Data Science in Reciprocal Dating Platforms

Research boundary: This article analyses public documentation and Murudo source code without using production-user or human-subject data. Its legal discussion is governance research, not legal advice.

Abstract

Dating platforms are a consequential application of data science because the ranked entities are people who can accept, ignore or reject one another. This paper asks how data science can improve relevant, reciprocal and safe partner discovery in Murudo, a Zimbabwe-focused platform, while protecting fairness, privacy and autonomy. It uses a qualitative socio-technical case study, reading peer-reviewed literature alongside Murudo's repository and public privacy documentation without accessing production records or conducting human-subject research. Murudo currently combines profile information, behavioural feedback, distance, mutual interest, verification, activity and exposure history in a lightweight recommendation pipeline. These signals can improve opportunities for introduction, but cannot establish pair-specific chemistry or future relationship quality. Swipes are shaped by earlier rankings, sensitive attributes can reproduce exclusion, and accuracy-only objectives can concentrate visibility. The paper therefore proposes eligibility filtering, reciprocal-probability estimation and fairness-aware reranking, evaluated through temporal validation and controlled experiments. Matches, replies and exposure should be interpreted with safety and privacy guardrails. Murudo should use data science as accountable decision support for creating better opportunities to meet, not as a system for predicting love.

Keywords: dating applications; data science; reciprocal recommender systems; algorithmic fairness; privacy; Zimbabwe; Murudo

Introduction

Online dating transforms partner discovery into a sequence of data-mediated decisions. A platform first decides which profiles are eligible for consideration, then chooses which candidates to display and in what order. Users respond through likes, passes, messages, reports and other actions, and these responses become inputs to later decisions. Data science is therefore not an optional analytical layer added after the product is built. It helps constitute the market in which attention and opportunities to connect are distributed.

Dating differs from familiar recommendation domains such as films or music. A film does not evaluate the viewer, experience unwanted exposure or suffer when a ranking system repeatedly places it last. In dating, every recommended person is simultaneously a user, a candidate and a decision-maker. A useful recommendation must respect bilateral eligibility and must create a plausible opportunity for reciprocal interest. This is the defining property of a reciprocal recommender system (Pizzato et al., 2013; Palomares et al., 2021). It also makes conventional engagement metrics incomplete. A profile view or swipe can reveal something about user behaviour, yet it does not by itself represent a good outcome for either person.

Murudo provides a concrete setting for examining these issues. Its repository describes Daily introductions, For You discovery, likes, matches, chat, verification, safety controls, subscriptions and compatibility features across backend, mobile, administrative and web surfaces (ForetellHQ, 2026a). The product is oriented towards intentional connection in a Zimbabwean context. Its transparent, computationally light ranking system permits analysis of both current data science and a more rigorous future evaluation programme.

The research question is: How can data science improve relevant, reciprocal and safe partner discovery in Murudo while protecting fairness, privacy and user autonomy? The paper advances a bounded thesis. Data science can improve candidate retrieval, ordering, reciprocal opportunity, safety operations, fair exposure and product measurement. It cannot reliably infer chemistry between two people before they interact, and it should not market a behavioural score as a prediction of relationship success. This distinction is supported by relationship science showing that general tendencies to desire or be desired can be partially predicted while pair-specific desire remains difficult to predict from pre-meeting traits (Joel et al., 2017).

The paper connects reciprocal-recommendation research to an actual architecture, proposes measures reflecting bilateral outcomes, exposure and safety, and situates technical design within Zimbabwe's data-protection framework. It is deliberately non-empirical: no production user data, confidential metric or causal result is presented. Observed code behaviour, proposed models and external empirical findings are distinguished throughout. Evidence comprises peer-reviewed research, official legal and regulatory material, the repository and Murudo's public privacy policy (ForetellHQ, 2026b; 2026c; 2026d; 2026e). No database, private profile or production configuration was inspected. The study is neither a security audit nor legal advice, and cannot establish whether current weights improve real matches.

Literature review

1 Reciprocal recommendation and relevance

Traditional recommenders estimate whether a user will value an item. Reciprocal recommenders consider two directed preferences: whether A is likely to value B and whether B is likely to welcome A. RECON operationalised why one-sided relevance is incomplete in online dating (Pizzato et al., 2010). People-to-people recommendation is distinct because both parties express preferences and success depends on response from the recommended person (Pizzato et al., 2013).

