Open Access Research Article
White Black Legal – International Law Journal · ISSN 2581-8503
MISLEADING AND UNFAIR TRADE PRACTICES IN PLATFORM-BASED SERVICES
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Abstract
MISLEADING
AND UNFAIR TRADE PRACTICES
IN PLATFORM-BASED SERVICES
IN PLATFORM-BASED SERVICES
Study of Consumer Autonomy, Digital
Choice Architecture and Platform Accountability
AUTHORED BY - AVDHESH
MISHRA:
Research Scholar: S.N.
College of Law – Nirwan University-Jaipur
CO-AUTHOR - PROF. (DR.)
RAKESH DAIYA
Principal & Dean:
S.N. College of Law – Nirwan University-Jaipur
Abstract
Platform-based
services have transformed consumer transactions from relatively linear
exchanges into data-intensive, multi-actor environments in which search
ranking, interface design, targeted advertising, recommender systems, reviews,
automated pricing and standard-form digital contracts shape the consumer's
decision before the legal act of purchase is completed. This article examines
whether conventional doctrines of MISLEADING and unfair trade practice can
adequately respond to this transformation, with India as the principal
jurisdiction and the European Union, United Kingdom and United States used for
comparison. The study adopts a doctrinal-comparative method supported by
peer-reviewed literature on dark patterns, deceptive reviews, algorithmic pricing
and platform governance. It argues that digital deception should not be
confined to provably false words. A legally relevant MISLEADING may arise from
an overall transactional architecture where design, omission, ranking or
personalization materially distorts the consumer's understanding or choice. The
Indian Consumer Protection Act, 2019, Consumer Protection (E-Commerce) Rules,
2020, CCPA advertising guidelines and the Guidelines for Prevention and
Regulation of Dark Patterns, 2023 provide an increasingly coherent consumer-law
response, while the Information Technology Act, 2000 continues to govern
conditional intermediary safe harbour. The Digital Personal Data Protection
Act, 2023 and Digital Personal Data Protection Rules, 2025 add an important but
phased data-governance layer. Comparative analysis shows that the EU Digital
Services Act moves most clearly toward interface-level accountability, while
the United States retains a broad deception model under Section 5 of the
Federal Trade Commission Act. The article proposes a control-and-contribution
test for platform accountability, auditable design duties for high-impact
interfaces, verifiable review systems, transparent total-price rules and
coordinated consumer-data enforcement. The central claim is that platform law
should protect not only informational accuracy but the integrity of the
consumer decision-making environment.
Keywords: Misleading;
unfair trade practices; platform economy; e-commerce; dark patterns; consumer
autonomy; fake reviews; algorithmic pricing; intermediary liability; digital
consumer protection.
1. Introduction
Digital
commerce is no longer adequately described as the electronic equivalent of a
physical sale. A contemporary platform does more than host an offer. It
determines which products are visible, how alternatives are ranked, when
urgency is displayed, whether a review appears credible, what information is
highlighted, how difficult cancellation will be and, increasingly, what price
or promotional message a particular consumer sees. The legal character of the
transaction therefore depends not only on statements made by a seller but also
on the architecture through which the platform structures attention and choice.
This shift is especially important for the law of MISLEADING and unfair trade
practices because both fields are concerned with the quality of the information
and influence that precede consent.
Traditional
MISLEADING doctrine is built around identifiable representations, inducement
and consent. In India, Sections 17 and 18 of the Indian Contract Act, 1872
distinguish fraud from MISLEADING, while Section 19 addresses voidability where
consent has been caused by coercion, fraud or MISLEADING. These principles
remain important, yet platform transactions create a harder attribution
problem. The consumer may be influenced by a scarcity banner generated by the
platform, a seller-supplied product description, an algorithmically ordered
review list, a sponsored recommendation that resembles neutral content, and a
preselected add-on presented in the same transaction. A legal framework that
asks only whether one sentence was false risks overlooking the cumulative
effect of the digital choice environment.
Consumer
law has begun to respond to this gap. The Consumer Protection Act, 2019
recognizes unfair trade practices and misleading advertisements and establishes
the Central Consumer Protection Authority (CCPA). The Consumer Protection
(E-Commerce) Rules, 2020 impose duties on e-commerce entities, marketplace
entities and sellers. The CCPA's 2022 Guidelines on misleading advertisements
and its 2023 Guidelines on dark patterns move regulation closer to the
interface itself. The 2023 Guidelines identify specified patterns such as false
urgency, basket sneaking, confirm shaming, forced action, subscription traps,
interface interference, bait and switch, drip pricing and disguised
advertisement. Their significance is doctrinal as well as regulatory: they
accept that deception may be embedded in design rather than expressed through a
conventional verbal representation.
The
empirical literature supports this regulatory concern. Mathur et al. (2019), in
a large-scale crawl of approximately 11,000 shopping websites, identified 1,818
instances of dark patterns across multiple categories and found third-party
entities offering such techniques as services. Luguri and Strahilevitz (2021)
experimentally demonstrated that several dark-pattern techniques can materially
alter consumer decisions. Brenncke (2024) places the problem within a theory of
behavioural exploitation and consumer autonomy, arguing that digital choice
architecture can interfere with reflective decision-making even when formal
freedom of choice remains. Recent work on algorithmic pricing also shows that
dynamic pricing can reduce trust and extend consumer search (Vomberg, Homburg
& Sarantopoulos, 2025), while research on deceptive reviews demonstrates
that review integrity directly affects brand evaluation, search visibility and
purchasing behaviour.
These
developments create a central research problem. Platform-based services combine
characteristics of intermediaries, marketplaces, advertisers, data processors,
payment facilitators and, in some models, sellers. The platform may disclaim
responsibility for third-party content while simultaneously shaping the
commercial meaning of that content through ranking, badges, fulfilment labels,
review moderation and personalized display. Safe-harbour doctrine was never
designed to become a general immunity from independent consumer-law
obligations. A legally defensible framework must therefore distinguish
liability for merely hosting third-party information from liability arising
from the platform's own representation, design choice, commercial intervention
or failure to perform a statutory duty.
