The Azerbaijan defense ministry has released videos it claims to show drone attacks on the Armenian military in the Nagorno-Karabakh region earlier this month. The videos of the drone strikes have been posted on the Azerbaijan's defense ministry website and social media every day. Since September, Azerbaijan has deployed several different types of missile-firing drones in the conflict with Armenia. Missile-firing drones are now produced in many countries and have been used in battles including a U.S. drone strike that killed Iran's top general Qassem Soleimani at Baghdad airport last January. Following the September 11 terrorist attacks, unmanned combat weapons of various types have been increasingly used by the U.S. military in its war on terror.

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; [www.ftc.gov/news-events/events-calendar/ftc-hearing-7-competition-consumer-protection-21st-century][26]. 7. [↵][27]“Algorithms and Collusion—Note from the European Union,” OECD Roundtable, June 2017; [www.oecd.org/competition/algorithms-and-collusion.htm][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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SCI COMMUN### Infectious diseases The 11th Ebola outbreak in the Democratic Republic of the Congo (DRC) is officially over, giving the country respite from the disease for the first time in more than 2 years. On 18 November, the World Health Organization (WHO) announced that no new cases had been identified for 42 days, twice the incubation period for the deadly virus. The outbreak, in the western Équateur province, started in late May, just as a bigger one in the eastern DRC was coming to an end. (That outbreak had killed 2200 people.) The Équateur outbreak sickened 130 and killed 55; a campaign that vaccinated more than 40,000 people is credited with helping end it. Special portable coolers that keep the vaccine at −80°C for up to 1 week allowed health workers to administer the shots in communities deep in the rainforest, accessible only by boat or helicopter. The same technology will be useful in efforts to distribute COVID-19 vaccines in Africa, says Matshidiso Moeti, WHO's regional director. The coronavirus pandemic complicated the fight against Ebola, WHO says, but the expertise gained by local health workers in earlier outbreaks in the region was a major advantage. They will remain on the lookout for potential flare-ups. $1,000,000 —Gift from entertainer Dolly Parton in April to support development of Moderna's coronavirus vaccine, which the company last week said showed an efficacy of 94.5%. “I felt so proud to have been part of that little seed money,” Parton told BBC. ### Marine ecology The Allen Coral Atlas, a project to map the world's shallow coral reefs with high-resolution satellites, last week launched a monitoring system to detect coral bleaching events as they occur. When corals face extreme heat, they expel their algal symbionts, leaving them bone white and vulnerable to stress; repeated bleaching episodes, growing more common with global warming, can cause massive die-offs. The system detects the whitening using imagery from the privately owned Planet satellite constellation, processed with machine learning. A pilot has begun in Hawaii to use the data as an early warning system for researchers, to help them identify and study species both vulnerable and resistant to warming extremes. The monitoring of bleaching is expected to expand next year to shallow reefs globally. ### Diagnostics The U.S. Food and Drug Administration (FDA) issued its first emergency use authorization last week for an at-home diagnostic test that can detect the pandemic coronavirus in just minutes. However, the test might not be widely available until spring 2021. Produced by Lucira Health, a biotech company, it is expected to cost less than $50 and require a doctor's prescription. The company says it will soon distribute tests in parts of California and Florida; it says it needs time to scale up manufacturing for national distribution. Lucira's test amplifies viral genetic material, making it nearly as accurate as laboratory tests that use the polymerase chain reaction, the current gold standard. FDA previously approved at-home tests that must be mailed to a laboratory for analysis. Several other companies are working on rapid antigen tests, which detect viral particles, for home use. But concerns remain about antigen tests' reliability. Still, some public health specialists consider widely available, low-cost, at-home testing vital for controlling the pandemic. ### Funding A new