Abstract
This chapter shows that the use of algorithms by digital service providers leads to insidious new forms of discrimination that cannot be protected by European anti-discrimination legislation alone. This legislation has limitations that can be traced back to two factors. Firstly, the European legislation is too closely linked to lists and typical causes of discrimination. Secondly, the European legislation shows gaps regarding indirect discrimination, as it does not provide adequate protection against proxies and hidden discrimination. It is more difficult to identify discrimination in the case of characteristics that are formally neutral, but which recur frequently in protected categories. Considering the scenario described, effective protection of online discrimination cannot be entrusted to sector-specific rules, but to a systematic interpretation, which is inevitably also attentive to the rules on unfair commercial practices and data protection.
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Notes
- 1.
Empirical evidence of bias in automated decision-making will require us to revisit many of the fundamental challenges to the conceptual apparatus of discrimination law—from academic debates about the discipline’s normative foundations to courts’ reluctance to approach causation through a ‘legalistic’ lens in the equality context. In these terms Adams-Prassl et al. (2023) p. 145. For an overview of European anti-discrimination legislation, see Wójcik (2022), p. 95. The author points out that “according to article 2 of the Treaty on the European Union, equality is one of the founding values of the EU. Pursuant to article 3 of the treaty, equality, non-discrimination, and social justice also remain the EU’s objectives. Furthermore, in Mangold, the European Court of Justice confirmed that non-discrimination constitutes a general principle of EU law”. On the fact that the existing categories of EU anti-discrimination law do not provide an easy fit for algorithmic decision making, see Hacker (2018), p. 1143; Wachter et al. (2021), p. 11.
- 2.
Gerards and Xenidis (2021), p. 11, in their report, they state that ‹‹at the legal level, such measures notably include adopting the draft Horizontal Directive under negotiation at the Council since 2008 to equalise the scope of EU non-discrimination law, addressing the gaps linked to the exceptions in the material scope of the Gender Goods and Services Directive in relation to the media, advertising and education, and bringing clarity on the prohibition of intersectional discrimination. Turning to the role of the Court of Justice, an expansive interpretation of the personal scope of EU equality law—both in terms of the scope of single protected grounds and the exhaustive nature of the list established in Article 19 TFEU considering the open-ended provision in Article 21 of the Charter—would enhance the law’s capacity to address algorithmic discrimination››.
- 3.
As to the debate on whether a discriminatory intent is necessary to apply the anti-discriminatory legislation see Mattarella (2020), p. 696. With reference to the thesis that discriminatory intent may be lacking, see Morozzo Della Rocca (2002), p. 123; Morozzo Della Rocca (2007), p. 289; Sitzia (2011), p. 99. From a different perspective see Maffeis (2015), p. 171; Navarretta (2020) p. 25. Concerning proxies, there are no unambiguous indications from the Court of Justice of the European Union. See on this point Case C-177/88, Elisabeth Johanna Pacifica Dekker mv. Stichting Vormingscentrum voor Jong Volwassenen (VJV-Centrum) Plus and Case C-668/15, Jyske Finans A/S v Ligebehandlingsnævnet, acting on behalf of Ismar Huskic. In the latter case, the Court of Justice of the European Union ruled that unequal treatment based on the plaintiff’s country of origin does not constitute discrimination based on ethnic origin, while in the Dekker case it held that discrimination based on pregnancy is a form of sex discrimination.
- 4.
Gerards and Xenidis (2021), p. 8.
- 5.
- 6.
Compare Zuiderveen Borgesius (2023), p. 11.
- 7.
Zuiderveen Borgesius (2019), p. 13. The author shows that if a bank uses an artificial intelligence system to predict which loan applicants will have problems repaying it, and the artificial intelligence system is trained on data that does not contain information on protected characteristics such as skin colour, this does not exclude that the training data cannot be equally discriminatory.
- 8.
Prince and Schwarcz (2020). The author states that the proxy discrimination is a particularly pernicious subset of disparate impact. Like all forms of disparate impact, it involves a facially neutral practice that disproportionately harms members of a protected class.
- 9.
See the Report of the Defender of Rights in partnership with the CNIL, Algorithms preventing automated discrimination. https://www.defenseurdesdroits.fr., p. 4.
- 10.
See Schönberger (2019), p. 184. The author points out that, especially in the health sector, where indirect discrimination is also very common, it is not difficult to pass the propoporality test.
- 11.
- 12.