This
article makes three contributions. First, it develops an integrated account of
digital MISLEADING that links contract doctrine with unfair-trade-practice
regulation and behavioural evidence. Second, it proposes a functional approach
to platform responsibility based on control, contribution and consumer reliance
rather than the formal label attached to the platform. Third, it places the
Indian framework in comparative perspective with the EU Digital Services Act
(DSA), the EU Artificial Intelligence Act, UK consumer law and the US Federal
Trade Commission's deception standard. The resulting argument is not that every
persuasive interface is unlawful. Commercial persuasion is ordinary market
conduct. The legal threshold should instead focus on whether a representation,
omission or design practice materially distorts the consumer's capacity to make
a free and informed transactional decision or creates a misleading overall
impression that the responsible actor could reasonably prevent or correct.
The
timing of the inquiry is also significant. India has moved beyond a purely
sectoral approach to digital consumer problems. At the same time, the Digital
Personal Data Protection Act, 2023 and Digital Personal Data Protection Rules,
2025 are being brought into force on a phased schedule. As of September 2026,
the commencement notification of November 2025 has activated specified
institutional and miscellaneous provisions, while major substantive processing
obligations are scheduled for later phases. Any analysis that treats the entire
DPDP regime as fully operative today would therefore be legally imprecise. This
article uses the data-protection framework as part of the emerging regulatory
architecture while distinguishing enacted norms from provisions whose
commencement is deferred.
2. Research Questions, Objectives and Methodology
The
article addresses four research questions: (i) how should MISLEADING be
conceptualized when deception is produced by interface design, ranking,
personalization or omission rather than a single false statement; (ii) to what
extent do Indian contract and consumer-protection laws regulate such practices;
(iii) when should a platform bear responsibility for seller-originated or
algorithmically amplified deception; and (iv) which comparative regulatory
techniques can strengthen Indian law without converting intermediaries into
insurers of every third-party act? The objectives are correspondingly
doctrinal, evaluative and reform-oriented.
The
research uses a doctrinal-comparative method. Primary Indian materials include
the Indian Contract Act, 1872; Consumer Protection Act, 2019; Consumer
Protection (E-Commerce) Rules, 2020; Information Technology Act, 2000;
Information Technology (Intermediary Guidelines and Digital Media Ethics Code)
Rules, 2021; the 2022 misleading-advertising guidelines; the 2023 dark-pattern
guidelines; and the DPDP Act and Rules. Judicial materials are used selectively
to explain intermediary safe harbour, platform participation and the
relationship between statutory duties and online facilitation. Comparative
primary materials include Regulation (EU) 2022/2065 (Digital Services Act),
Directive 2005/29/EC on unfair commercial practices, Regulation (EU) 2024/1689
(Artificial Intelligence Act), UK consumer law and the US Federal Trade
Commission Act.
The
literature component is designed in a Scopus-oriented manner: priority is given
to recent peer-reviewed work from established journals and conference
proceedings addressing online choice architecture, consumer autonomy, fake
reviews, algorithmic pricing and platform governance. The purpose is not a
statistical meta-analysis and no claim is made that the search reproduces the
proprietary Scopus database exhaustively. Instead, the reviewed studies are
used to identify empirically documented forms of consumer influence and to test
whether legal doctrine is capable of recognizing those forms. This distinction
matters because a doctrinal article should not convert behavioural evidence
into legal conclusions without identifying the normative step that connects the
two.
The
analytical method applies five criteria to each practice: source of the
representation or influence; degree of platform control; materiality to the
consumer decision; transparency and verifiability; and availability of an
effective remedy. These criteria permit comparison across seemingly different
practices such as false scarcity, fake reviews and dynamic pricing. They also
support a more disciplined approach to platform liability than a binary
distinction between 'publisher' and 'neutral intermediary'.
3. Literature Review: From False Statements to
Manipulative Choice Architecture
The
first strand of relevant literature concerns dark patterns and online choice
architecture. Mathur et al. (2019) demonstrated that manipulative interface
practices are not isolated design accidents but observable patterns at scale.
Their taxonomy and large empirical dataset helped shift scholarly attention
from individual instances of poor design to systematic techniques capable of
steering consumers. Luguri and Strahilevitz (2021) then provided experimental
evidence that certain dark patterns materially affect consumer behaviour. Their
findings are legally significant because materiality and inducement are central
concepts in both deception law and MISLEADING doctrine. Where a design
intervention reliably changes a transactional decision, a regulator has a
stronger basis for treating the practice as more than mere aesthetic
persuasion.
Brenncke
(2024) adds a normative dimension. Rather than reducing consumer harm to
economic welfare alone, the analysis treats behavioural exploitation as an
interference with autonomy. This is especially useful for platform law because
many dark patterns do not remove formal choice. The consumer can technically
refuse a preselected option, find the cancellation route or ignore a repeated
prompt. The concern is that the interface deliberately increases friction for
one option and decreases it for another in order to exploit predictable biases.
The legal question then becomes whether formal click-based consent should carry
the same weight when the platform has engineered the pathway to secure it.
Koh
and Seah (2023) provide additional experimental support from e-commerce
settings, finding that multiple dark-pattern techniques can influence product
selection and that limited-time messaging can be particularly effective. Their
study also indicates that vulnerability may not be evenly distributed across
consumers. For legal analysis, this reinforces two propositions: first,
interface interventions can have measurable transactional effects; second, the
reasonable-consumer inquiry should remain sensitive to contexts in which
particular groups are predictably more susceptible to pressure or confusion.
A
second strand concerns online reviews and reputation systems. Harrison-Walker
and Jiang (2023) show that suspicion of fake reviews affects attitudes toward
reviewers, brands and websites. Yin, Bond and Zhang (2025) examine consumers'
truth bias in judging review authenticity, demonstrating that consumers may
systematically over-credit apparently genuine reviews. Jin, Yang and Hosanagar
(2022) study 'brushing', where merchants create fake orders to improve ranking,
showing how manipulation of platform metrics can affect search visibility even
before a consumer reads a review. These findings broaden the legal conception
of deception. The unfair advantage may arise not merely because a fake review
contains a false opinion, but because fabricated transaction signals alter the
ranking system that determines what consumers encounter.
Technical
research on fake-review detection similarly confirms that review integrity is a
platform-governance problem. Deshai and Bhaskara Rao (2023) and other
computational studies develop detection techniques based on content, behaviour
and relational features. The existence of such tools does not imply that every
undetected fake review proves negligence. It does, however, weaken claims that
platforms are technologically incapable of taking proportionate preventive
measures. A reasonable-duty inquiry can therefore consider the availability,
cost and reliability of detection mechanisms alongside the scale and risk
profile of the platform.
