Bad defaults / preselection
The provider-preferred option is already selected or treated as the normal path, so inaction becomes consent, spending, or data sharing.
- Code
- I12
- Category
- Informational / interface
- Severity
- Medium
- Evidence
- ModerateGray et al. identify bad defaults and preselection as interface-interference patterns; game-specific evidence overlaps with accidental purchase and consent-flow studies.
- Purpose served
- Serves businessPrimarily serves the provider's revenue, retention, or data — the most suspect.
- Mechanism family
- Interface interference
- Platforms
- Mobile / F2P · PC / console · Live-service · UGC platforms
- Player costs
- Autonomy / choiceFinancialData / privacy
- Target Audience
- policymakersdevelopers
- Tags
- bad defaultspreselectiondefaultsconsentpurchase flowserves businessdeceptive communicationlow transparencyconsent underminedmonetary pressurecognitive pressuredata pressureugc platformsvulnerability exploitation
- Also known as
- preselected option, default-to-opt-in, default-to-purchase
How it works
Settings, purchase confirmations, account-linking prompts, or privacy choices ship with the risky option preselected. The player must notice and actively undo the default, often while trying to continue play.
Why it can be harmful
Defaults exploit inertia and misplaced trust: players often assume defaults are safe or recommended. In games, this can turn a quick continue action into opt-in tracking, notifications, matchmaking exposure, subscription renewal, or a paid purchase path.
Examples in the wild
- A paid bundle tier preselected in a shop modal
- In-game personalised offers or tracking enabled by default
- Game subscription auto-renewal or notifications preselected during account linking
- A default matchmaking or social-visibility setting that exposes more than expected
Illustrative genre examples to aid recognition — not allegations about specific titles.
References
- Gray, C. M.; Santos, C. T.; Bielova, N.; Mildner, T. (2024). An ontology of dark patterns knowledge: Foundations, definitions, and a pathway for shared knowledge-building. Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. doi.org/10.1145/3613904.3642436 · citing patterns
- Mathur, A.; Acar, G.; Friedman, M. J.; Lucherini, E., et al. (2019). Dark patterns at scale: Findings from a crawl of 11K shopping websites. Proceedings of the ACM on Human-Computer Interaction (CSCW). doi.org/10.1145/3359183 · citing patterns
- Luguri, J. B.; Strahilevitz, L. J. (2021). Shining a light on dark patterns. Journal of Legal Analysis. doi.org/10.1093/jla/laaa006 · citing patterns
- King, J. (2023). Investigating players' perceptions of deceptive design practices within a 3D gameplay context. Proceedings of the ACM on Human-Computer Interaction (CHI PLAY). doi.org/10.1145/3611053 · citing patterns
Related patterns
Trick wording / misleading copy
Confusing, ambiguous, or expectation-violating wording makes the player take an action they did not mean to take.
Language inaccessibility / complex copy
Important purchase, privacy, odds, or consent information is presented in language the player cannot reasonably understand.
Accidental-purchase / default-to-purchase UI
Purchase is the default or easily mis-tapped path, so spending happens without express, informed consent.
Feedforward ambiguity / unclear consequences
The interface fails to make clear what a button, prompt, or action will actually do before the player commits.
Personalised spend-optimisation
Silently using a player's behavioural data to tune offers, prices, odds, difficulty, or matchmaking to maximise that individual's spending.
Comparison prevention
Making it hard to compare prices, odds, or options so players can't judge value.