See Zuiderveen Borgesius (2020), p. 3. Compare Perlingieri (2020b) p. 451. Hacker (2018), p. 1143, says that empirical evidence is mounting that artificial intelligence applications threaten to discriminate against legally protected groups. Whereas this evidence, the author shows how the concepts of anti-discrimination law may be combined with algorithmic audits and data protection impact assessments to unlock the algorithmic black box. On the need for multi-level anti-discrimination protection, see Capuzzo (2020), p. 89.
- 13.
- 14.
Gilman (2020), p. 1 shows that algorithms analyze data, sort people into categories, and serve as gatekeepers to life’s necessities. Yet people remain largely in the dark about these systems, creating an informational asymmetry whose harmful consequences fall most harshly on low-income people.
- 15.
See COM (2020) 264 final, Communication from the Commission to the European Parliament and the Council, Data protection as a pillar of citizens’ empowerment and the EU’s approach to the digital transition—two years of application of the General Data Protection Regulation. https://eur-lex.europa.eu. Regarding the circumstance that GDPR rules have the advantage of being based on ex ante and not only ex post protection mechanisms, taking a risk-based approach to prevent the misuse of personal data, compare Li (2022) p. 4; Contissa et al. (2018); Mantelero (2018), p. 299.
- 16.
Hoeren and Niehoff (2022); De Franceschi and Schulze (2019), p. 22. The author emphasises that European Union started bringing consumer protection in line with the digital age by acknowledging data as counter-performance for the purposes of the new Digital Content Directive. See also Mendola (2023), p. 423; Schmidt-Kessel (2023), p. 153; Gardiner (2022).
- 17.
About data as fragments of people’s lives to which they refer see Tomassini (2020). These are fragments that have a value not because of the intent of the person who left them, often unconsciously, but because they are collected and processed according to a certain procedure by a machine learning algorithm. The value of this data is determined because of the work of the person who collects and processes it and by doing so obtains a certain useful result that can be spent on the network. See Franzoni (2021), p. 6.
- 18.
https://eur-lex.europa.eu. Regarding profiling, reference must be made to Article 4 of the GDPR, which states that profiling means any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in particular to analyse or predict aspects concerning that natural person’s performance at work, economic situation, health, personal preferences, interests, reliability, behaviour, location or movements. With reference to the relationship between profiling and technological evolution, compare: Perlingieri (2020a), p. 177; Perlingieri (2018), p. 481; Garofalo (2021), p. 1505; Pellecchia (2018), p. 1209; D’ Ippolito (2021), p. 87.
- 19.
See Mendoza and Bygrave (2017) p. 77, where, in particular, a critical analysis is made of Article 22 of the GDPR in comparison with Article 15 of Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. Article 22 of the GDPR, according to the authors, while offering individuals stronger protection than Article 15 of the 1995 directive, is doubtful to have a significant practical impact on automated profiling.
- 20.
The prohibition of decisions based solely on automated processing is not absolute, there are exceptions, specified in paragraph 2 of Article 22, namely where such a decision: is necessary for entering into, or performance of, a contract between the data subject and a data controller; is authorised by Union or Member State law to which the controller is subject and which also lays down suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests; or is based on the data subject’s explicit consent. Compare Grasso (2023) p. 29.
- 21.
Convention 108, Convention for the protection of individuals regarding the processing of personal data. Council of Europe. https://www.europarl.europa.eu. Convention 108 constitutes the first binding international instrument which is aimed at protecting individuals against abuses which may accompany the collection and processing of personal data, and which seeks to regulate, at the same time, the cross-border flow of personal data.
- 22.
- 23.
Skitka et al. (1999), p. 991. The authors are referred to for an analysis of some computerised human decision support and aid systems in some common contexts, in comparison with unsupported decisions.
- 24.
Zadeh et al. (2017).
- 25.
- 26.
A recent empirical study showed how the targeting of online ads can reinforce stereotyping and segregation on the labour market: during the experiment, researchers used the Facebook advertising platform to neutrally disseminate various employment ads. In the end, cashier positions in supermarkets reached an audience composed of 85% women, while ads for taxi driver positions reached a 75% black audience and ads for lumberjack positions reached an audience that was 90% male and 72% white. A global consensus has emerged among both researchers and policymakers that risks of algorithmic discrimination are pervasive and multifaceted. In this context, understanding these risks and the types of legal challenges they create is key to ensuring equality and combating discrimination. On this point, please refer to Gerards and Xenidis (2021), p. 7; De Franceschi (2022), p. 73; Frosini (2020), p. 402.
- 27.
Griffin (2022), p. 1.
- 28.
