Author(s): Rohith. M and Prathesaa. R
Paper Details: Volume 3, Issue 4
Citation: IJLSSS 3(4) 61
Page No: 821 – 837
ABSTRACT
In the era of digital commerce, dark patterns—deceptive user interface designs intended to manipulate user behaviour—are increasingly prevalent in India’s online ecosystem. These patterns exploit cognitive biases, nudging users towards decisions that favour business interests at the expense of consumer autonomy, privacy, and financial welfare. While such practices have attracted scrutiny under the Consumer Protection Act 2019 and the Advertising Standards Council of India (ASCI) guidelines, their antitrust implications remain underexplored within the ambit of the Competition Act 2002. This paper examines how dominant enterprises, by deploying dark patterns, may engage in behavioural exploitation amounting to abuse of dominance under Section 4, thereby distorting competition, creating artificial entry barriers, and reinforcing market power.
Drawing on examples from platforms such as Amazon, Flipkart, Zomato, Swiggy, BookMyShow, and Byju’s, alongside comparative insights from the EU and US, this study argues for a behavioural antitrust framework that integrates dark pattern detection into competition enforcement. The paper also highlights governance challenges, including the Competition Commission of India’s (CCI) limited technical expertise, jurisdictional overlaps, and litigation delays. It proposes targeted reforms, including mandatory UI/UX audits for dominant platforms, cross-agency collaboration, and algorithmic transparency obligations.
KEYWORDS
Dark patterns; Competition Act 2002; Section 4 abuse of dominance; Consumer Protection Act 2019; behavioural antitrust; UI/UX audits; digital markets regulation.
INTRODUCTION
Digital markets in India have experienced exponential growth, fuelled by rapid smartphone adoption, inexpensive internet access, and aggressive platform expansion strategies. This transformation has reshaped consumer behaviour and market dynamics but has also given rise to subtle, yet powerful, forms of consumer manipulation—dark patterns. These deceptive design choices are embedded in websites and mobile applications to steer users towards actions they might not have otherwise taken, often to the detriment of their interests.
For instance, Flipkart’s “limited time only” urgency banners, Swiggy’s pre-ticked tip options, and BookMyShow’s hidden cancellation pathways exemplify how interface design can be weaponised to drive engagement, increase revenue, or collect more personal data. While such practices are not inherently illegal, they raise profound legal and ethical concerns when deployed by dominant enterprises in a manner that entrenches market power and limits consumer choice.
Under the Competition Act 2002, particularly Section 4, dominant players are prohibited from abusing their position by imposing unfair or discriminatory conditions. While the law traditionally addresses price-based exploitation, dark patterns constitute a form of non-price exploitation—shaping market outcomes through manipulation rather than direct pricing strategies. This nuanced form of harm is under-analysed in Indian antitrust jurisprudence, despite recognition in global regulatory landscapes, such as the European Union’s Digital Services Act and the US Federal Trade Commission’s enforcement actions.
This paper contends that incorporating behavioural antitrust analysis into Indian competition law enforcement is essential to address the unique harms posed by dark patterns. By integrating behavioural economics, legal analysis, and comparative case studies, the study proposes a comprehensive regulatory strategy to counter these manipulative practices in digital markets.
LITERATURE REVIEW
Scholarship on dark patterns in India remains limited, with most academic literature focusing on human-computer interaction (HCI) and interface design rather than legal regulation. Internationally, Harry Brignull first popularised the term “dark patterns,” creating a taxonomy of tactics that exploit cognitive biases such as scarcity bias, loss aversion, and status quo bias. Building on this, Gray, Kou, Battles, Hoggatt and Toombs (2018) examined the ethical dimensions of deceptive design in HCI, while Mathur, Acar, Friedman and others (2019) conducted a large-scale audit across over 11,000 shopping websites, revealing the pervasiveness of such practices.
In India, legal scholarship has largely emerged alongside the Consumer Protection Act 2019 and subsequent policy discussions by the Department of Consumer Affairs (DoCA) and ASCI. DoCA’s 2023 Guidelines for Prevention and Regulation of Dark Patterns identify and prohibit a set of manipulative design practices, while ASCI’s discussion papers and advisories have flagged widespread use of deceptive patterns across e-commerce, travel, health-tech and gaming apps. Yet, despite these developments, the connection between dark patterns and competition law—particularly abuse of dominance under Section 4 of the Competition Act 2002—remains underexplored in Indian legal literature.
