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Online tribunal judgments and the limits of open justice

Published online by Cambridge University Press:  07 June 2021

Zoe Adams ,
Abi Adams-Prassl  and
Jeremias Adams-Prassl Open the ORCID record for Jeremias Adams-Prassl [Opens in a new window]
Zoe Adams
Affiliation:
King's College, University of Cambridge, Cambridge, UK
Abi Adams-Prassl
Affiliation:
Department of Economics, University of Oxford, Oxford, UK
Jeremias Adams-Prassl*
Affiliation:
Magdalen College and Faculty of Law, University of Oxford, Oxford, UK
*
*Corresponding author e-mail: [email protected]
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Abstract

The principle of open justice is a constituent element of the rule of law: it demands publicity of legal proceedings, including the publication of judgments. Since 2017, the UK government has systematically published first instance Employment Tribunal decisions in an online repository. Whilst a veritable treasure trove for researchers and policy makers, the database also has darker potential – from automating blacklisting to creating new and systemic barriers to access to justice. Our scrutiny of existing legal safeguards, from anonymity orders to equality law and data protection, finds a number of gaps, which threaten to make the principle of open justice as embodied in the current publication regime inimical to equal access to justice.

Keywords

employment tribunalsonline judgmentsopen justicemachine learningdata protectionalgorithmic discrimination
Type
Research Article
Information
Legal Studies , Volume 42 , Issue 1 , March 2022 , pp. 42 - 60
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Society of Legal Scholars

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Footnotes

†

Research Fellow, King's College, University of Cambridge; Senior Research Fellow and Associate Professor, Department of Economics, University of Oxford; Professor of Law, Magdalen College and Faculty of Law, University of Oxford. We acknowledge funding from the Economic and Social Research Council, Grant No ES/ S010424/1, and are grateful to Stergios Aidinlis, John Armour, Alysia Blackham, David Erdos, Aislinn Kelly-Lyth, Ravi Naik, Hannah Smith, Fabian Stephany, and Stefan Theil, as well as participants at a workshop in Oxford in March 2020 and the anonymous reviewers for feedback and discussion. The usual disclaimers apply.

References

1 Susskind, R Online Courts and the Future of Justice (Oxford: Oxford University Press, 2019)CrossRefGoogle Scholar.

2 National Audit Office Transforming Courts and Tribunals – A Progress Update, HC 2638 Session 2017–2019 13 September 2019, https://www.nao.org.uk/wp-content/uploads/2019/09/Transforming-Courts-and-Tribunals.pdf (accessed 20 May 2021); Justice Select Committee, Courts and Tribunal Reforms, Second Report of Session 2019 HC 190, 30 October 2019, https://publications.parliament.uk/pa/cm201919/cmselect/cmjust/190/190.pdf (accessed 20 May 2021).

3 See, for example, https://www.gov.uk/government/statistics/tribunal-statistics-quarterly-january-to-march-2020 (accessed 20 May 2021).

4 SI 2013/1237, reg 14(1); see also the Employment Tribunal Rules of Procedure 2013 (as amended), r 67.

5 SE Ryder ‘Securing open justice’ in B Hess and A Koprivica Harvey (eds) Open Justice (Nomos Verlagsgesellschaft mbH & Co KG, 2019) https://www.nomos-elibrary.de/index.php?doi=10.5771/9783845297620-125 (accessed 20 May 2021).

6 R (on the application of UNISON) v Lord Chancellor [2017] UKSC 51 at [68].

7 These principles are also embodied in Art 6(1) of the European Convention on Human Rights, and have been reiterated by the Supreme Court in R (Guardian News and Media) v City of Westminster Magistrates’ Court [2012] EWCA Civ 420.

8 The general practice in the Family Division is for judgments in ancillary relief cases, if published, to be anonymised (see Lykiardopulo v Lykiardopulo [2010] EWCA Civ 1315 at [79]). However, ordinary civil claims brought by children are not routinely anonymised (see JXF v York Hospitals NHS Foundation Trust [2010] EWHC 2800 (QB)). The fact that the case concerns private information is not, of itself, a sufficient basis for making an anonymity order (see eg Bernard Gray v UVW [2010] EWHC 2367 (QB)).

9 ‘Judiciary and data protection: privacy notice’ https://www.judiciary.uk/about-the-judiciary/judiciary-and-data-protection-privacy-notice/ (accessed 20 May 2021). Indeed, an order for anonymity is seen as a derogation from the principle of open justice and an interference with the Art 10 rights of the public at large (see eg Bernard Gray v UVW [2010] EWHC 2367 (QB) at [1]).

