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Artificial intelligence has become a new engine for economic growth and as the central driving force of the new round of industrial reforms, artificial intelligence will further discharge the energy accumulated from prior technological revolutions and industrial alterations by generating new powerful engines to modernize economic activities such as production, distribution, exchange, and consumption. The decentralized nature of blockchain generates, the new concept of a token economy in which the community’s revenue is allocated to the actual content producers and service users who generate value. In addition, Blockchain is a key technology that enables new protocols for the establishment of a token economy in the future, leading to a new economic paradigm. Digital technologies are now turning the world upside down and so an ongoing series of technological developments have transformed economic and social life. The integration of AI agents into society has led to a different manner in which persons interact with each other, along with a new kind of direct interaction presented with AI agents, which are increasingly posed in society.
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Řehák, D., Šenovský, P., Hromada, M., Pidhaniuk, L., Dvořák, Z., Loveček, T., Ristvej, J., Leitner, B., Sventeková, E., Mariš, L., 2018. Metodika hodnocení resilience prvku kritické infrastruktury. Ostrava: VŠB-Technická univerzita Ostrava, Fakulta bezpečnostního inženýrství. ISBN 978-80-248-4164-9.
Obrová, V., Smolíková, L., 2013. The role of risk management in successful project management. International Business Information Management Association Conference, IBIMA 2013; Vienna.
Klučka, J., Havko, J., Haviernikova, K., 2016. Risk Management in Cluster’s Cooperation in Slovak Republic. Conference: 3rd International Multidisciplinary Scientific Conference on Social Sciences and Arts, SGEM 2016 Location: Albena, Bulgaria.
P. Dunphy, F.A. Petitcolas, A first look at identity management schemes on the blockchain, IEEE Secur. Priv. 16 (4) (2018) 20–29.
R. Beck, M. Avital, M. Rossi, J.B. Thatcher, Blockchain Technology in Business and Information Systems Research, Springer, 2017.
G. He, W. Su, S. Gao et al., TD-Root: A trustworthy decentralized DNS root management architecture based on permissioned blockchain, Future Generation Computer Systems (2019), doi: https://doi.org/10.1016/j.future.2019.09.037.
Durst, S., Bruns, G., & Henschel, T. (2016), The management of knowledge risks: What do we really know? International Journal of Knowledge and Systems Science, 7, 19–29. https://10.4018/IJKSS.2016070102.
Bratianu, C. (2018). A holistic approach to knowledge risk. Management Dynamics in the Knowledge Economy, 6, 593–607. https://doi.org/10.25019/MDKE/6.4.06.
Susanne Dursta, Christoph Hinteregger, Malgorzata Zieba, The linkage between knowledge risk management and organizational performance, 2019 Journal of Business Research 1-10.
Valeria Naciti, Corporate governance and board of directors: The effect of a board composition on firm sustainability performance, 2019 Journal of Cleaner Production 117727.
Charilaos Mertzanisa, Mohamed A.K. Basuonyb, Ehab K.A. Mohamed, Social institutions, corporate governance and firm-performance in the MENA region 2019 Research in International Business and Finance 75.
Shiyang Huang, Ying Jiang, Zhigang Qiuc, Zhiqiang Ye, An equilibrium model of risk management spillover, 2020 Journal of Banking and Finance 105604 p15 (The risk management spillover effect arises because relative performance concerns have different effects on different managers. Specifically, relative performance concerns do not change the behaviors of RM managers but have a significant impact on those of normal managers. As a result, relative performance concerns amplify the effects of risk management suggesting that a small fraction of RM managers can have a significant market impact.) Sumon Bhaumik, Nigel Driffield, Ajai Gaur, Tomasz Mickiewicz, Paul Vaalere, Corporate governance and MNE strategies in emerging economies, Journal of World Business 54 (2019) 234–243.