U.S. National Institutes of Health (NIH) award will allow early-career investigators who want to shift research directions when applying for their first independent award to submit a proposal without first generating preliminary data to support their idea. Reviewers will instead assess the soundness of the project's approach. The Katz award is named for Stephen Katz, a longtime champion of young researchers who was director of the National Institute of Arthritis and Musculoskeletal and Skin Diseases when he died in 2018. The grant will build on an NIH policy that prioritizes proposals from early-stage investigators—those no more than 10 years from completing their training who are applying for their first research grant. The policy has been credited with raising their numbers from fewer than 600 supported in 2013 to more than 1300 last year. Applications for the first Katz awards are due on 26 January 2021. ### Leadership Democrats in Congress say a political appointee given a senior post at the U.S. National Institute of Standards and Technology (NIST) is unfit for the job because he lacks technical skills and holds pseudoscientific views about racial differences on IQ tests. On 9 November, Jason Richwine, an independent public policy analyst, took up the new position of deputy undersecretary of commerce for standards and technology, and Commerce Secretary Wilbur Ross subsequently issued an order that would put Richwine in charge of the $1 billion research agency if NIST Director Walter Copan leaves or is fired. On 17 November, Representative Eddie Bernice Johnson (D–TX), who leads the science committee in the U.S. House of Representatives, asked Ross to justify the moves. Richwine has advocated for more restrictive immigration policies, and his 2009 doctoral thesis argued that lower IQ scores by Mexican and Hispanic immigrants suggest a genetic component to intelligence that is “likely to persist over several generations.” ### Diversity The editors of Nature Communications say they are reviewing a paper that drew scalding criticism after it suggested that encouraging female junior scientists to work with female mentors could “hinder the careers of women.” The 17 November study, led by data scientist Bedoor AlShebli of New York University, Abu Dhabi, examined 3 million mentor-protégé pairs and how gender influenced the impact of papers later published by the protégés. Female protégés, it concluded, did better if they worked with male mentors. Critics pounced, noting the authors ignored reviewer complaints about the study's methods and arguing the journal was promoting a harmful and unfounded message. The article's authors said they welcome the review. ### Animal diseases European authorities reported on 19 November they have detected highly pathogenic avian influenza in 302 birds in eight countries. Only 18 cases were in poultry; most of the rest were in wild birds, the European Food Safety Authority and its partners said. The number of infected birds is expected to rise with winter migrations. Several flu strains were identified, but no people were reported to be infected, and the risk of that occurring is considered low; researchers studying the viruses found no genetic markers indicating they had adapted to infect mammals. But the threat to poultry is high, and the report's authors recommended bird producers increase precautions against infections. VACCINE APPLICATION Days after making public the final analysis of their 40,000-person COVID-19 vaccine trial, which found 95% efficacy, Pfizer and its German partner BioNTech filed for emergency authorization of the messenger RNA vaccine from the U.S. Food and Drug Administration—the first such request for a vaccine during the pandemic. They plan to seek additional approvals in other countries soon. Pfizer hopes to supply up to 50 million doses this year. REMDESIVIR PANNED A World Health Organization panel recommended against using the antiviral drug remdesivir to treat most hospitalized COVID-19 patients. Its review of four studies of 7000 people found that the drug, which the U.S. Food and Drug Administration approved last month for hospitalized patients, did not reduce mortality or speed recovery. But the panel encouraged further study of it. AMMO BAN Denmark has become the first nation to ban all lead-based hunting ammunition, including bullets and shotgun pellets, to protect wildlife. Hunters annually release about 2 tons of lead into Denmark's environment; waterbirds and other species eat the toxic material and die. European regulators are considering a ban like Denmark's.