10 For a detailed comparison of the position in different countries, see van Opijnen, M et al. ‘Online publication of Court decisions in Europe’ (2017) 17 LIM 136CrossRefGoogle Scholar.

11 Judiciary and data protection: privacy notice’, above n 9.

12 See for example ‘A threatened interference with the Art 8 rights of a claimant is not, by itself, always sufficiently serious to necessitate the imposition of an injunction or anonymity order’: JIH v News Group (No 2) [2010] EWHC 2979 (QB) at [30].

13 The exemption is set out in GDPR, Art 23(1)(f) and DPA 2018, s 15(2)(b) and Sch 2, Pt 2, paras 6 and 14(2). The reason for the exception, according to the Judicial Working Group, being ‘to secure the constitutional principles of judicial independence and of the rule of law’.

14 This tension is implicit in ECHR, Art 6, which states: ‘the press and public may be excluded from all or part of the trial in the interest of morals, public order or national security in a democratic society, where the interests of juveniles or the protection of the private life of the parties so required, or to the extent strictly necessary in the opinion of the court in special circumstances where publicity would prejudice the interests of justice’.

15 See for example https://fama.io/.

16 See for example the work of legal realist scholars: Merry, S Engle, The New Legal Realism: 2, Klug, H (ed) (Cambridge: Cambridge University Press, 2016)Google Scholar; Llewellyn, KN ‘A realistic jurisprudence – the next step’ (1930) 30 Columbia Law Review 431CrossRefGoogle Scholar.

17 Barnard, C et al. ‘Beyond employment tribunals: enforcement of employment rights by EU-8 migrant workers’ (2018) 47(2) Industrial Law Journal 226CrossRefGoogle Scholar.

18 Lockwood, G et al. ‘A quantitative and qualitative analysis of sexual harassment claims 1995–2005’ (2011) 42 Industrial Relations Journal 86Google Scholar; Rosenthal, P and Budjanovcanin, A ‘Sexual harassment judgments by British employment tribunals 1995–2005: implications for claimants and their advocates’ (2011) 49 British Journal of Industrial Relations 236CrossRefGoogle Scholar.

19 LD Irving ‘Challenging ageism in employment: an analysis of the implementation of age discrimination legislation in England and Wales’ (Coventry University 2012) p 71, https://curve.coventry.ac.uk/open/file/ffc88163-6994-4400-bead-121298f52bd1/1/Irving%202012.pdf (accessed 20 May 2021). Cf also L Bengtsson ‘Addressing age stereotyping against older workers in employment: the CJEU and UK approach’ (2020) 62 International Journal of Law and Management 67.

20 A Blackham ‘Enforcing in employment tribunals: insights from age discrimination claims in a new “dataset”’ (2021) Legal Studies 1 at 6–8 provides a detailed explanation of her methodology.

21 See https://www.vizlegal.com (accessed 20 May 2021).

22 Screenshot of https://www.gov.uk/employment-tribunal-decisions taken on 3 March 2020.

23 See https://www.solomonic.co.uk/news/solomonic-to-develop-employment-dispute-resolution (accessed 20 May 2021).

24 GDPR, Art 4.

25 DPA 2018, Sch 2, Pt 5, para 26.

26 For a detailed overview (mutatis mutandis) see Mourby, M et al. ‘Governance of academic research data under the GDPR – lessons from the UK’ (2019) 9 International Data Privacy Law 192CrossRefGoogle Scholar.

27 This is usually achieved through a series of Python scripts.

28 E Ash et al ‘Gender attitudes in the judiciary: evidence from the US circuit courts’ (2020) CAGE Working Paper 462.

29 We return to this question, below.

30 R (on the application of UNISON) v Lord Chancellor [2017] UKSC 51. For a discussion of the impact on low-value claims see A Adams and J Prassl ‘Vexatious claims: challenging the case for employment tribunal fees’ (2017) 80 Modern Law Review 412.

31 See discussion in J Armour ‘AI and judicial precedents: a review of the literature’, February 2020 (on file with the authors).

32 I Chalkidis et al ‘Extreme multi-label legal text classification: a case study in EU legislation’ (2019) arXiv preprint arXiv:1905.10892.

33 Armour, above n 31.

34 Mulcahy, L ‘The collective interest in private dispute resolution’ (2013) 33 Oxford Journal of Legal Studies 59CrossRefGoogle Scholar.

35 Smith, D and Chamberlain, P Blacklisted; The Secret War Between Big Business and Union Activists (Oxford: New Internationalist, 2015)Google Scholar.