Corinne H.Y. Tan, ‘Technological “Nudges” and Copyright on Social Media Sites’ (2015) 2015(1) Intellectual Property Quarterly 62; David Tan, ‘Fair Use and Transformative Play in the Digital Age’ in Megan Richardson and Sam Ricketson (eds), Research Handbook on Intellectual Property in Media and Entertainment (Edward Elgar 2017).
‘Intellectual Property’ (Facebook) < transparency.facebook.com/intellectual-property> accessed 26 July 2019. See further Daniel Seng, ‘The State of the Discordant Union: An Empirical Analysis of DCMA Takedown Notices’ (2014) 18 Virginia Journal of Law & Technology 369; Jennifer Daskal, ‘Google Inc. v. Equustek Solutions Inc.’ (2018) 112 American Journal of International Law 727.
Anat Lior, The AI Accident Network: Artificial Intelligence Liability Meets Network Theory https://ssrn.com/abstract=3561948.
Hous. Fed’n of Teachers, Local 2415 v. Hous. Indep. Sch. Dist., 251 F. Supp. 3d 1168 (S.D. Tex. 2017). Danielle Keats Citron & Frank Pasquale, The Scored Society: Due Process for Automated Predictions, 89 WASH. L. REV. 1, 8–10 (2017) (describing the use of algorithms in credit scoring). Andrew D. Selbst, Disparate Impact in Big Data Policing, 52 GA. L. REV. 109, 113–15 (2017) (describing predictive policing). Meredith Whittaker Et Al., AI Now Inst., AI Now Report 2018, at 18–22 (2018), https://ainowinstitute.org/AI_Now_2018_Report.pdf.
Andrew Guthrie Ferguson, Big Data and Predictive Reasonable Suspicion, 163 U. PA. L. REV. 327, 389–95 (2015) (discussing some of the ways law enforcement can use large data sets to eliminate biases in policing); Ric Simmons, Big Data, Machine Judges, and the Legitimacy of the Criminal Justice System, 52 U.C. DAVIS L. REV. 1067, 1096–97 (2018) (noting that one of the “primary benefits of using predictive algorithms” is “their complete disregard of irrelevant subjective factors” like race, religion, what a person wears, how they conduct themselves in court, and so forth).
Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money And Information 1–17 (2015) (describing the ways in which data analyses about consumers are hidden from view and from legal process). Emily Berman, A Government of Laws and Not of Machines, 98 B.U. L. REV. 1277, 1279 (2018).
Rebecca Wexler, Life, Liberty, and Trade Secrets: Intellectual Property in the Criminal Justice System, 70 Stan. L. Rev. 1343 (2017) (describing how trade secrecy impacts the criminal justice system noting that policies for algorithmic transparency should consider the potential for gaming). Andrew D. Selbst & Solon Barocas, The Intuitive Appeal of Explainable Machines, 87 Fordham L. Rev. 1085, 1092–94 (2018) (arguing that understanding machine learning algorithms takes “specialized knowledge” and even with that knowledge, the basis of a decision is often still inscrutable). Harry Surden, Machine Learning and the Law, 89 Wash. L. Rev. 87, 89 (2014) (explaining how machine learning can make algorithms capable of adapting “to enhance their performance on some task through experience”).
Mayer-Schönberger/Cukier, Big Data: A Revolution that will Transform How We Live, Work and Think, 2013, p. 14, 15, 18, and p. 163: “Big Data does not tell us anything about causality”. Skopek, Big Data’s Epistemology and Its Implications for Precision Medicine and Privacy, in: Cohen/Fernandez Lynch/Vayena/Gasser (eds.), Big Data, Health Law, and Bioethics, 2018, pp. 30 et seq.
Ari Ezra Waldman, Power, Process, and Automated Decision-Making, 88 Fordham L. Rev. 613 (2019). Available at: https://ir.lawnet.fordham.edu/flr/vol88/iss2/9; Jedediah Purdy, Neoliberal Constitutionalism: Lochnerism for a New Economy, 77 Law & Contemp. Probs. 195, 198–203 (2014) (describing neoliberal interpretations of constitutional provisions); Amanda Shanor, The New Lochner, 2016 Wis. L. Rev. 133, 145 (chronicling a corporate social movement to interpret the First Amendment in line with neoliberal, libertarian ideas).