This paper fills that gap by synthesising behavioural economics, doctrinal analysis of Section 4, and comparative insights from the EU and US to propose a behavioural antitrust framework tailored to India’s platform economy.
UNDERSTANDING DARK PATTERNS: DEFINITION AND TYPOLOGY
Dark patterns are user interface (UI) and user experience (UX) design choices that intentionally steer users toward outcomes that benefit the platform at the user’s expense. They are distinct from mere persuasive design because they involve deception, obstruction, or coercion. Common categories include the “Roach Motel” (easy to enter, hard to exit), “Confirmshaming” (guilt-based nudges), “Hidden Costs,” “Forced Continuity,” “Scarcity Urgency,” and “Privacy Zuckering.” In the Indian context, these appear in subscription flows, checkout funnels, privacy permission prompts, and cancellation pathways across sectors such as e-commerce, food delivery, travel, OTT streaming, health-tech, and gaming.
DIAGRAM 1 – TYPOLOGY OF DARK PATTERNS IN INDIAN DIGITAL MARKETS
| Dark Pattern Type | Description | Indian Example |
| Roach Motel | Easy to subscribe/join; difficult to cancel/exit. | BookMyShow – hidden cancellation pathways for event tickets. |
| Confirmshaming | Guilt-tripping users into compliance. | RummyCircle – “No thanks, I don’t want to win today.” |
| Hidden Costs | Charges revealed late in the transaction. | Ola & Swiggy – surge fees or tips added at checkout. |
| Forced Continuity | Auto-renewal after “free” trials without explicit consent. | OTT platforms – auto-renew settings by default. |
| Scarcity Urgency | Artificial time/stock pressure to rush decisions. | Flipkart – “Only 1 left in stock!” banners. |
| Privacy Zuckering | Tricking users into sharing more personal data. | Health-tech apps – unnecessary health info requested. |
REGULATORY FRAMEWORK IN INDIA
Three regimes intersect over dark patterns in India: (i) competition law under the Competition Act 2002, (ii) consumer protection under the Consumer Protection Act 2019 and allied rules, and (iii) self-/co-regulation via ASCI and policy instruments issued by DoCA.
Section 4 of the Competition Act 2002 prohibits abuse of a dominant position. Although historically focused on price-based conduct, Section 4 is wide enough to capture non-price exploitation where a dominant enterprise imposes unfair or discriminatory conditions on consumers, or where its conduct amounts to denial of market access to rivals. Consumer law complements this by proscribing unfair trade practices and misleading advertisements; DoCA’s 2023 Guidelines name and ban multiple deceptive patterns. ASCI’s advisories and standards reinforce these norms in the advertising ecosystem, creating a layered governance model.
ANTITRUST IMPLICATIONS OF DARK PATTERNS UNDER SECTION 4
A behavioural antitrust lens treats manipulative interface design by dominant platforms as a form of non-price exploitation and exclusion. Three analytical steps are crucial: (1) market definition and dominance; (2) theory of harm; and (3) evidence of effects.
First, relevant market definition in digital ecosystems must consider multi-sidedness, data advantages, network effects, and switching costs. Dominance may stem from high market shares, user lock-in via defaults, and control over key interfaces (e.g., app stores, search results, buy-boxes).
Second, the theory of harm links specific dark patterns to Section 4(2) prohibitions: (a) unfair or discriminatory conditions on consumers under s 4(2)(a)(i) (e.g., roach motels in cancellation flows, forced continuity in subscriptions, default opt-ins for data sharing); and (b) denial of market access under s 4(2)(c) where interface bias (e.g., self-preferencing, buy-box design, steering) forecloses rivals.
Third, effects evidence may include: increased churn costs; degraded ability to exercise choice (measured by click-path analysis); inflated effective prices via hidden fees; or foreclosure indicators (e.g., reduced visibility of rivals in choice architecture). Remedies can involve interface conduct obligations (neutral choice screens, explicit consent, friction symmetry for entry and exit), audits of UI changes, and data-access commitments.
CASE STUDIES: INDIA, EU, AND US
India – Google Search Bias (2018): The CCI found that Google had abused its dominant position in online general web search and web search advertising by, inter alia, placing its own specialised services more prominently in search results. Although the case centred on search bias and self-preferencing rather than “dark patterns” per se, it illustrates how interface design can distort competition and user choice within the meaning of Section 4.