General Atomics Aeronautical Systems, Inc. (GA-ASI) has been awarded a contract by the U.S. Department of Defense's Joint Artificial Intelligence Center (JAIC) to develop enhanced autonomous sensing capabilities for unmanned aerial vehicles (UAVs). The JAIC Smart Sensor project aims to advance drone-based AI technology by demonstrating object recognition algorithms and employing onboard AI to automatically control UAV sensors and direct autonomous flight. GA-ASI will deploy these new capabilities on a MQ-9 Reaper UAV equipped with a variety of sensors, including GA-ASI's Reaper Defense Electronic Support System (RDESS) and Lynx Synthetic Aperture Radar (SAR). GA-ASI's Metis Intelligence, Surveillance and Reconnaissance (ISR) tasking and intelligence-sharing application, which enables operators to specify effects-based mission objectives and receive automatic notification of actionable intelligence, will be used to command the unmanned aircraft. J.R. Reid, GA-ASI Vice President of Strategic Development, commented: "GA-ASI is excited to leverage the considerable investment we have made to advance the JAIC's autonomous sensing objective. This will bring a tremendous increase in unmanned systems capabilities for applications across the full-range of military operations."

The government is exploring the use of artificial intelligence technology to detect what proportion of people using public transport are wearing a face covering. In addition to monitoring the use of masks, the tech system could also be used to display messages designed to encourage their use. According to rail minister Chris Heaton-Harris, work to trial the use of such technology is currently at the stage of being a “proof-of-concept study”. We are investigating a non-intrusive AI-model capable of detecting the number of face-coverings, and the number of uncovered faces, in an image, he said. The model would then display message responses focused on positive engagement. This work will not be able to identify or track individuals, and no images will be stored by the system. Heaton-Harris added that this project – and others exploring the use of emerging technology – are supported by specialist teams at the Department for Transport.

AutoML is poised to turn developers into data scientists -- and vice versa. Here's how AutoML will radically change data science for the better. In the coming decade, the data scientist role as we know it will look very different than it does today. But don't worry, no one is predicting lost jobs, just changed jobs. Data scientists will be fine -- according to the Bureau of Labor Statistics, the role is still projected to grow at a higher than average clip through 2029.

Like it or not, artificial intelligence (AI) is already part of our daily lives. From the smartphones in our pockets to the Alexa virtual assistants on our kitchen counters, AI and its applications are accepted norms today. While we appreciate that AI can automate repetitive workplace tasks or even drive a car, the reality is that its implications are much further reaching. Luminaries like Elon Musk and Bill Gates have spoken out about the potential downsides of AI. At times, they have even issued outright warnings.

The Select Committee on Artificial Intelligence--an interagency group of AI experts across the federal government--issued several recommendations Nov. 17 regarding how agencies can better tap cloud computing resources for research and development efforts. The report makes clear cloud computing provides "robust, agile, reliable and scalable computing capabilities" that augment existing AI technologies. However, while cloud computing is near ubiquitous in the private sector, there are still "several technical and administrative challenges" limiting cloud adoption in other arenas, including federal agencies' research and development areas. Gaining access to cloud computing varies across the federal landscape, and best practices differ depending on environments, according to the report. In addition, "limited access to education and training opportunities" for researchers themselves limit how well they make use of cloud environments. The White House established the select committee of federal AI experts in May 2018.

The Defense Department, Army and Howard University linked up to collectively push forward artificial intelligence and machine learning-rooted research, technologies and applications through a recently unveiled center of excellence. Work it will underpin will "shape the future," according to an announcement Monday from the Army Research Laboratory--and the $7.5 million center also marks a move by the Pentagon to help expand its pipeline for future personnel. "Diversity of science and diversity of the future [science and technology] talent base go hand-in-hand in this new and exciting partnership," Dr. Brian Sadler, Army senior research scientist for intelligent systems said. Tapped to manage the partnership, Sadler added that Howard University is "an intellectual center for the nation." Encompassing 13 schools and colleges, the institution is a private, historically Black research university that was founded in 1867.

On Thursday, 26 November, Prof. Andrew Murray, will deliver the Sixth T.M.C. Asser Lecture – 'Almost Human: Law and Human Agency in the Time of Artificial Intelligence'. Asser Institute researcher Dr. Dimitri Van Den Meerssche had the opportunity to speak with professor Murray about his perspective on the challenges posed by Artificial Intelligence to our human agency and autonomy – the backbone of the modern rule of law. A conversation on algorithmic opacity, the peril of dehumanization, the illusionary ideal of the'human in the loop' and the urgent need to go beyond'ethics' in the international regulation of AI. One central observation in your Lecture is how Artificial Intelligence threatens human agency. Could you elaborate on your understanding of human agency and how it is being threatened? In my Lecture I refer to the definition of agency by legal philosopher Joseph Raz. He argues that to be fully in control of one's own agency and decisions you need to have capacity, the availability of options and the freedom to exercise that choice without interference. My claim is that there are four ways in which the adoption and use of algorithms affect our autonomy, and particularly Raz's third requirement: that we are to be free from coercion. First, there is an internal and positive impact. This happens when an algorithm gives us choices, which have been limited by pre-determined values – values that we cannot observe. The second impact is internal and negative. In this scenario, choices are removed because of pre-selected values.