level. Having learned that undercutting the other firm's price brings forth a price war (with the associated lower profits), the algorithms evolved to maintain high prices (see the figure). It may seem paradoxical that collusion can be identified by the low retaliatory prices, which could be close to the competitive level, rather than by the high prices that are the ultimate concern for policy. But there are two important differences between retaliatory price wars and healthy competition. First, in the absence of the low-price perturbation, the price war remains hypothetical in that it is a threat that is not executed. Second, the price war shown in the figure is only temporary: Instead of permanently reverting to the competitive price level, the algorithms gradually return to the pre-shock prices. This is evidence that the price war is there to support high prices, not to produce low prices. Focusing on the collusive pricing rules is the key to identifying, preventing, and prosecuting algorithmic collusion (see the table). Policy cannot target the higher prices directly, nor can it target communications as they may not be present (unlike with human collusion). But the retaliatory pricing rules may now be observable, as firms' pricing algorithms can be audited and tested. We therefore propose that antitrust policy shift its focus from communications (with humans) to rules of conduct (with algorithms). Making the proposed change operational involves a broad research program that requires the combined efforts of economists, computer scientists, and legal scholars. One strand of this program is a three-step experimental procedure. The first step creates collusion in the lab for descriptively realistic models of markets. As the competitive price would be known by the experimenter, collusion is identified by high prices. Having identified an episode of collusion, the second step is to perform a post hoc auditing exercise to uncover the properties of the collusive pricing rules that produced those high prices. Some progress has been made on the identification of collusive rules of conduct adopted by algorithms, but much more work needs to be done. Economics provides several properties to watch out for. Of course, there is the retaliatory price war discussed above, which is what existing research has focused on (8, 9). Another property is price matching, whereby firms' prices move in sync: one firm changing its price and the other firm subsequently matching that change. Price matching has been documented for human collusion in various markets, but we do not yet know whether algorithms are capable of learning it. A third property is the asymmetry of price responses. When firms collude, they typically respond to a competitor's price cut more strongly—as part of a punishment—than to a price increase. No such asymmetry is to be expected when firms compete. The aforementioned properties are based on economic theory and studies of human collusion. Learning algorithms may devise rules of conduct that neither economists nor managers have imagined ( just as learning algorithms have done, for instance, in chess). To investigate this possibility, computer scientists might develop algorithms that explain their own behavior, thereby making the collusive properties more apparent. One way of doing so is to add a second module to the reinforcement-learning module that maximizes profits; this second module maps the state representation of the first one onto a verbal explanation of its strategy ([ 12 ][13]). Having uncovered collusive pricing rules, the third step is to experiment with constraining the learning algorithm to prevent it from evolving to collusion. Computer scientists are particularly valuable here, given that they are involved in similar tasks such as trying to constrain algorithms so that, for instance, they do not exhibit racial and gender bias ([ 13 ][14]). Once the capacities to audit pricing algorithms for collusive properties and to constrain learning algorithms so that they do not adopt collusive pricing rules have been developed, legal scholars are called upon to use that knowledge for purposes of prosecution and prevention. One route is to make certain pricing algorithms unlawful, perhaps under Section 5 of the FTC Act, which prohibits unfair methods of competition. In the area of securities law, the 2017 case U.S. v. Michael Coscia made illegal the use of certain programmed trading rules and thus provides a legal precedent for prohibiting algorithms. Another path is to make firms legally responsible for the pricing rules that their learning algorithms adopt ([ 14 ][15]). Firms may then be incentivized to prevent collusion by routinely monitoring the output of their learning algorithms. These are some of the avenues that can be pursued for preventing and shutting down algorithmic collusion. There are several obstacles down the road, including the difficulty of making a collusive property test operational, the lack of transparency and interpretability of algorithms, and courts' willingness and ability to incorporate technical material of this nature. In addition, there is the challenge of addressing algorithmic collusion without giving up the efficiency gains from pricing algorithms such as the quicker response to changing market conditions. As authorities prepare to take action ([ 15 ][16]), it is vital that computer scientists, economists, and legal scholars work together to protect consumers from the potential harm of higher prices. 1. [↵][17]1. A. Ezrachi, 2. M. Stucke , Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy (Harvard Univ. Press, 2016). 2. [↵][18]1. S. Mehra , Minn. Law Rev. 100, 1323 (2016). [OpenUrl][19] 3. [↵][20]1. J. Harrington , The Theory of Collusion and Competition Policy (MIT Press, 2017). 4. [↵][21]1. L. Kaplow , Competition Policy and Price Fixing (Princeton Univ. Press, 2013). 5. [↵][22]1. D. Silver et al ., Science 362, 1140 (2018). [OpenUrl][23][Abstract/FREE Full Text][24] 6. [↵][25]“The Competition and Consumer Protection Issues of Algorithms, Artificial Intelligence, and Predictive Analytics,” Hearing on Competition and Consumer Protection in the 21st Century, U.S. Federal Trade Commission, 13–14 November 2018; [][26]. 7. [↵][27]“Algorithms and Collusion—Note from the European Union,” OECD Roundtable, June 2017; [][28]. 8. [↵][29]1. E. Calvano, 2. G. Calzolari, 3. V. Denicolo, 4. S. Pastorello , Am. Econ. Rev. 110, 3267 (2020). [OpenUrl][30] 9. [↵][31]1. T. Klein , “Autonomous Algorithmic Collusion: Q-Learning Under Sequential Pricing,” Amsterdam Law School Research Paper 2018-15 (2019). 10. [↵][32]1. S. Assad, 2. R. Clark, 3. D. Ershov, 4. L. Xu , “Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market,” CESifo Working Paper No. 8521 (2020). 11. [↵][33]1. J. Harrington , J. Compet. Law Econ. 14, 331 (2018). [OpenUrl][34] 12. [↵][35]1. Z. C. Lipton , ACM Queue 16, 30 (2018). [OpenUrl][36] 13. [↵][37]1. P. S. Thomas et al ., Science 366, 999 (2019). [OpenUrl][38][Abstract/FREE Full Text][39] 14. [↵][40]1. S. Chopra, 2. L. White , A Legal Theory for Autonomous Artificial Agents (Univ. of Michigan Press, 2011). 15. [↵][41]European Commission, document Ares(2020)2877634. Acknowledgments: The paper benefited from detailed and insightful comments by three anonymous reviewers. All authors contributed equally. The authors declare no competing interests. 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