A
third strand addresses algorithmic pricing. Dynamic pricing is economically
legitimate in many settings: prices may reflect demand, inventory, time,
capacity or competitor activity. Legal concern arises when consumers are misled
about the nature of the price, when additional mandatory charges are hidden
until a late stage, when false urgency is coupled with personalized pricing, or
when sensitive data are used contrary to applicable law. Vomberg et al. (2025)
find that algorithmic dynamic pricing can reduce consumer trust and increase
search duration, even though adaptation may occur over time. Spann et al.
(2026) situate algorithmic pricing within both marketing strategy and
regulation, highlighting the need to distinguish efficiency-enhancing pricing from
manipulative or collusive applications.
A
fourth strand concerns platform liability and the changing boundary between
intermediary and market organizer. Aade (2025), examining social-media commerce
under the DSA, observes that social platforms increasingly function as
commercial marketplaces. The conceptual difficulty is familiar in India: a
service may claim intermediary status while offering integrated payments,
advertising, shopping tags, seller verification, recommendation tools and
transaction support. The more a platform designs the commercial encounter, the
less persuasive a purely passive characterization becomes for duties that arise
from its own conduct.
Taken
together, the literature reveals a gap in legal analysis. Computer science and
behavioural research can identify manipulative design; marketing research can
measure trust and search effects; information-systems research can study
ranking manipulation; and consumer-law scholarship can theorize autonomy. Yet
MISLEADING doctrine often remains organized around a relatively discrete
statement made by an identifiable contracting party. The platform economy
requires a bridge between these literatures. That bridge should not abandon
established legal elements. It should reinterpret representation, inducement,
materiality and responsibility in light of how digital transactions are
actually structured.
Table
1. Selected Peer-Reviewed Literature and Its Legal Relevance
|
Study
|
Field
/ Source
|
Principal
contribution
|
Legal
relevance
|
|
Mathur
et al. (2019)
|
HCI
/ Proceedings of the ACM on Human-Computer Interaction
|
Large-scale
identification and taxonomy of dark patterns on shopping sites
|
Supports
prevalence, foreseeability and design-based deception analysis
|
|
Luguri
& Strahilevitz (2021)
|
Journal
of Legal Analysis
|
Experimental
evidence on effectiveness of dark patterns
|
Links
design interventions to material consumer decisions and consent
|
|
Brenncke
(2024)
|
Journal
of Consumer Policy
|
Autonomy-based
theory of behavioural exploitation
|
Provides
normative threshold for regulating manipulative choice architecture
|
|
Harrison-Walker
& Jiang (2023)
|
Journal
of Business Research
|
Consumer
cues and consequences of suspected fake reviews
|
Shows
reputational and purchasing consequences of review deception
|
|
Yin,
Bond & Zhang (2025)
|
Information
Systems Research
|
Truth
bias and authenticity judgments in online reviews
|
Supports
stronger review-integrity and provenance duties
|
|
Vomberg
et al. (2025)
|
International
Journal of Research in Marketing
|
Trust
and search effects of algorithmic dynamic pricing
|
Distinguishes
legitimate dynamic pricing from transparency/fairness risks
|
|
Aade
(2025)
|
Computer
Law & Security Review
|
Consumer
protection in social-media commerce under DSA
|
Shows
convergence of social networks and marketplaces and need for functional
platform duties
|
4. Reconstructing MISLEADING for Platform-Based
Services
MISLEADING
in platform transactions should begin with the conventional requirement of a
representation or legally relevant misleading conduct. Section 18 of the Indian
Contract Act addresses positive assertions not warranted by information, breach
of duty that gains an advantage by misleading another, and innocent causation
of mistake as to the substance of the subject matter. Section 17 separately
defines fraud, including active concealment and promises made without intention
of performing them. The distinction between fraudulent and non-fraudulent
MISLEADING remains important because intention, proof and remedies differ.
Digitalization does not erase these elements; it changes the form in which the
misleading impression may be produced.
A
representation should therefore be understood functionally. A prominent 'Only
one room left' banner communicates a factual proposition even if generated
automatically. A crossed-out reference price communicates a comparison. A
platform badge labelled 'verified' communicates something about authentication.
A default add-on positioned as necessary may communicate an implication even
where the text is technically qualified. The legal system has long recognized
that half-truths, context and conduct can matter. Platform design simply
increases the range of non-verbal and composite signals from which a reasonable
consumer forms an understanding.
Materiality
offers the necessary limiting principle. Not every visual asymmetry should
trigger legal liability. A practice becomes legally serious when it is capable
of affecting a transactional decision concerning whether, with whom, on what
terms or at what price a consumer contracts. This criterion aligns the Indian
consumer-law concept of misleading commercial conduct with the US FTC's
long-standing focus on representations, omissions or practices likely to
mislead consumers acting reasonably where the matter is material. It also
resembles the EU emphasis on practices that materially distort or impair free
and informed decisions.
Inducement
must also be adapted carefully. In a conventional bilateral transaction, the
claimant may identify a statement and testify that it induced the contract. In
a high-volume platform, a particular representation may be one element of a
sequence: ranking creates visibility, a review creates trust, a scarcity signal
creates urgency and a preselected option affects price. Courts and regulators
should permit proof of inducement through the transaction as a whole, supported
where appropriate by interface records, A/B testing evidence, internal metrics
and behavioural data. This does not reverse the burden of proof automatically;
it recognizes that platforms themselves often possess the best evidence of how
interface interventions are designed and measured.
The
concept of consumer autonomy adds a second limiting principle. The objective is
not to forbid persuasion. A well-designed interface may legitimately reduce
complexity and help consumers decide. The legal concern is asymmetric
manipulation: the platform intentionally or foreseeably makes the commercially
preferred option easier to select while making refusal, cancellation or price
comprehension materially harder. Where design interferes with the consumer's
ability to translate genuine preferences into action, formal assent becomes
less persuasive as evidence of meaningful consent.
5.