India – Coal India: The Commission’s abuse findings against Coal India for unfair and discriminatory conditions in fuel supply agreements highlight how Section 4 reaches exploitation beyond pricing. The case underscores enforcement challenges, including protracted litigation.
EU – Amazon Buy Box Commitments (2022): The European Commission accepted commitments from Amazon addressing concerns that its Buy Box and Prime eligibility criteria favoured its own retail business and sellers that used its logistics. The interface (buy box prominence) was central to how choice architecture could foreclose competitors, offering lessons for Indian platforms.
US – FTC’s Dark Patterns Enforcement: The FTC’s 2022 staff report, “Bringing Dark Patterns to Light,” catalogues deceptive design tactics across industries; US enforcement has increasingly targeted subscription cancellation “roach motels,” default opt-ins, and misleading consent flows. These matters provide a comparative blueprint for interface-focused remedies, disclosures, and friction symmetry rules.
DIAGRAM 2 – TIMELINE OF INDIAN REGULATORY ACTION ON DARK PATTERNS
| Date | Milestone |
| 22 Nov 2022 | ASCI publishes discussion document on deceptive patterns. |
| 13 Jun 2023 | ASCI, DoCA and stakeholders meet on prevalence of deceptive patterns. |
| 15 Jun 2023 | ASCI releases guidelines defining deceptive patterns and potential harms. |
| 30 Nov 2023 | DoCA issues “Guidelines for Prevention and Regulation of Dark Patterns, 2023.” |
| Feb 2025 | ASCI releases influence compliance scorecard; collaboration on online gaming ad risks. |
| 24 Mar 2025 | Study on deceptive patterns across top Indian apps released. |
TABLE 1 – COMPARATIVE FRAMEWORK: INDIA, EU, AND US APPROACHES TO DARK PATTERNS
| Jurisdiction | Primary Legal Basis | Regulatory Posture | Illustrative Actions |
| India | Competition Act 2002 (s 4); Consumer Protection Act 2019; DoCA 2023 Guidelines; ASCI codes | Hybrid: competition + consumer protection; emerging co-regulatory guidance | CCI Google Search bias order; DoCA dark pattern guidelines; ASCI advisories |
| EU | TFEU art 102; UCPD; GDPR; Digital Services Act | Active competition enforcement + horizontal digital regulation | Amazon Buy Box commitments (2022); consent and interface obligations under GDPR/DSA |
| US | FTC Act §5 (UDAP); state privacy statutes | Aggressive consumer protection enforcement; growing scrutiny of design practices | FTC Staff Report (2022) on dark patterns; actions on subscription cancellation flows |
CHALLENGES IN ENFORCEMENT AND GOVERNANCE
Technical Expertise: Evaluating algorithmic ranking, interface testing, and behavioural impact requires in-house or advisory expertise in HCI, data science, and experimental design. Without this, enforcement risks under- or over-deterrence.
Evidentiary Burden: Capturing UI changes over time, A/B test results, and clickstream data is essential to establish harm; discovery tools and data-access remedies matter.
Jurisdictional Overlaps: Conduct may simultaneously trigger competition, consumer protection, and data protection laws; coordinated frameworks and joint task forces (CCI–DoCA–MeitY) can reduce fragmentation.
Litigation Delays: Protracted appeals can blunt timely remedies; interim measures and conduct commitments can preserve competitive conditions.
RECOMMENDATIONS
1) Interface Conduct Guidelines under Section 4: Issue sector-agnostic guidance specifying prohibited UI practices for dominant firms (e.g., default opt-ins for sensitive data; asymmetric friction in cancellation vs subscription flows; misleading scarcity claims).
2) UI/UX and Data Audits: Mandate independent annual audits for dominant platforms covering consent flows, cancellation pathways, and fee disclosures; require publication of audit summaries.
3) Friction Symmetry Principle: Ensure that the effort to cancel equals the effort to subscribe; require one-click cancellation for recurring services.
4) Choice Architecture Neutrality: For gatekeeper interfaces (search results, app stores, buy boxes), adopt neutrality obligations and prohibit self-preferencing through deceptive design.
5) Data and A/B Test Disclosure: Require retention and disclosure of interface A/B test results and clickstream metrics to the regulator and, where appropriate, to rivals via data-sharing remedies.
6) Cross-Agency Task Force: Institutionalise a CCI–DoCA–MeitY working group for coordinated action, joint market studies, and harmonised notices.
7) Penalty Calibration and Remedies: Calibrate penalties to UI-driven gains; prefer structural and behavioural remedies that directly fix interface harms (neutral choice screens, consent dashboards).