Content-based systems compare profile attributes; collaborative systems infer patterns from similar users; hybrid systems combine both. Akehurst et al. (2011) used profile similarity to support new users and interaction histories to improve later recommendations. Diaz et al. (2010) framed dating as retrieval and ranking in which interaction depends on both participants. For Murudo, the target should therefore be a bilateral event, not merely the probability that the viewer taps Like.

Reciprocity changes relevance. Repeatedly showing a popular person who is already overwhelmed may yield high one-sided accuracy but little chance of conversation. The objective should distinguish viewer preference, candidate receptiveness and scarce attention.

2 Cold start, implicit feedback and selection

Every behavioural recommender encounters cold start: new members lack swipe histories, while underexposed profiles provide little response evidence. Collaborative methods perform poorly under sparsity, motivating content features, elicited preferences or exploration (Schein et al., 2002). Dating intensifies the problem because early exposure determines whether evidence is generated; low ranking can keep a profile data-poor.

Profile attributes offer an initial prior, but stated preferences may be incomplete, aspirational or contextual. Behaviour is noisy: a pass may reflect the photo order, mood, accident or browsing fatigue. Because clicks are position-dependent, behavioural observations are not independent labels of intrinsic relevance (Joachims et al., 2005). The same caution applies to swipes and profile opens.

Recommendation logs are generated by a previous policy: users respond only to profiles shown. Treating recommendation as exposure reveals self-selection and system-selection bias (Schnabel et al., 2016). Simulation also shows that retraining on algorithmically confounded behaviour can homogenise users without increasing utility (Chaney et al., 2018). Historical like rates therefore cannot identify the best Murudo policy without considering position, availability and prior scoring.

3 Attraction, compatibility and epistemic limits

Relationship research sets a demanding bar for compatibility claims. Finkel et al. (2012) found that online dating expands access and changes communication, but insufficient evidence that proprietary algorithms predict relationship success. Making an introduction and forecasting a future relationship are different tasks.

In two speed-dating studies, models using more than one hundred pre-meeting traits predicted some actor variance (a general tendency to desire) and partner variance (a tendency to be desired), but not relationship variance: one person's unique attraction to another (Joel et al., 2017). Initial face-to-face impressions later predicted interest and pursuit (Baxter et al., 2022), suggesting that interaction generates information unavailable to profile models.

Murudo's defensible promise is procedural, not prophetic: identify eligible candidates, improve ordering, recognise reciprocal signals and diversify opportunities. A score should not imply that two strangers are objectively compatible; labels must communicate limited inputs and uncertainty.

4 Popularity, market structure and exposure fairness

Dating markets can develop strong hierarchies of attention. Analysing messaging behaviour from a large online dating service in four United States cities, Bruch and Newman (2018) found structured patterns of desirability and aspirational pursuit. Their results should not be transferred directly to Zimbabwe or to Murudo, but they demonstrate that behavioural datasets reflect a market shaped by demographics, competition and interface design rather than a collection of independent preferences.

Exposure is the gateway to every downstream action. Singh and Joachims (2018) formalise ranking fairness in terms of how position allocates exposure to candidates. This perspective is especially relevant in a two-sided platform: maximising expected likes can repeatedly surface those already favoured, while depriving others of enough impressions to estimate their relevance. Fairness need not require identical exposure irrespective of eligibility or safety. It requires a declared normative rule, measurable opportunity and justification for departures from that rule.

Intimate platforms also operate where personal autonomy and structural inequality meet. Hutson et al. (2018) argue that interface and ranking choices can exacerbate or mitigate bias without simply overriding individual choice. A platform may respect a person's decision about whom to contact while still declining to optimise an ecosystem for segregation. Mehrabi et al. (2021) similarly distinguish several sources of machine-learning bias, including historical data, feature choices, measurement and evaluation. A fair dating system must inspect the full pipeline, not only the final model.

Murudo case study: a data-science system

1 Data lifecycle

Murudo's public documentation describes the collection of account details, profile content, photos, prompts, interests, preferences, verification submissions, likes, matches, messages, reports, blocks, approximate location, notification tokens and diagnostics (ForetellHQ, 2026e). These data serve different purposes and should not be collapsed into one undifferentiated “user profile.” Some establish eligibility, some support safety, some personalise ranking and some measure operations.

The reviewed implementation can be represented as the following lifecycle:

Profile attributes and account state determine eligibility; ranking determines exposure; exposure enables behaviour; and behaviour updates preference statistics. Analytics describe parts of this feedback system. Governance must apply throughout because a collection, eligibility or measurement error cannot be repaired by changing only the score.