Indian Legal Framework Governing Digital MISLEADING and Unfair Trade Practices
5.1 Consumer Protection Act, 2019 and the E-Commerce Rules
The
Consumer Protection Act, 2019 is the principal statutory anchor for unfair
digital trade practices. Section 2(47) defines 'unfair trade practice' broadly
and includes false representations concerning quality, standard, sponsorship,
approval, price and other characteristics, as well as misleading conduct
associated with the promotion or supply of goods and services. The Act also
defines misleading advertisements and gives the CCPA market-wide powers that go
beyond the resolution of a single bilateral dispute. This architecture is
particularly suitable for platform harms because a single interface practice
may affect thousands or millions of consumers even where the monetary injury to
each individual is small.
The
Consumer Protection (E-Commerce) Rules, 2020 recognize the institutional role
of e-commerce entities. They apply duties concerning grievance redress,
transparency and information, while separate provisions address marketplace
entities, sellers and inventory models. The legal importance of the Rules lies
in their functional differentiation. A marketplace may not be the seller of
every product, but that fact does not eliminate duties imposed on it in its
capacity as an e-commerce entity. Conversely, the presence of platform duties
does not automatically make the marketplace liable for every defect or
MISLEADING created solely by a seller. Liability must be tied to the specific
statutory obligation or the platform's own conduct.
The
Rules also challenge the notion that platform disclaimers can settle liability
in advance. Consumer law is mandatory in important respects. A contractual term
describing the platform as a mere facilitator cannot negate a statutory
obligation to provide specified information, maintain grievance mechanisms or
avoid unfair conduct. Where a platform independently makes a
representation—such as a verification claim, fulfilment guarantee, ranking
label or sponsored endorsement—that representation should be assessed as the
platform's own, even if the underlying product is supplied by a third party.
5.2 Misleading Advertisements and Endorsements
The
CCPA's Guidelines for Prevention of Misleading Advertisements and Endorsements
for Misleading Advertisements, 2022 strengthen the regulatory treatment of
digital promotion. They require advertising claims to satisfy conditions of
truthfulness and substantiation and address bait advertisements, free-claim
advertisements and endorsements. This is important for platform services
because the distinction between editorial content, influencer communication and
paid promotion is often visually blurred. A sponsored result that resembles an
organic recommendation can mislead not because the product claim is false but
because the commercial relationship is concealed or insufficiently prominent.
Digital
advertising also raises attribution issues. Advertisers, endorsers, agencies
and platforms may each contribute to the final message. Consumer law should
therefore distinguish the originator of the substantive claim from the actor
that controls its presentation. A platform that simply transmits an
advertisement under a transparent advertising system occupies a different
position from a platform that rewrites claims, assigns credibility badges,
suppresses sponsorship disclosures or targets vulnerable groups through
data-driven optimization. The appropriate legal response is actor-specific
rather than collective liability without fault.
5.3 Dark Patterns Guidelines, 2023
The
2023 Dark Patterns Guidelines are among the most important Indian developments
for this field. Issued under the CCPA's statutory authority, the Guidelines
identify thirteen specified dark patterns and treat manipulative design as a
consumer-protection issue. The list includes false urgency, basket sneaking,
confirm shaming, forced action, subscription traps, interface interference,
bait and switch, drip pricing, disguised advertisement and other practices. The
significance of the Guidelines is not merely the taxonomy. They articulate a
principle that the design and choice architecture of a digital interface can
amount to an unfair trade practice where it subverts or impairs consumer
autonomy, decision-making or choice.
False
urgency illustrates the convergence between MISLEADING and design. If a
platform displays a fabricated countdown timer or scarcity message, the
practice contains a factual MISLEADING and a behavioural nudge. Drip pricing
illustrates a different mechanism: the initial price may be literally true but
incomplete in a way that prevents meaningful comparison until the consumer has
invested time in the transaction. Subscription traps and difficult cancellation
show that unfairness can arise after contract formation, where exit friction is
deliberately made disproportionate to entry. A coherent legal framework should
therefore regulate the lifecycle of the digital transaction, not merely the
pre-contract advertisement.
The
Guidelines also help resolve a common objection that 'nudges' are too
subjective to regulate. The law need not determine whether every interface is
psychologically manipulative in the abstract. It can identify objective design
features: default selections, hidden mandatory charges, asymmetrical
cancellation steps, repeated prompts after a decision, false stock indicators
and disguised advertising. Evidence of consumer impact and internal design
intent can then inform enforcement priority and penalty, but basic illegality
may be established from the practice itself where the guideline is sufficiently
specific.
5.4 Information Technology Act, 2000 and Intermediary Safe
Harbour
Section
79 of the Information Technology Act, 2000 provides conditional safe harbour
for intermediaries in relation to third-party information, subject to statutory
requirements. The provision is essential to the functioning of online services
because automatic liability for every third-party statement would produce
excessive monitoring and removal. Yet safe harbour should be kept conceptually
separate from independent duties. If liability is alleged because a marketplace
itself displayed a misleading verification badge, designed an unlawful dark
pattern or failed to satisfy a consumer-rule obligation, the claim is not
necessarily one 'for third-party information' merely because a third-party
seller participated in the transaction.
Shreya
Singhal v. Union of India, (2015) 5 SCC 1, remains central to the
interpretation of 'actual knowledge' in Section 79(3)(b), which the Supreme
Court read down in the context of unlawful content to knowledge through a court
order or notification by the appropriate government or agency. The judgment
protects intermediaries from being required to adjudicate the legality of all
user content on private notice alone. Consumer-platform disputes, however, may
involve different questions: statutory disclosure duties, contractual
representations, product listing systems and platform-created transaction
architecture. Shreya Singhal should therefore not be transformed into a general
proposition that intermediaries owe no consumer-facing duties until a court
order arrives.
Christian
Louboutin SAS v. Nakul Bajaj, 2018 SCC OnLine Del 12215, is frequently
discussed because the Delhi High Court examined the extent of an e-commerce
platform's participation in the sale process when considering intermediary
protection. The case arose in an intellectual-property setting and should not
be mechanically transplanted into consumer law, but its functional inquiry is
useful: the more a platform contributes to the commercial transaction and
representation, the more carefully courts should identify which conduct is
genuinely third-party content and which is the platform's own activity. Google
India Pvt. Ltd. v. Visakha Industries, (2020) 4 SCC 162, similarly confirms
that Section 79 is a statutory safe-harbour regime whose conditions and temporal
application matter; it is not a freestanding immunity detached from the text of
the Act.