Behavioural Economics Foundations of Dark Patterns
Dark patterns exploit well-established behavioural economics principles to influence user decisions in ways that may be contrary to their welfare. Prospect theory, developed by Daniel Kahneman and Amos Tversky, demonstrates that individuals weigh losses more heavily than equivalent gains—a bias known as loss aversion. Platforms harness this by framing potential outcomes in terms of losses (e.g., ‘Don’t miss out on this limited-time offer’) to trigger urgency.
Scarcity bias, another cognitive bias, leads consumers to assign higher value to items perceived as scarce. E-commerce platforms often exploit this by displaying false or exaggerated stock counts (‘Only 1 left!’). Anchoring effects occur when initial reference points—such as a higher ‘original’ price—are used to make a current price seem more attractive, even if the anchor is artificially inflated.
These biases are reinforced through status quo bias, where users tend to accept default settings rather than changing them. Defaults in privacy permissions or subscription auto-renewals benefit platforms at the expense of user autonomy. The systematic use of these cognitive levers aligns dark patterns closely with the concept of behavioural exploitation, making them relevant to competition law where deployed by dominant firms.
SECTOR-SPECIFIC MANIFESTATIONS OF DARK PATTERNS IN INDIA
E-commerce: Platforms such as Amazon and Flipkart have been reported to use countdown timers, hidden costs at checkout, and default add-ons (such as extended warranties) to increase transaction values. These tactics can inflate effective prices beyond initial quotes, amounting to unfair conditions under Section 4 when applied by dominant entities.
Food Delivery: Swiggy and Zomato often present pre-selected tip amounts and use subtle colour contrasts to make higher tip options more prominent. They may also bundle delivery fee discounts with loyalty subscriptions, nudging users into recurring payments.
OTT Streaming: Services like Disney+ Hotstar and Amazon Prime Video frequently employ forced continuity, where free trial users are automatically enrolled into paid subscriptions without prominent reminders or easy cancellation options.
Travel Booking: MakeMyTrip and OYO have been scrutinised for preferential listing algorithms that give undue prominence to certain properties, coupled with urgency claims such as ‘Only 2 rooms left at this price’—often without substantiation.
Ed-Tech: Companies like Byju’s and Unacademy use confirmshaming tactics in sales calls and online prompts, making parents feel guilty for not purchasing premium plans for their children’s education.
ADDITIONAL GLOBAL CASE STUDIES
UK Competition and Markets Authority (CMA): The CMA has conducted in-depth studies on ‘Online Choice Architecture’ and issued guidance warning against practices that impair consumer choice, including complex subscription cancellation processes and misleading urgency claims. In 2022, the CMA secured commitments from several companies to simplify cancellation pathways.
Australian Competition and Consumer Commission (ACCC): The ACCC’s Digital Platforms Inquiry examined the role of default settings, pre-ticked boxes, and other design features in undermining consumer choice. It recommended stronger privacy protections and penalties for misleading interface design.
Norwegian Consumer Council (NCC): In its ‘Deceived by Design’ reports (2018, 2021), the NCC analysed major tech platforms’ consent flows and found that they systematically steered users toward invasive data sharing through colour cues, misleading button labels, and emotional framing.
PROPOSED LEGISLATIVE AMENDMENTS FOR INDIA
To address the regulatory gap, India could amend the Competition Act 2002 to explicitly recognise manipulative interface design as a potential form of abuse of dominance. This could involve inserting an explanation to Section 4 defining ‘digital choice architecture’ and specifying that unfair or deceptive design practices by dominant enterprises constitute abuse.
The Consumer Protection Act 2019 could also be amended to include a dedicated chapter on dark patterns, setting out prohibited practices and penalties, similar to the DoCA’s 2023 Guidelines but with statutory force. Coordination with data protection law—particularly the Digital Personal Data Protection Act 2023—would ensure that privacy-related dark patterns are addressed alongside economic exploitation.
FUTURE ENFORCEMENT CHALLENGES
AI-Driven Personalised Dark Patterns: With the rise of AI-powered recommendation and targeting systems, dark patterns can be dynamically personalised to exploit individual vulnerabilities. For example, platforms could use behavioural data to determine when a specific user is most likely to respond to scarcity cues or upselling prompts.
Cross-Border Jurisdictional Issues: Global platforms operate across multiple jurisdictions, complicating enforcement when manipulative design choices are deployed from servers located outside India. International cooperation and participation in cross-border enforcement networks will be key.