36 For examples of novel blacklisting practices in the context of data-analytics see N Newman ‘Reengineering workplace bargaining: how big data drives lower wages and how reframing labor law can restore information equality in the workplace’ (2017) 85 University of Cincinnati Law Review 693. For a discussion of blacklisting in the specific context of ‘big data’ see M Hu ‘Big data blacklisting’ (2016) 67 Florida Law Review 77. For a discussion of blacklisting practices in the UK more specifically see P Chamberlain and D Smith ‘Blacklisted: the secret war between big business and union activists’ (2016) New Internationalist; and on present-day concerns about blacklisting practices in the UK and elsewhere see S Kessler ‘Companies are using employee survey data to predict – and squash – union organizing’ (Medium, 30 July 2020), https://onezero.medium.com/companies-are-using-employee-survey-data-to-predict-and-squash-union-organizing-a7e28a8c2158; ‘New report recommends public inquiry into blacklisting scandal and criminal sanctions for blacklisters’ (IER, 14 December 2017), https://www.ier.org.uk/press-releases/new-report-recommends-public-inquiry-blacklisting-scandal-criminal-sanctions-blacklisters/.

37 Barnard et al, above n 17, at 242.

38 E Gordon Walker and G Baker ‘Litigants anonymous: the tribunal database and anonymity’ (2007) 24 ELA Briefing 8 at 9, citing Scott v Scott [1913] AC 417 at 446.

39 The QTS currently provides only annual statistics on the average level of compensation awarded for seven types of complaints.

40 This is particularly so in the context of pre-termination negotiations which, since 2013, cannot be introduced as evidence into an unfair dismissal hearing, except if there is evidence of improper behaviour. This is, however, not likely to include attempts by employers to impose an unfair bargain on workers where no form of discrimination, aggression, or victimisation is employed.

41 S Dahan ‘Determining worker type from legal text data using machine learning. Pervasive intelligence and computing’ (2020) IEEE PICom.

42 Aslam v Uber BV [2017] IRLR 4 at [96], citing the observations of Elias J in Kalwak.

43 J Adams-Prassl ‘What if your boss was an algorithm? Economic incentives, legal challenges, and the rise of artificial intelligence at work’ (2020) 41 Comparative Labor Law and Policy Journal 123.

44 Similar practices are already widespread, with some firms using them to identify candidates most likely to stay long-term in a job; to engage in fraudulent activities, notwithstanding no past record of misbehaviour; and/or to identify, in advance, any workers likely to quit their job. See ‘JPMorgan Chase develops “early warning system”’, https://www.bankinfosecurity.com/jpmorgan-chase-develops-early-warning-system-a-12855 (accessed 20 May 2021); ‘JPMorgan algorithm knows you're a rogue employee before you do’ Bloomberg.com (8 April 2015), https://www.bloomberg.com/news/articles/2015-04-08/jpmorgan-algorithm-knows-you-re-a-rogue-employee-before-you-do (accessed 20 May 2021); J Liu ‘This algorithm can predict when workers are about to quit – here's how’ (CNBC, 10 September 2019) https://www.cnbc.com/2019/09/10/this-algorithm-can-predict-when-workers-are-about-to-quitheres-how.html (accessed 20 May 2021); E Rosenbaum ‘IBM artificial intelligence can Predict with 95% accuracy which workers are about to quit their jobs’ (CNBC, 3 April 2019) https://www.cnbc.com/2019/04/03/ibm-ai-can-predict-with-95-percent-accuracy-which-employees-will-quit.html (accessed 20 May 2021).

45 F Zuiderveen Borgesius ‘Discrimination, artificial intelligence, and algorithmic decision-making’ Council of Europe (Strasbourg, 2018) p 51; Leicht-Deobald, U et al. ‘The challenges of algorithm-based HR decision-making for personal integrity’ (2019) 160 Journal of Business Ethics 377CrossRefGoogle ScholarPubMed.

46 Wachter, S and Mittelstadt, B ‘A right to reasonable inferences: re-thinking data protection law in the age of big data and AI’ (2019) Columbia Business Law Review 494Google Scholar, available at SSRN: https://ssrn.com/abstract=3248829; K Lum and J Johndrow ‘A statistical framework for fair predictive algorithms’ (2016) arXiv:1610.08077 [cs, stat] https://arxiv.org/abs/1610.08077 (accessed 20 May 2021); Williams, BA et al. ‘How algorithms discriminate based on data they lack: challenges, solutions, and policy implications’ (2018) 8 Journal of Information Policy 78CrossRefGoogle Scholar.

47 See ‘Amazon scraps secret AI recruiting tool that showed bias against women’ (Reuters, 10 October 2018) https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G (accessed 20 May 2021).