Chris Jay Hoofnagle et al., The European Union General Data Protection Regulation: What It Is and What It Means, 28 Info. & Comm. Tech. L. 65, 67 (2019) (“[T]he GDPR can be seen as a data governance framework. The GDPR encourages companies to think carefully about data and have a plan for the collection, use, and destruction of the data. The GDPR compliance process may cause some businesses to increase the use of data in their activities, especially if the companies are not dataintensive, but the GDPR causes them to realize the utility of data.”); Margot E. Kaminski, Binary Governance: Lessons from the GDPR’s Approach to Algorithmic Accountability, 92 S. Cal. L. Rev. 1529, 1552–53 (2019).
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Mikella Hurley and Julius Adebayo, “Credit Scoring in the Era of Big Data,” Yale Journal of Law & Technology 18, no. 1 (2016): 148–216.
Wachter/Mittelstadt/Floridi, Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation, (2017) 7(2) International Data Privacy Law 76, at p, 92. http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-%2F%2FEP%2F%2FTEXT%2BREPORT%2BA7-2013-0402%2B0%2BDOC%2BXML%2BV0%2F%2FEN&language=EN.
F. Decarolis and G. Rovigatti. From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising. working paper, 2018. E. Calvano, G. Calzolari, V. Denicolo, and S. Pastorello. Artificial Intelligence, Algorithmic Pricing and Collusion. SSRN Electronic Journal, 2019b. www.ssrn.3304991.
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Direct discrimination is defined as follows in Article 2(2)(a) of the Racial Equality Directive 2000/43/EC: ‘Direct discrimination shall be taken to occur where one person is treated less favourably than another is, has been or would be treated in a comparable situation on grounds of racial or ethnic origin.’ the Employment Equality Directive (2000/78/EC), the Gender Goods and Services Directive (2004/113/EC), and the Recast Gender Equality Directive (2006/54/EC) use similar definitions.
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P. Hacker, ‘Teaching fairness to artificial intelligence: Existing and novel strategies against algorithmic discrimination under EU law’ (2018) 55 Common Market Law Review, Issue 4, pp. 1143–1185.
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A. Rieke, M. Bogen and D.G. Robinson, ‘Public Scrutiny of Automated Decisions: Early Lessons and Emerging Methods, Upturn and Omidyar Network’ (2018) http://www.omidyar.com/sites/default/files/file_archive/Public%20Scrutiny%20of%2Automated%20Decisions.pdf; B. Bodo et al., ‘Tackling the algorithmic control crisis-the technical, legal, and ethical challenges of research into algorithmic agents’ (2017) 19 Yale JL & Tech. 133.
Zuiderveen Borgesius, Frederik J. “Strengthening legal protection against discrimination by algorithms and artificial intelligence.” The International Journal of Human Rights (2020): 1–22. https://doi.org/10.1080/13642987.2020.1743976.
Emre Kazim, Adriano Koshiyama, Lack of Vision A Comment on the EU’s White Paper on Artificial Intelligence, https://ssrn.com/abstract=3558279 p29.
42 USC § 3601 et seq. 4 42 USC § 3605(a). These laws do not exhaust the legal framework governing discrimination in credit pricing.
Talia B. Gillis & Jann L. Spiess, Big Data and Discrimination, 2019 The University of Chicago Law Review 459 p17.
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In ASIC v Healey (2011) 278 ALR 618 (the “Centro case”) the Federal Court found the directors of the Centro Group personally liable for errors in the financial statements of the Group for the financial year ended 30 June 2007. The court found that the directors failed to discharge their duties with the degree of care and diligence that a reasonable person would in the circumstances; and failed to take all reasonable steps to comply with, or to secure compliance with, the financial reporting provisions in the Corporations Act 2001.