5.5 Digital Personal Data Protection: Relevance and Current
Commencement
Data
processing is increasingly inseparable from digital MISLEADING because
personalization determines what a consumer sees, in what sequence and sometimes
at what price. The Digital Personal Data Protection Act, 2023 creates a
statutory framework for digital personal data, and the Digital Personal Data
Protection Rules, 2025 provide implementation details. The 2025 commencement
notification, however, staggers the entry into force of the Act. As of
September 2026, specified provisions concerning definitions, the Data
Protection Board and miscellaneous matters have commenced, while substantial
processing obligations are scheduled for later dates under the notification.
The same phased structure appears in the Rules, with only specified rules
immediately effective and others commencing after one year or eighteen months.
This
phased status matters for legal scholarship. It is tempting to state that a
personalized dark pattern already violates every consent and data-fiduciary
obligation in the DPDP Act, but such a claim may be premature before the
relevant section commences. A more accurate position is that data-protection
legislation establishes the direction of Indian digital governance and will
increasingly interact with consumer law as substantive provisions come into
force. Until then, consumer law, contract law, existing information-technology
duties and sectoral rules remain central to deceptive platform design.
Table 2. Indian Legal
Framework and Platform Conduct
|
Instrument
|
Relevant
subject
|
Platform
implication
|
|
Indian
Contract Act, 1872, ss. 17-19
|
Fraud,
MISLEADING and voidability
|
Applies
to consent induced by materially misleading representations or conduct
|
|
Consumer
Protection Act, 2019
|
Unfair
trade practices, misleading advertisements, CCPA powers
|
Supports
individual and market-wide remedies against deceptive digital conduct
|
|
Consumer
Protection (E-Commerce) Rules, 2020
|
Duties
of e-commerce entities, marketplaces and sellers
|
Creates
platform-specific disclosure and grievance obligations
|
|
CCPA
Misleading Advertisement Guidelines, 2022
|
Truthfulness,
substantiation and endorsements
|
Relevant
to sponsored results, influencer promotion and platform advertising
|
|
CCPA
Dark Patterns Guidelines, 2023
|
Manipulative
interface design
|
Directly
addresses choice architecture such as false urgency, drip pricing and
subscription traps
|
|
Information
Technology Act, 2000, s.79
|
Conditional
intermediary safe harbour
|
Protects
qualifying third-party hosting but does not erase independent platform duties
|
|
DPDP
Act, 2023 & Rules, 2025
|
Digital
personal data; phased commencement
|
Emerging
relevance to profiling, personalization and data-driven consumer influence
|
6. Principal Forms of MISLEADING and Unfair Trade
Practice on Platforms
6.1 False Urgency, Scarcity and Reference Pricing
Scarcity
and urgency claims illustrate the clearest overlap between conventional
MISLEADING and behavioural design. Statements such as 'two seats left', 'twenty
people are viewing this room' or 'offer ends in five minutes' can affect
purchasing speed and reduce comparison. Where the statement is fabricated or
materially misleading, the issue is not merely that the consumer was nudged; a
factual proposition has been communicated. Automated generation does not remove
responsibility. The legally relevant inquiry is who designed or supplied the
rule, whether the data supporting the claim existed, whether the representation
was reasonably verifiable and whether it was material to the transaction.
Reference
pricing raises a related problem. A crossed-out 'original price' may imply that
the product was genuinely offered at that higher price or that the reduction is
meaningful. If the reference price is artificial, stale or selectively
constructed, the consumer receives a misleading comparative signal. Platforms
can reduce this risk by requiring sellers to substantiate reference prices and
by retaining price-history data for enforcement. Such measures are technically
feasible in a digital environment and may be less burdensome than post-hoc
litigation over millions of transactions.
6.2 Drip Pricing and Hidden Mandatory Charges
Drip
pricing occurs when a consumer is attracted by an incomplete headline price and
mandatory or practically unavoidable charges are disclosed progressively. The
harm is not simply surprise at checkout. Late disclosure distorts comparison
because the consumer may have already invested time, disclosed data or become
psychologically committed to the transaction. The 2023 Dark Patterns Guidelines
expressly identify drip pricing as a dark pattern in specified circumstances. A
strong legal rule should require the total unavoidable price to be displayed
sufficiently early for meaningful comparison, while variable charges should be
explained through objective criteria rather than hidden until the final screen.
6.3 Fake Reviews, Review Suppression and Ranking Manipulation
Reviews
have become a substitute for physical inspection and personal recommendation.
Their informational value depends on authenticity, independence and reasonably
neutral presentation. Fake positive reviews, coordinated negative attacks,
undisclosed incentivized reviews and suppression of legitimate criticism can
each create a misleading impression. The legal wrong is compounded when
platform ranking systems convert manipulated reviews into increased visibility,
making deception self-amplifying.
A
proportionate platform duty should focus on systems rather than perfect
outcomes. Large marketplaces should maintain reasonable verification, anomaly
detection, conflict-of-interest rules and disclosure mechanisms for
incentivized reviews. Consumers should be told whether a reviewer completed a
verified transaction and whether any material benefit was provided for the
review. Platforms should not claim that reviews are 'verified' unless the
verification standard is clear and meaningful. Where an algorithm determines
which reviews appear first, the platform should also avoid designs that
systematically suppress representative negative information while presenting
the resulting score as neutral.
The
problem of fake orders or 'brushing' demonstrates why consumer deception can
occur upstream of the visible review. Fabricated transactions may improve
product ranking and generate a veneer of popularity. A narrow content-based
approach that removes only obviously false text will miss this structural
manipulation. Platform governance therefore requires integrity controls for
transaction signals as well as for written reviews.
Recent
platform research strengthens this point. Schneider and Teubner (2024) examine
fake reviews in closed reputation systems and show that transaction-linked
review systems can still face credibility problems, especially during early
reputation formation. Gu, Cao and Fang (2026) find that manipulated reviews can
affect product market performance even when platforms later filter them. The
regulatory implication is that ex post deletion alone may be insufficient where
ranking and visibility effects occur before detection; platforms need
preventive controls, rapid correction and mechanisms capable of addressing
downstream ranking consequences.