48 Employment Relations Act 1999 (Blacklists) Regulations 2010, SI 2010/493.

49 Ibid, reg 3(1), 3(2)(a).

50 Ibid, reg 3(2)(a)).

51 For a full discussion see C Barrow ‘The Employment Relations Act 1999 (Blacklists) Regulations 2010: SI 2010 No 493’ (2010) 39(3) Industrial Law Journal 300. See also R Zahn ‘Recent developments in blacklisting (2014) 124 Greens Employment Law Bulletin 4, who concludes that ‘[t]he Regulations seem to have had little impact on restricting the practice of blacklisting’.

52 Smith v Carillion (JM) Ltd [2015] EWCA Civ 209, [2015] IRLR 467; see further A Bogg ‘Common law and statute in the law of employment’ (2016) 69 Current Legal Problems 67 at 109.

53 Employment Tribunals (Constitution and Rules of Procedure) Regulations 2013, SI 2013/1237. The Rules are set out in Sch 1.

54 Ibid, r 50(3)(b).

55 Ibid, r 50(2).

56 See eg BBC v Roden [2015] ICR 985.

57 Gordon Walker and Baker, above n 38.

58 Re S (A Child) (Identification: Restrictions on Publication) [2005] 1 AC 593 at [17].

59 Stammeringlaw ‘Online tribunal decision database, and anonymity orders’ (Online Blog, 15 September 2020) https://www.stammeringlaw.org.uk/employment/resolving-employment-disputes/online-tribunal-decision-database-anonymity-orders/.

60 Aslam v Uber, above n 42, at [87].

61 Ameyaw v Pricewaterhousecoopers Services Ltd [2019] UKEAT 0244_18_0401 (4 January 2019), at [45].

62 Ibid, at [48].

63 Kirkham v United Kingdom Research and Innovation [2019] UKET 2501482/2018, at [44].

64 X v Y [2019] UKEAT/0302/18/RN, at [43], [47].

65 L v Q Ltd [2019] EWCA Civ 1417, para [26].

66 Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation), OJ L 119/1, 4.5.2016 (GDPR).

67 GDPR, Art 4(1); DPA 2018, s 3(2).

68 GDPR, Art 4(2); DPA 2018, s 3(4).

69 GDPR, Art 5(1)(b).

70 DPA 2018, s 56(2).

71 GDPR, Art 9(1) and Recital 51; DPA 2018, ss 10 and 11.

72 Wachter and Mittelstadt, above n 46.

73 Williams et al, above n 46.

74 For an in-depth discussion of this issue, see Wachter and Mittelstadt, above n 46.

75 Even where inferences have been recognised as personal data, the courts have made it clear that if the inference or opinion came about in a situation where the individual is seeking to be assessed, as in a job application, to request access to, and to rectify, those inferences, would be anathema to the very purposes of the processing, and would not be allowed under the GDPR. The right to rectification is also limited by the requirement that it not affect the fundamental rights and freedoms of others, including intellectual property and trade secrets (and, according to the recitals, the code underpinning the algorithm). For the most recent discussion of inferences, see Case C-434/16 Peter Nowak v Data Protection Commissioner [2017] ECR I-994, particularly the Opinion of Advocate General Jojott, 9–13; Joined Cases C-141/12 YS v Minister voor Immigratie, Integratie en Asiel, C-372/12 Minister voor Immigratie, Integratie en Asiel v M and S.

76 In this respect, the DPA 2018 is not as strict as the GDPR, which requires that an individual be able to object to being subject to automated profiling and request that a decision be made by a human (Art 22).

77 See for example Case C-553/07, College van burgemeester en wethouders van Rotterdam v MEE Rijkeboer, [2009] ECR I-293 at 48–52 and Case C-434/16, above n 75. See also Joined Cases C-141/12 and C-372/12, above n 75.

78 Wachter and Mittelstadt, above n 46.

79 Williams et al, above n 46.

80 Equality Act 2010, ss 13 and 19.

81 Though this is itself problematic: GDPR, Art 22.

82 There already exists provision in the Equality Act for ‘dual factor’ discrimination and this would go some way towards addressing some of the issues identified: Equality Act 2010, s 14.

83 Unfortunately, the questions of anonymity and online reporting of judgments were not included in the terms of reference for the Law Commission's recent report on Employment Law Hearing Structures Law Commission Report No 390, ‘Employment Law Hearing Structures: Report’ (2020) HC308.

84 van Opijnen et al, above n 10, at 178, 24.

85 At present such grounds are limited to the list of protected characteristics in the Equality Act, and trade union membership.