Evidentiary Complexity: Proving the existence and effect of dark patterns may require technical capture of interface designs over time, user journey mapping, and expert behavioural analysis—capabilities that regulators must invest in.
Balancing Regulation and Innovation: Overly prescriptive design rules could stifle legitimate innovation in UI/UX. Regulators must distinguish between persuasive design that benefits users and manipulative practices that harm them.
CONCLUSION
Dark patterns represent a frontier for competition policy in India. When deployed by dominant platforms, manipulative design can entrench market power, raise effective prices, and suppress genuine consumer choice—harms that fit within Section 4’s prohibition on abuse of dominance. India’s emerging policy tools—from DoCA’s 2023 guidelines to ASCI’s advisories—provide momentum, but integrating a behavioural antitrust approach within the Competition Act’s enforcement framework is essential. By adopting interface conduct rules, mandating UI/UX audits, enforcing friction symmetry, and strengthening cross-agency coordination, India can develop a principled, technology-aware regime that protects competition and consumers alike while preserving innovation in digital markets.
Department of Consumer Affairs, ‘Guidelines for Prevention and Regulation of Dark Patterns, 2023’.
Further, within the Indian academic landscape, scholarship has also started engaging with the behavioural economics aspects of dark patterns. Some Indian legal researchers have drawn upon Thaler and Sunstein’s ‘Nudge Theory’ to distinguish between legitimate nudges that improve user decision-making and manipulative nudges, or ‘sludges’, which impair autonomy. Comparative studies highlight that in the EU, the interplay between the Unfair Commercial Practices Directive (UCPD) and the General Data Protection Regulation (GDPR) has allowed regulators to address both economic and privacy harms in dark patterns cases. In the United States, the Federal Trade Commission (FTC) has invoked Section 5 of the FTC Act alongside state-level consumer protection statutes to build cases against misleading subscription flows and data harvesting interfaces. These comparative insights can be instructive for Indian regulators, particularly in building a hybrid enforcement framework that leverages both competition and consumer protection tools.
There is also an emerging field of empirical work using controlled experiments to measure the effect of dark patterns on consumer behaviour in India. For example, UI/UX testing in mock e-commerce environments has shown that scarcity cues (‘only 1 left in stock’) and urgency timers can significantly increase purchase likelihood even when the scarcity is artificial. Similarly, privacy permission prompts framed as ‘Allow access to get rewards’ result in higher consent rates than neutral wording, raising questions about meaningful consent. These findings underscore the psychological mechanisms through which dark patterns operate and reinforce the argument that they can amount to exploitation under Section 4 when deployed by dominant enterprises.
Section 4(2) contains several specific clauses relevant to dark patterns. Clause (a)(i) prohibits imposing unfair or discriminatory conditions in the purchase or sale of goods or services; this could cover coercive consent flows, forced continuity in subscriptions, and default opt-ins for personal data processing. Clause (a)(ii) addresses predatory pricing, which may not directly relate to dark patterns but could overlap where artificially low upfront prices are paired with hidden costs revealed later in the transaction process. Clause (b)(i) addresses limiting or restricting production or technical development; certain interface manipulations could reduce innovation in competing platforms by steering users away from them. Clause (c) covers denial of market access, which can occur when interface bias—such as preferential placement of the dominant firm’s offerings—makes it materially harder for rivals to compete. Clause (e) addresses leveraging dominance in one relevant market to enter or protect another market; dark patterns that exploit dominance in a platform’s core market to push users into affiliated services could fall within this prohibition.
Additional case study – MakeMyTrip-GoIbibo and OYO (2021): The CCI imposed penalties on MakeMyTrip and GoIbibo for abuse of dominance in the online travel booking market, including preferential listing of OYO properties. While the primary finding related to exclusivity arrangements, the interface design—through ranking algorithms and default sorting—played a role in steering user choice, echoing dark pattern concerns.
Additional global example – Norwegian Consumer Council (2021): In its report ‘Deceived by Design’, the NCC analysed consent flows of Facebook, Google, and Windows 10, concluding that their design nudged users toward privacy-invasive settings. Although this was primarily a data protection matter, it has implications for competition where data accumulation fuels dominance.