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Sarah Danckert and Clancy Yeates, ‘CBA Accused of Board Minutes Criminal Breach’ The Sydney Morning Herald (21 November 2018).
Maziar Peihani, ‘Financial Regulation and Disruptive Technologies: The Case of Cloud Computing in Singapore’ (2017) Singapore Journal of Legal Studies 77 Ellie Chapple and Elisabeth Sinnewe, ‘So What’s a Secretary to Do? Banking Royal Commission Raises Questions About What’s in Minutes’ The Conversation (29 November 2018) (online) http://theconversation.com/so-whats-a-secretary-to-do-banking-royal-commission-raises-questions-aboutwhats-in-minutes-107509: ‘Company secretaries are already acutely aware that every set of minutes of every board meeting might one day end up as evidence.’ Australian Securities and Investments Commission v Hellicar (2012) 286 ALR 501 (the resolution adopting a misleading ASX announcement in the minutes of a board meeting was held to have been a ‘contemporaneous record of proceedings at the meeting).
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He Li, Jun Dai, Tatiana Gershberg and Miklos Vasarhelyi, ‘Understanding Usage and Value of Audit Analytics for Internal Auditors: An Organisational Approach’ (2018) 28 International Journal of Accounting Information Systems 59; Nurmazilah Mazhan and Andy Lymer, ‘Examining the Adoption of Computerassisted Audit Tools and Techniques’ (2014) 29 Managerial Auditing Journal 327.
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The New Reality: Turning Risk Into Opportunity Through The Dupont Legal Model 2 (Silvio J. Decarli & Andrew L. Schaeffer eds., 5th ed. 2009).
Sterling Miller, Artificial Intelligence And Its Impact on Legal Technology: To Boldly Go Where No Legal Department Has Gone Before, Thomson Reuters (“As CEOs and CFOs become more accustomed to using AI, they will expect the other members of the CSuite—including the general counsel and legal department—to follow suit. In-house lawyers that embrace AI, will become more valuable to the next generation of CEOs and CFOs.”).
Sarah Murray, Algorithms Tame Ambiguities in Use of Legal Data, FIN. TIMES (Nov. 15, 2018), https://www.ft.com/content/50b0eba4-d063-11e8-9a3c-5d5eac8f1ab4 (“[T]he next step is to enable machines to make qualitative analyses of legal documents.”).
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Carolyn Kay Brancato Et Al., The Conference Bd., The Role Of U.S. Corporate Boards In Enterprise Risk Management 10 (2006), https://www.conference-board.org/pdfdownload.cfm?masterProductID=3840.
Alm Intelligence & Morrison & Foerster LLP, General Counsel Up-Atnight Report 5 (2018), https://media2.mofo.com/documents/170622-gc-up-at-nightreport.pdf (“Now, GCs must not only think globally to maintain a culture of compliance regardless of geography, but also act locally in establishing policies and procedures to ensure corporate action meets the prevailing local regulatory standards.”).
June Eichbaum, Globalization and General Counsel, MINORITY CORP. COUNS. ASS’N, https://www.mcca.com/mcca-article/globalization-and-general-counsel/ (explaining that “hands-on experience with regulators in the European Union and in Asia” is a ‘must-have’ for general counsel”). Jennifer G. Hill, Legal Personhood and Liability for Flawed Corporate Cultures (European Corp. Governance Inst. Working Paper Series in Law, Working Paper No. 413/2018, 2018).
Lisa M. Fairfax, From Apathy to Activism: The Emergence, Impact, and Future of Shareholder Activism as the New Corporate Governance Norm, 99 B.U. L. REV. 1301, 1314 (2019). Matteo Tonello & Matteo Gatti, Board-Shareholder Engagement Practices, Harv. L. Sch. F. On Corp. Governance (Dec. 30, 2019), https://corpgov.law.harvard.edu/2019/12/30/board-shareholder-engagement-practices/ (“Sixty percent or more of the largest companies involve their general counsel in board exchanges with investors.”).
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- AI Risk Management
Georgios I. Zekos
- Springer International Publishing
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