题名

論人工智慧及電子監控之研究－以美國勞工法為例

并列篇名

On the Study of AI, Electronic Surveillance-Take U.S.A. Labor Law as an Example

作者

許淑媛(Cadalina Hsu)

关键词

人工智慧 ； 電子監控 ； 電子網路 ； 員工隱私權 ； AI ； Electronic surveillance ； Internet ； Worker privacy

期刊名称

電腦稽核

卷期/出版年月

44期（2021 / 08 / 31）

页次

11 - 27

内容语文

繁體中文
中文摘要

雇主和其他僱用員工來執行相關工作上之活動，並且讓員工使用許多電子機制，其中有關招聘、員工評估、薪酬、紀律等和保留這些電子機制，例如包括電子追蹤器、監控攝影頭、身體的代謝監測儀、測量裝置和其中的技術，雇主利用這些工具啟用來記錄他們員工的一切活動、聆聽他們的對話、測量績效的時間等方面，並檢測反對組織活動，收集通過的數據等等。因人工智慧演算法方法進入永久保存，電子簡歷也可以識別和預測人的績效以及他們的職業道德、個性、工會傾向、雇主忠誠度和未來醫療保健成本、電子簡歷等等。由人工智慧生產的各種機制追隨員工從一項工作場所到另一項工作環境，因為他們移動圍繞網路世界無遠弗屆的工作場所，因此人工智慧和電子監控產生一個無形的電子網路，不但入侵員工隱私權，還阻止工會運行，使微妙的雇主形式化加劇就業歧視問題，讓工會變成無法正常運作以及無法賦予勞工相關勞工法之保障。本文介紹人工智慧在工作場所之運用及其使用如何改變招聘之實踐、評估、補償、控制和解僱員工。然後，專注於人工智慧威脅要破壞員工法律領域之保障：反歧視法、隱私權法、反托拉斯法和勞工法，最後，本文希冀能夠提出建議，為法律上未來提供相關改革和研究之方向。
英文摘要