Comparatively, the EU has been more proactive in embedding interface fairness into digital market regulation through the Digital Services Act (DSA) and Digital Markets Act (DMA). These instruments impose transparency and choice architecture obligations on ‘gatekeeper’ platforms, prohibiting manipulative consent designs and mandating interoperability. The US approach, while less codified, has seen the FTC develop a rich enforcement record against dark patterns via settlements, consent decrees, and public guidance. India can draw on these models by crafting a dedicated interface conduct code under the Competition Act and integrating it with sectoral regulations in e-commerce, fintech, and digital advertising.
ADDITIONAL SECONDARY SOURCES
Voigt, C., Schlögl, S., & Groth, A., ‘Dark Patterns in Online Shopping: Of Sneaky Tricks, Perceived Annoyance and Respective Brand Trust’ (16 July 2021).
Gray, C. M., Mildner, T., & Bielova, N., ‘Temporal Analysis of Dark Patterns: A Case Study of a User’s Odyssey to Conquer Prime Membership Cancellation through the Iliad Flow’ (18 September 2023).
Bongard-Blanchy, K., Rossi, A., Rivas, S., Doublet, S., Koenig, V., & Lenzini, G., ‘I am Definitely Manipulated, Even When I am Aware of it. It’s Ridiculous! – Dark Patterns from the End-User Perspective’ (26 April 2021).
Dickinson, G. M., ‘Privately Policing Dark Patterns’ (15 July 2023).
ENDNOTES (OSCOLA STYLE)
1. Competition Act 2002, s 4.
2. Consumer Protection Act 2019 (India).
3. Department of Consumer Affairs, Government of India, ‘Guidelines for Prevention and Regulation of Dark Patterns, 2023’ (30 November 2023).
4. Advertising Standards Council of India (ASCI), ‘Discussion Paper on Dark Patterns’ (2022).
5. Arunesh Mathur and others, ‘Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites’ (2019) Proceedings of the ACM on Human-Computer Interaction (CSCW).
6. Harry Brignull, ‘Dark Patterns’ (website, accessed 2025).
7. Competition Commission of India, ‘In Re: Matrimony.com Limited and Google LLC’, Case Nos 07 & 30 of 2012, Order (8 February 2018).
8. Competition Commission of India, ‘In Re: Coal India Limited’, Case No 03 of 2012.
9. European Commission, ‘Antitrust: Commission accepts commitments by Amazon concerning marketplace and Buy Box’ (Decision, 20 December 2022).
10. Federal Trade Commission, ‘Bringing Dark Patterns to Light’ (Staff Report, October 2022).
11. Regulation (EU) 2022/2065 of the European Parliament and of the Council of 19 October 2022 on a Single Market for Digital Services (Digital Services Act).
12. Advertising Standards Council of India, advisories and guidance on deceptive design practices (various).
13. Times of India, ‘These 53 popular apps across nine biggest industries are using deceptive patterns: Advertising Standards Council of India’ (news report).
14. Voigt, C., Schlögl, S., & Groth, A., ‘Dark Patterns in Online Shopping: Of Sneaky Tricks, Perceived Annoyance and Respective Brand Trust’ (16 July 2021).
15. Gray, C. M., Mildner, T., & Bielova, N., ‘Temporal Analysis of Dark Patterns: A Case Study of a User’s Odyssey to Conquer Prime Membership Cancellation through the Iliad Flow’ (18 September 2023).
16. Bongard-Blanchy, K., Rossi, A., Rivas, S., Doublet, S., Koenig, V., & Lenzini, G., ‘I am Definitely Manipulated, Even When I am Aware of it. It’s Ridiculous! – Dark Patterns from the End-User Perspective’ (26 April 2021).
17. Dickinson, G. M., ‘Privately Policing Dark Patterns’ (15 July 2023).
BIBLIOGRAPHY
Primary Sources
Competition Act 2002 (India).
Consumer Protection Act 2019 (India).
Regulation (EU) 2022/2065 (Digital Services Act).
Competition Commission of India, ‘In Re: Matrimony.com Limited and Google LLC’, Case Nos 07 & 30 of 2012, Order (8 February 2018).
Competition Commission of India, ‘In Re: Coal India Limited’, Case No 03 of 2012.
European Commission, Amazon commitments decision (20 December 2022).
Secondary Sources
Arunesh Mathur and others, ‘Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites’ (2019) Proc ACM HCI (CSCW).
Federal Trade Commission, ‘Bringing Dark Patterns to Light’ (2022).
Harry Brignull, ‘Dark Patterns’ (website).
ASCI, ‘Discussion Paper on Dark Patterns’ (2022).