6.4 Sponsored Rankings, Native Advertising and Influencer
Commerce
A
search result can mislead by omission when paid placement is presented in a
form substantially similar to an organic result. The consumer may reasonably
assume that higher placement reflects relevance, quality or popularity rather
than payment. Clear, proximate and visually meaningful sponsorship disclosure
is therefore necessary. The same principle applies to influencer and creator
commerce. A disclosure hidden among hashtags or placed after an expanded
caption may be legally inadequate if the overall presentation suggests
independent opinion. The 2022 CCPA Guidelines provide a statutory basis for
evaluating endorsements, while the platform's own advertising architecture may
create additional responsibility where it controls labels and placement.
6.5 Algorithmic Dynamic Pricing and Personalization
Algorithmic
pricing should not be treated as inherently deceptive. Airlines, hotels, ride
services and retailers have long adjusted prices based on capacity, timing and
demand. The legal risk emerges where personalization is opaque in a way that
creates a false impression of a universal or time-limited price, where a
consumer is shown fictitious discounts, or where the system uses data in a
legally impermissible manner. The empirical finding that algorithmic pricing
can reduce trust does not itself establish unlawfulness; distrust is a market
response, while deception requires a misleading representation, omission or
practice or another applicable statutory violation.
A
workable regulatory approach should therefore distinguish dynamic pricing from
deceptive price architecture. Platforms should disclose the total price and
material mandatory components, avoid false scarcity, explain material
personalization where legally required or necessary to prevent a misleading
impression, and retain audit records for high-impact pricing systems.
Competition law may address collusive or parallel-pricing concerns, while
consumer law should focus on transparency, fairness of presentation and
misleading individualized claims.
6.6 Subscription Traps, Confirm Shaming and Asymmetric
Friction
Subscription
traps reveal why post-contract design matters. A consumer may sign up through
one click but face multiple menus, retention prompts, hidden links or mandatory
telephone calls to cancel. The DSA expressly identifies making termination more
difficult than subscription as a paradigmatic dark-pattern concern, and India's
2023 Guidelines similarly address subscription traps and interface
interference. The legal principle should be symmetry of material transactional
friction: businesses may confirm a cancellation or explain consequences, but
they should not deliberately convert exit into an obstacle course designed to
defeat the consumer's previously expressed decision.
7. A Functional Test for Platform Accountability
The
central liability question is not whether platforms are always publishers or
always neutral conduits. Both propositions are too crude. Platform
responsibility should be assessed through a functional test based on control,
contribution and reliance. 'Control' asks whether the platform could determine
the design, ranking, label, disclosure or transaction process at issue.
'Contribution' asks whether the platform created, materially modified,
amplified or commercially integrated the misleading element. 'Reliance' asks
whether the platform presented itself to consumers as performing a
verification, guarantee, curation or trust function that reasonably influenced
the transaction.
This
test preserves safe harbour for genuinely third-party information while
recognizing independent platform conduct. If a seller uploads a false product
description and the platform merely stores it subject to statutory due
diligence, the seller is the primary source of the representation. If the
platform adds a 'verified authentic' badge without a reasonable verification
process, the badge is the platform's own representation. If the platform
designs a ranking system that knowingly rewards fake orders or offers sellers
paid tools that manufacture false scarcity, contribution and control increase.
Liability can therefore be calibrated rather than assumed.
A
second principle is proportionality. A small classified-ad service cannot be
expected to deploy the same detection infrastructure as a dominant marketplace
processing millions of transactions. Duties should reflect scale, risk,
technical capacity and the platform's role. However, scale can also increase
responsibility because large platforms benefit from extensive behavioural data,
centralized interface control and sophisticated experimentation. A firm that
can optimize a checkout flow at massive scale can often implement compliance
interventions at the same scale.
A
third principle is evidentiary accountability. Digital platforms routinely
conduct A/B tests, maintain event logs and measure conversion. These records
can be highly relevant where the legality of a design practice depends on
whether it was intended or known to distort choice. Regulators should be
empowered, subject to confidentiality safeguards, to require production of
relevant design and experimentation records in serious investigations. Courts
may also draw appropriate evidentiary consequences where a platform fails to
preserve records that it was legally required to maintain.
A
fourth principle is remedy matching. Deceptive interface cases often involve
small individual losses but large aggregate gains. Individual damages alone may
under-deter. Effective remedies may include cessation orders, corrective
disclosures, refund programmes, interface redesign, administrative penalties,
publication of enforcement outcomes and, where legally available, compensation.
Online dispute resolution can complement these tools for transaction-specific
disputes, while the CCPA is better positioned to address recurring market-wide
practices.
8. Comparative Regulatory Perspective
8.1 European Union
The
European Union offers the clearest statutory movement toward interface-level
regulation. Article 25 of the Digital Services Act prohibits providers of
online platforms from designing, organizing or operating interfaces in a manner
that deceives or manipulates recipients or otherwise materially distorts or
impairs their ability to make free and informed decisions. Recital 67
specifically discusses practices such as giving choices unequal prominence,
repeatedly requesting a decision already made and making cancellation
significantly more cumbersome than sign-up. The provision is important because
it treats interface organization itself as a regulated activity rather than
requiring every case to be forced into a conventional false-statement model.
The
DSA operates alongside the Unfair Commercial Practices Directive (UCPD), which
already prohibits misleading and aggressive commercial practices in
business-to-consumer relations. Article 25(2) DSA limits overlap by excluding
practices covered by the UCPD or GDPR from the DSA prohibition. This layered
structure has advantages and complexity. It allows specialized regimes to
apply, but it also requires regulators and businesses to identify the correct
legal basis. India's more centralized CCPA guidance avoids some of this
fragmentation, though India lacks the same breadth of platform-systemic
obligations found in the DSA.
The
EU Artificial Intelligence Act, Regulation (EU) 2024/1689, adds another layer
relevant to platform manipulation. The Act is risk-based and does not convert
all recommendation or pricing algorithms into high-risk systems. It
nevertheless demonstrates a broader regulatory willingness to impose
transparency, governance and prohibited-practice rules where AI creates
specified harms. For Indian reform, the lesson is not to copy the EU
classification wholesale but to recognize that algorithmic consumer influence
may require auditable governance when its scale and impact become significant.