The regulation appears to convey a clear message: to restore centrality to the human factor, although without the necessary logical and legal precedence of the human agent being an obstacle to technological innovation and to the improvement of the processes that public and private parties put in place. This is what has been affirmed by Pajno et al. (2019), p. 13.
- 29.
- 30.
- 31.
The Article 10 of the Directive (EU) 2016/680 states that processing of personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, and the processing of genetic data, biometric data for the purpose of uniquely identifying a natural person, data concerning health or data concerning a natural person’s sex life or sexual orientation shall be allowed only where strictly necessary, subject to appropriate safeguards for the rights and freedoms of the data subject, and only: where authorised by Union or Member State law; to protect the vital interests of the data subject or of another natural person; or where such processing relates to data which are manifestly made public by the data subject.
- 32.
- 33.
Freire de Carvalho (2020), p. 6.
- 34.
- 35.
Irti (2021), p. 40, points out that while Article 1 of Directive 95/46/EC stated that Member States shall protect the fundamental rights and freedoms of natural persons, and in particular their right to privacy with respect to the processing of personal data, Article 1(2) of the GDPR, stated that GDPR protects fundamental rights and freedoms of natural persons and in particular their right to the protection of personal data. The author dwells, in particular, on the implications of the different content of the rules in question.
- 36.
Compare Gentili (2022) p. 707.
- 37.
Perlingieri (2018), p. 481. The author emphasises that the innovation marks the transition from a conception based exclusively on informed consent to one characterised predominantly by control. The author also states that it is no longer possible to disguise the fact that consent is not sufficient and in fact in some respects misleading and unsuitable to guarantee respect for the person. Compare Francesca (2022), p. 563; Thobani (2015), p. 459; Mignone (2014), p. 14.
- 38.
Irti (2021), p. 42; Petit and Van Cleynenbreugel (2020), p. 4, underline that Digital businesses and online platforms defy traditional notions of market power grounded in a well-defined relevant product/price. In the context of digital businesses consumers rather pay with their own data or with their time, which may require different tools to analyze market dominance. Often considered as powerful economic actors, a platform is dependent on the ability of the technology underlying it to maintain and attract users to its services. The compilation and use of an ever-increasing amount of personal data and big data plays an essential role in this regard.
- 39.
- 40.
The Data Act lays down harmonised rules on making data generated by the use of a product or related service available to the user of that product or service, on the making data available by data holders to data recipients, and on the making data available by data holders to public sector bodies or Union institutions, agencies or bodies, where there is an exceptional need, for the performance of a task carried out in the public interest. In the Data Act, attention is also paid to the fact that the clauses on the provision of data are non-discriminatory. Whereas 41 in fact provides that to compensate for the lack of information on the conditions of different contracts, which makes it difficult for the data recipient to assess if the terms for making the data available are non-discriminatory, it should be on the data holder to demonstrate that a contractual term is not discriminatory. It is not unlawful discrimination, where a data holder uses different contractual terms for making data available or different compensation if those differences are justified by objective reasons. On this point see Orlando (2022a, b); Sganga (2022), p. 65; Caloprisco (2021), p. 169.
- 41.
See in particular Article 26(2) of the Data Governance Act. On this point compare Alfonsi (2022) p. 294. About the European Strategy Data, see Arisi (2022) p. 33. For a comparison between the GDPR and the Data Governance Act, see Resta (2022), p. 971. The author, p. 984, argues that Articles 15 of the GDPR do not always work at the operational level, either because the incentives needed to overcome the costs of inertia involved in exercising the right are lacking or because the very possibilities of super vision as to how data can be used are limited. The Data Governance Act, on the other hand, prefigures more or less advanced forms of support to individuals both in the pre-consent phase (Art. 12(1)(m)); and in the phase of exercising the data subject’s rights and exercising the relevant remedies (Art. 2(1)(11); Art. 10(1)(b)).
- 42.
- 43.
- 44.
- 45.
Principles, Definitions and Model Rules of European Private Law Draft Common Frame of Reference (DCFR), Outline Edition, 2009, p. 65 (7). See Books II and III—2:101 to II—2:105 and III—1:105.
- 46.