Employers and others who hire or engage workers to perform services use a dizzying array of electronic mechanisms to make personnel decisions about hiring, employee evaluation, compensation, discipline, and retention. These electronic mechanisms include electronic trackers, surveillance cameras, metabolism monitors, wearable biological measuring devices, and implantable technology. These tools enable employers to record their workers' every movement, listen to their words, measure the minutes of performance evaluation, and detect oppositional organizing activities. The data collected is transformed by means of artificial intelligence (AI) algorithms into a permanent electronic resume that can identify and predict an individual's performance as well as their professional ethics, personality, union proclivity, employer loyalty, and future health care costs. The electronic resume produced by AI will accompany workers from job to job as they surround the boundless workplace in the cyber space. Thus, AI and electronic monitoring produce an invisible internetthat invade worker privacy as well as deter unionization, enabling subtle forms of employer blackballing, exacerbating employment discrimination. With these demerits, unions become ineffective and obliterate the protections of the labor laws. This article describes the many ways AI is being used in the workplace and how its use is transforming the practices of hiring, evaluating, compensating, controlling, and dismissing workers. It then focuses on four fields of law in which AI threatens to undermine worker protections：Anti-discrimination law, privacy law, antitrust law and labor law. Finally, this article maps out an agenda for future law reform and research.
主题分类 基礎與應用科學 > 資訊科學
参考文献
  1. Boch Imports, Inc.v NLRB, 826 F. 3d 558( 1stCir., 2016).
  2. NLRB Advice Memo, BP Exploration of Alaska, Inc.Case 19-CA- 29566 (July 11, 2005).
  3. NLRB v. Katz, 369 U.S. 736 ( 1962).
  4. American Civil Liberties Union, Sandvig v. Sessions—Challenge to CFAA Prohibition on Uncovering Racial Discrimination Online(Sept. 12, 2017)(last visited Dec. 20, 2018)
  5. NLRBAdvice Memorandum, East Coast MechanicalContractors, 22-CA- 253245 (Feb. 6, 2003).
  6. Citing Lutheran Heritage Village–Livonia, 343 NLRB 646 ( 2004).
  7. Dynatron/Bondo Corporation , 323 NLRBNo . 217 (N.L.R.B.), 323 NLRB 1263, 1269 ( 1997).
  8. 2018 WL 3703476(August 1, 2018).
  9. Sprain Brook Manor Nursing Home, 351 NLRB No. 75 (N.L.R.B.), 351 NLRB 1190, 1191 ( 2007)
  10. Aladdin Gaming, LLC, 345 NLRB 585 ( 2005).
  11. (2016).OKLA. L. REV.,555
  12. Lechmere, Inc., 295 NLRB No. 15 ( 1989).
  13. Grand Canyon Education, Inc., 359 NLRB No. 164 (N.L.R.B.), 359 NLRB 1481 ( 2013).
  14. 58 U.S.C. 158(a)( 5).
  15. NLRB v. Wooster Div. of Borg-Warner Corp, 356 U.S. 342 ( 1958).
  16. Brunswick Hospital Center, 265 NLRB No. 112 (N.L.R.B.), 265 NLRB 803, 807 ( 1982).
  17. Boch Imports, 362 NLRB No. 83at 707 ( 2015)
  18. Intertype Polymer Corp.,Petitionerv.NLRB, 801 F. 3d 224 ( 4thCir, 2015).
  19. Csc Holdings, Llc and Communications Workers of America , Case No . 29-CA- 190108(NY 2018).
  20. The Boeing Co., 365 NLRBNo. 154 (December 14, 2017).
  21. Allegheny Ludlum Corp., 333 N.L.R.B. No. 109, 2001 WL 855870 (Mar. 30, 2001)
  22. Allegheny Ludlum v NLRB, 301 F. 3d 167 ( 3rd Cir. 2002).
  23. Caterpillar Inc., 322 NLRB 674 ( 1996).
  24. Purple Communications, 361 NLRB No. 126 ( 2014).
  25. Agrawal, Ajay,Gans, Joshua,Goldfarb, Avi(2018).Prediction Machines: The Simple Economics of Artificial Intelligence.
  26. Bayer, David(2012).Employers Are Not Friends With Facebook: How The Nlrb Is Protecting Employees' Social Media Activity.Brook. J. Corp. Fin. & Com. L.,7,169+174.
  27. Bornstein, Stephanie(2019).Anti discriminatory Algorithms.ALABAMA L.REV.,70
  28. Gabriel J.X. Dance et al.,Facebook Gave Device Makers Deep Access to Data on Users and Friends, New York Times (June 3, 2018).
  29. Daugherty, Paul R.,Wilson, H. James(2018).Human + Machine: Reimagining Work in the Age of AI.
  30. Gasparetto, A.,Scalera, L.(2019).ABrief History of Industrial Robotics in the 20th Century.ADVANCES IN HISTORICAL STUDIES,8(1)
  31. Saul Hansell, GoogleAnswerto Filling Jobs Is an Algorithm, N.Y. TIMES (Jan. 3, 2007), http://www.nytimes.com/ 2007/ 01/ 03/technology/ 03google.html?_r= 1.
  32. Herbert, William A.(2008).The Electronic Workplace : To Live Outside the Law You Must Be Honest.EMPLOYEERTS.&EMP.POL'Y J.,12,49.
  33. Jacoby, Sanford M.(1988).Employee Attitude Surveys in Historical Perspective.Indus. Rels.,74(27),74-75.
  34. Katherine, V.W.(2002).Employee Representation in the Boundaryless Workplace.CHI.-KENT L. REV.,77,773- 819.
  35. Kim, Pauline T..Data-Driven Discrimination at Work.WM.&MARYL.REV.,58,857.
  36. King, G.,Mrkonich, Marko J.(2018).“ Big Data” and the Risk of Employment Discrimination.VILL. L. REV.,395.
  37. Anja Lambrecht & Catherine Tucker, Algorithmic Bias? An Empirical Study into Apparent Gender-Based Discrimination in the Display of STEM Career Ads, available athttps://papers.ssrn.com/sol 3/papers.cfm?abstract_id= 2852260.
  38. Kevin McGowan, Big Bad Data May Be Triggering Discrimination, BLOOMBERG LAW (Aug. 15, 2016).https://bol.bna.com/big-bad-data-may-be-triggering-discrimination/
  39. Savage, David D.,Bales, Richard.Video Games in Job Interviews: Using Algorithms to Minimize Discrimination and Unconscious Bias.ABA J. LAB. & EMPLOY. L.,2017(32),211+215.
  40. Noam Schieber, Unorthodox Hires, and Maybe Lower Pay, New York Times, December 7, 2018.
  41. Sprague, Robert(2018).Survey of (Mostly Outdated) LawsAffecting Workplace Monitoring.CHI-KENT LREV,93,221.
  42. Stone, Katherine V.W.(2007).A Fatal Mis-Match: Employer-Centric Benefits in a Boundaryless Workplace.LEWIS & CLARK L. REV.,11,451-480.
  43. Stone, Katherine V.W.(2006).Legal Protections for Atypical Employees: Employment Law for Workers Without Workplaces and Employees without Employers.BERKELEY J. EMPLOY. & LAB. L.,27,251-286.
  44. Stone, Katherine V.W.(2004).FROM WIDGETS TO DIGITS: EMPLOYMENT REGULATION FOR THE CHANGING WORKPLACE.Cambridge Univ. Press.
  45. Harriet Taylor, Lowe's introduces Lobos, a new autonomous in-store robot, CNBC (Aug. 30, 2016).
  46. Volz, Dustin(2014).Silicon Valley Thinks It Has the Answer to Its Diversity Problem.THE ATLANTIC,Sept. 26
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