8.2 United States
The
United States relies heavily on Section 5 of the Federal Trade Commission Act,
which prohibits unfair or deceptive acts or practices. The FTC's Policy
Statement on Deception identifies three familiar elements: a representation,
omission or practice likely to mislead; assessment from the perspective of
consumers acting reasonably in the circumstances; and materiality. This
formulation is notably broad enough to address conduct rather than words alone.
US enforcement against negative-option practices, deceptively formatted
advertising and manipulative online design demonstrates the adaptability of a
general deception standard, although the precise scope of federal rulemaking
and remedial authority is institutionally different from India.
8.3 United Kingdom
UK
consumer law combines statutory rights, unfair-commercial-practice rules and
increasingly assertive competition-and-consumer enforcement. The Consumer
Rights Act 2015 remains central to contractual fairness and digital content,
while the Competition and Markets Authority has developed substantial work on
online choice architecture, subscriptions and consumer-facing digital
practices. The UK experience supports a practical point: guidance, enforcement
and market studies can evolve faster than primary legislation and can identify
design practices before they are fully litigated. India can use CCPA guidance
similarly, provided that important coercive duties remain anchored in statutory
authority and due process.
Table 3. Comparative
Approaches to Digital Consumer Deception
|
Jurisdiction
|
Primary
legal technique
|
Strength
|
Potential
limitation
|
|
India
|
CPA
2019 + E-Commerce Rules + CCPA guidelines + contract law
|
Specific
dark-pattern taxonomy and centralized consumer authority
|
Fragmentation
with IT/data regimes; developing platform case law
|
|
European
Union
|
UCPD
+ DSA + GDPR + AI Act
|
Express
interface and systemic platform duties
|
Complex
overlap among legal instruments
|
|
United
States
|
FTC
Act Section 5 + sectoral rules/enforcement
|
Flexible
deception and unfairness standards
|
Remedial/rulemaking
questions vary with statutory authority
|
|
United
Kingdom
|
Consumer
statutes + CMA enforcement/guidance
|
Strong
practical enforcement and market studies
|
Post-Brexit
regulatory evolution can produce transitional complexity
|
9. Findings and Discussion
The
first finding is that digital MISLEADING is best understood as an
'overall-impression' problem. The consumer's decision may be shaped by text,
colour, ordering, defaults, review signals, price presentation and timing. A
legal rule limited to explicit false propositions will under-capture deception,
while a rule that treats every persuasive design as unlawful will over-capture
ordinary commerce. Material distortion of a transactional decision provides a
defensible middle threshold.
The
second finding is that Indian law already contains more tools than is sometimes
acknowledged. The Consumer Protection Act, e-commerce rules, advertising
guidelines and dark-pattern guidelines collectively reach many platform
practices. The principal deficiency is not total statutory silence but
coordination, evidentiary access and uncertainty regarding how
platform-specific consumer duties interact with intermediary safe harbour.
Legislative reform should therefore clarify interfaces between regimes rather
than simply add another broad prohibition.
The
third finding is that platform liability should be functional. The legal
position should change as the platform's role changes. A service that hosts a
seller's statement is not situated identically to a service that verifies the
seller, controls fulfilment, authors a badge, ranks the product using paid
criteria and designs the checkout. Control, contribution and consumer reliance
provide administrable factors for assigning responsibility without abolishing
safe harbour.
The
fourth finding is that review and ranking integrity should be treated as
consumer-information infrastructure. Fake reviews are not only false speech;
they can corrupt the metrics that structure visibility. A platform's duty
should therefore extend to reasonable anti-manipulation systems and accurate
statements about verification. The goal is not perfect authenticity, which is
unattainable, but a defensible governance process proportional to scale and
risk.
The
fifth finding concerns algorithmic systems. The law should resist treating
'algorithmic' as a synonym for unlawful. Dynamic pricing, personalization and
recommendation can improve matching and reduce search costs. Legal intervention
becomes justified when the system creates a misleading impression, exploits
hidden mandatory charges, uses fabricated scarcity, unlawfully processes data,
discriminates contrary to applicable law or makes significant consumer
decisions without adequate transparency and accountability. Regulation should
target the harmful mechanism rather than the mere use of automation.
The
sixth finding is temporal. Data-protection law is becoming more relevant to
consumer manipulation, but India's current phased DPDP commencement requires
precise legal writing. Consumer-law enforcement should not be postponed while
data provisions await commencement. At the same time, regulators should prepare
for convergence because future personalization disputes may simultaneously
implicate consent, purpose limitation, consumer deception and algorithmic
governance.
The
comparative analysis indicates that the EU has moved furthest toward direct
regulation of interface design, while the US model demonstrates the continuing
value of a broad deception standard. India's distinctive advantage is the
CCPA's ability to issue specific consumer guidelines within a general statutory
framework. The challenge is to combine specificity with procedural clarity and
to avoid guidance becoming a substitute for legislation where substantial new
liabilities are created.
10. Legislative and Policy Recommendations
First,
India should codify a general digital-choice principle within consumer
legislation or rules: an e-commerce entity should not design, organize or
operate a transactional interface in a manner that materially deceives,
manipulates or impairs a consumer's ability to make a free and informed
decision. The existing 2023 Guidelines provide a strong foundation, but a
statutory or rule-based articulation would improve durability, enforcement
clarity and judicial application.
Second,
platform accountability should be expressly separated into two categories:
liability for third-party information and liability for the platform's
independent conduct. Section 79 safe harbour should continue to protect
qualifying intermediary activity, while consumer rules should clarify that safe
harbour does not exempt a platform from its own statutory disclosures,
guarantees, verification claims, advertising practices, dark patterns or
transaction design. This would reduce unnecessary doctrinal conflict.
Third,
large marketplaces should maintain review-integrity programmes proportionate to
scale. Minimum expectations should include detection of anomalous review and
transaction patterns, clear disclosure of incentivized reviews, meaningful
verified-purchase labels, documented procedures for contesting review
manipulation and periodic transparency reporting. Regulators should avoid
prescribing a single detection algorithm because technical methods evolve
rapidly.
Fourth,
total-price transparency should be strengthened. Mandatory charges that are
unavoidable for completion of a transaction should ordinarily be incorporated
into the price displayed at the stage at which consumers compare offers. Where
the charge genuinely depends on information not yet available, the platform
should explain the calculation basis prominently. False reference pricing and
fabricated discount claims should be supported by auditable price history.