For a detailed reconstruction on the point, see Checchini (2019), p. 4. The problem is usually approached in terms of the prevalence of the principle of equality over the principle of contractual freedom, neglecting that these are principles of equal weight operating in different fields, since equality is pertinent to cases of distributive justice: equality in a community, equality before the law. Contract, however, is a case of retributive justice, to which the principle of freedom pertains. On these aspects compare Galgano (2002), p. 53; Buonocore (2008), p. 551; Gentili (2009), p. 221. The author states that individual relationships do not lend themselves to being subjected to a judgement of equality of treatment which, in fact, presupposes the existence of two terms to be compared and a criterion of comparison that are not found in a strictly individual relationship. For a critique of attempts to transfer aspirations of distributive justice onto the contract, see D’Amico (2019), p. 48. With specific reference to the impact of the principle of non-discrimination in contractual relations, see Piraino (2015) p. 233; Perlingieri (2017), p. 1629, says that each contract is characterised by its own “proclaimed social function”, which represents the reference parameter for appreciating its merits. Roppo (2011), p. 79, considers that to the extent that it is the realm of freedom, the contract can also be the realm of inequality and discrimination, based on the free choices of the contracting parties. For the author, private autonomy is intrinsically discriminatory and between contract and equality there is an ambiguous relationship, which suffers from the ambiguity inherent in the very idea of equality: declined, as is well known, in terms of formal equality or substantial equality. See also Roppo (2020), p. 28.
- 47.
See Resta (2019), p. 211. The author suggests distinguishing issues concerning personal data from those concerning non-personal data. For a comparison between the Artificial Intelligence Act and the EU data protection acquis see Mazzini and Scalzo (2022) p. 35. Regarding the relationship between GDPR and consumer discipline, compare Pagliantini (2022), p. 1560.
- 48.
Lanni (2020), p. 120.
- 49.
Auer (2021), p. 14, fears the danger of moving backwards from the intellectual standards of decades, if not centuries, about discrimination. Montinaro (2022), p. 352, points out that the omnibus directive refers to the GDPR and that from this it can be deduced that the unfairness of the practice is not excluded by mere compliance with the obligation of transparency of parameters. In particular, the author observes that there could be the collection of data to personalise prices without the consent of consumers; the use of data to personalise prices when such data were requested for other stated reasons; the use of knowledge of a specific vulnerable condition obtained through real-time observation of an individual consumer.
- 50.
See Li (2022), p. 4. On the fact that artificial Intelligence (AI) systems are increasingly being deployed by marketing entities in connection with consumers’ interaction and manipulation as a substantive legal measure for drawing the line between fair and unfair practices, see Galli (2022), p. 1. On how instruments in EU consumer law could alleviate certain asymmetries in power and information, see Durovic and Watson (2022), p. 153. In particular, the authors point out that consumer data has become a driving force in the digital economy. As the number of data interactions increases, so too the insights into ever more intimate aspects of one’s daily life, behaviour and personality. Amongst the various products and services, one innovative advancement in the world of data-driven technology stands out as deserving particular attention: the capability to infer emotions from (personal) data and to use such information to respond to an individual’s needs on a highly intimate level. Whereas the technology has considerable potential, it is controversial not least due to the highly sensitive and private nature of emotions but also due to its questionable reliability as well as potential adverse effects.
- 51.
https://eur-lex.europa.eu. Compare Capobianco (2018), p. 13.
- 52.
- 53.
Duivenvoorde (2022), p. 43. The authors show that despite the liability exemptions in the E-Commerce Directive and the Digital Services Act, the Unfair Commercial Practices Directive provides significant room to hold on-line marketplaces liable.
- 54.
The Directive (EU) 2019/2161 of the European Parliament and of the Council, of 27 November 2019, amending Council Directive 93/13/EEC and Directives 98/6/EC, 2005/29/EC and 2011/83/EU of the European Parliament and of the Council as regards the better enforcement and modernisation of Union consumer protection rules. https://eur-lex.europa.eu.
- 55.
See whereas 45.
- 56.
Benedetti (2021), p. 413, analyses the implications of no longer seeking out potential customers with contractual proposals, but offering them what we already know they will accept, by means of invasive profiling against which there is a need for new forms of protection.
- 57.
See Guffanti Pesenti (2021), p. 635.
- 58.
See Smuha et al. (2021), p. 23. The authors criticise Article 5(c) of the Proposed Artificial Intelligence Act by stating that this article focuses on social behaviour or known or predicted personal or personality characteristics, while it is well documented that proxies may be employed for where such personal data is protected. These proxies could be drawn from factors not mentioned in the Article, such geographical location (postcodes, etc.). Scoring on these measures can be as discriminatory and devastating for individuals as those drawn from the characteristics included in the Proposal.
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Tommasi, S. (2023). Discrimination in the Digital Market: Protection from Different Sides. In: The Risk of Discrimination in the Digital Market. SpringerBriefs in Law. Springer, Cham. https://doi.org/10.1007/978-3-031-43640-6_4
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