Fifth,
cancellation and refusal should satisfy a symmetry principle. A consumer should
not face materially greater procedural friction to reject an optional add-on,
withdraw from a recurring service or exercise a cancellation right than was
required to accept it, subject to legitimate security and identity checks. This
principle translates the dark-pattern taxonomy into an administrable design
standard.
Sixth,
sponsored ranking and native advertising require standardized disclosure. The
label should be immediate, proximate and understandable, and the consumer
should be able to distinguish paid placement from organic relevance without
opening secondary menus. Platforms should also disclose the principal
commercial parameters that materially determine sponsored ranking where such
disclosure is necessary to avoid a misleading impression.
Seventh,
regulators should develop secure access to platform design evidence. In serious
investigations, the CCPA should be able to seek relevant A/B testing records,
interface change logs, complaint patterns and conversion metrics within
statutory powers and subject to confidentiality safeguards. Such evidence can
show whether a challenged design was accidental, experimental, known to affect
consumer decisions or deliberately retained because of increased conversion
despite consumer harm.
Eighth,
online dispute resolution should be integrated more closely with platform
grievance systems and consumer commissions. Standardized digital complaint data
would allow authorities to identify recurring practices rather than treating
each consumer's problem as isolated. Refunds, corrective disclosures and
interface changes can often provide more effective relief than conventional
litigation alone.
Ninth,
the CCPA, MeitY, competition authorities and sectoral regulators should
establish a formal coordination protocol for cases involving overlapping
consumer, data, competition and cybersecurity issues. Coordinated enforcement
reduces duplicative demands on firms while preventing regulatory gaps. As
substantive DPDP obligations commence, this coordination will become
increasingly important for personalized advertising, recommendation and
profiling disputes.
Tenth,
India should adopt risk-based audit expectations for high-impact automated
consumer systems rather than a generic requirement to disclose source code.
Audits can examine input governance, testing for deceptive outputs, pricing
logic, complaint outcomes, human oversight and change management. Source-code
disclosure is often neither necessary nor proportionate; what regulators need
is evidence capable of testing legal compliance and causal responsibility.
Eleventh,
consumer literacy should be treated as a complement to regulation, not a
substitute for it. Public campaigns can explain fake reviews, paid rankings,
subscription traps and data-driven personalization, but firms should not be
permitted to defend manipulative architecture on the ground that a sufficiently
sophisticated user could have discovered the truth. The legal benchmark must
remain reasonable consumer decision-making in the relevant context, with
sensitivity to vulnerable groups.
Finally,
enforcement should be outcome-sensitive. Repeated, scalable dark patterns that
generate substantial aggregate revenue deserve higher priority than isolated
technical defects. Penalties, corrective orders and refund programmes should
account for the number of affected consumers, duration of the practice,
knowledge, benefit obtained, remedial cooperation and risk of recurrence. This
approach would align deterrence with the economic scale of platform-based harm.
11. Limitations and Scope for Future Research
This
article is doctrinal and comparative rather than empirical. It does not measure
the prevalence of particular dark patterns in Indian platforms, nor does it
estimate consumer loss. Future research should conduct systematic interface
audits across Indian e-commerce, travel, food-delivery, fintech and
subscription services and should test how Indian consumers respond to scarcity,
defaults and cancellation friction. Empirical work would also help determine
whether particular groups are disproportionately affected by deceptive design.
A
second research agenda concerns AI-generated commercial communication.
Generative systems can produce personalized product descriptions, virtual
influencers, synthetic endorsements and automated customer-service
representations at scale. Future legal research should examine attribution,
evidentiary provenance and the standard of reasonable testing before such
systems are deployed. A third agenda concerns the interaction between consumer
law and the phased DPDP framework as substantive provisions commence.
Longitudinal analysis will be necessary to determine whether the two regimes
converge effectively or create overlapping and inconsistent compliance duties.
12. Conclusion
Platform-based
services have changed the mechanics of consumer deception. The legally relevant
representation may now be a sentence, a badge, a ranking position, a countdown
timer, a hidden fee, a review score, a default setting or a sequence of design
choices that collectively create a misleading impression. Contract law remains
relevant because consent and inducement still matter, but consumer law is
better equipped to address scalable practices that affect entire markets.
India's
present framework is not empty. The Consumer Protection Act, 2019, E-Commerce
Rules, advertising guidelines and Dark Patterns Guidelines create a substantial
foundation. Section 79 of the Information Technology Act appropriately protects
qualifying intermediary functions but should not be interpreted as immunity
from a platform's independent consumer-facing duties. The DPDP regime will add
another layer as its substantive provisions enter into force on the notified
schedule.
The
most workable legal principle is functional accountability. Responsibility
should follow control over the relevant interface or information, contribution
to the misleading practice and the reliance reasonably invited from consumers.
Combined with materiality, proportionality and effective remedies, this
framework can distinguish legitimate persuasion and innovation from deception
without requiring courts to treat every algorithm or marketplace feature as
inherently suspect.
The
deeper policy objective is the integrity of the consumer decision-making
environment. Digital markets cannot depend solely on the fiction that every
click represents a fully autonomous and informed choice when platforms can
experimentally optimize the path to that click. A trustworthy platform economy
requires accurate information, intelligible prices, authentic reputation
signals, fair exit mechanisms and accountability for design practices that
materially distort choice. Protecting those conditions is not hostile to
innovation; it is part of the legal infrastructure that makes digital commerce
sustainable.
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How to Cite This Article
AVDHESH MISHRA:, PROF. (DR.) RAKESH DAIYA, MISLEADING AND UNFAIR TRADE PRACTICES IN PLATFORM-BASED SERVICES., White Black Legal – International Law Journal, ISSN: 2581-8503, Vol. Volume 4, Issue VOLUME 4 ISSUE 2, October 2026, pp. 319-348. Available at: https://www.whiteblacklegal.co.in/details/misleading-and-unfair-trade-practices-in-platform-based-services
Author & Publication Record
Authors: AVDHESH MISHRA: & PROF. (DR.) RAKESH DAIYA
Registration ID: 107144 | Published Paper ID: WBL7144 & WBL7145
Year: Oct- 2026 | Volume: 4 | Issue: 2
Approved ISSN: 2581-8503 | Country: Delhi, India A
Page No.: 319-348
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