2 Observed recommendation behaviour

The current recommendation service uses a normalised weighted score (ForetellHQ, 2026b). Distance has the largest base weight, 4.0, and is converted into a linearly declining score over a 200-kilometre range; unknown distance is neutral. An existing inbound like receives a mutual-interest weight of 3.0. Verification and recent activity receive smaller weights of 0.3 each. These constants are product choices, not coefficients estimated from a reported causal experiment.

Behavioural preference learning activates after fifteen likes and passes. Its influence grows with the number of swipes and reaches full configured confidence at one hundred. Categorical observations for religion, ethnicity, intent, relationship type, drinking and children are stored as like/pass counts. A Laplace-smoothed rate avoids early values of exactly zero or one. After at least five liked ages, the candidate's age receives a Gaussian affinity based on the viewer's liked-age history. The system also records running statistics for liked distance, but the reviewed scorer currently uses the fixed distance normalisation rather than a learned distance distribution. This distinction illustrates why code-level analysis is preferable to relying only on a general design description.

The matching route performs eligibility and safety-oriented filtering before scoring, records feed impressions, and applies a recent-impression cooldown. It also computes a bounded fair-exposure set. Under the reviewed defaults, a candidate can receive a score lift of 0.04 when the person has no lifetime match, has a sufficiently complete and active profile, has at least two usable photos, has received no more than three recent Discover impressions and no more than ten recent inbound likes within the configured windows (ForetellHQ, 2026c). The rule is targeted assistance, not demographic parity, and it does not prove that exposure is equitable across groups. It is nevertheless an explicit recognition that relevance and opportunity are not identical objectives.

3 Observed analytics and their limits

Murudo's administrative analytics expose a funnel from signup through profile creation, verification, first swipe, first match and first message; weekly cohorts using last-seen thresholds for day-one, day-seven and day-thirty retention; counts by profile location; and real-time foreground and WebSocket presence (ForetellHQ, 2026d). These are useful operational views, but they are descriptive aggregates rather than a model-evaluation system.

Several cautions follow. A first message does not show whether the recipient replied. Last-seen-based retention records that someone returned after a threshold, not why they returned or whether the experience was beneficial. Geographic counts reveal supply distribution but do not establish local match quality. Real-time connection counts are capacity measures rather than relationship outcomes. Most importantly, a dating platform can succeed when a user forms a relationship and leaves. Optimising indefinite retention or time spent can conflict with the product's stated purpose.

Data-science framework and evaluation

Everything in this section is a proposal for research and governance; it is not a description of deployed Murudo behaviour.

1 Stage one: eligibility before prediction

The first stage should remain rule-based and auditable, applying bilateral gender and age choices, profile readiness, blocks, pauses, moderation restrictions, location availability and other hard constraints. A model must not trade a safety or consent boundary for predicted response. Eligibility decisions should carry machine-readable reasons, with monitoring for disproportionate feed depletion.

Before sufficient behaviour exists, Murudo can use explicit choices, broad distance, freshness and controlled exploration without inferring a detailed “type” from a few swipes. Hybrid approaches address sparse settings (Schein et al., 2002; Akehurst et al., 2011), but require local evaluation. New-user performance should be reported separately so averages do not conceal poor cold-start experience.

2 Stage two: estimate reciprocal opportunity

For an eligible viewer u and candidate c, the current score can be expressed conceptually as:

where x_k contains available distance, mutual-interest, trust, activity and learned-affinity signals, w_k contains the configured weights, and the exposure lift is bounded so the score remains at most one. This representation is interpretable and provides a useful baseline. Its limitation is that most preference signals estimate the viewer's side; an inbound like supplies strong reciprocal evidence only after the other person has already acted.

A research model could estimate two calibrated probabilities: p_u→c, the probability of a positive action by the viewer following a genuine exposure, and p_c→u, the candidate's probability of responding positively if subsequently exposed. A simple reciprocal score could use their product or geometric mean:

The geometric mean penalises one-sided recommendations without requiring equal probabilities. Calibration matters more than complexity because ranking and experimentation need meaningful estimates. Candidate receptiveness should account for attention saturation rather than route more people towards someone already overwhelmed.

Feature design should begin with explicit intent, broad age fit, approximate distance, activity, profile completeness, prior reciprocity and non-sensitive interaction summaries. Offline lift does not justify automatic use of religion or ethnicity; purpose, lawful basis, expectations, disparate effects and alternatives require separate review. A model card should document features, training window, cold-start behaviour, calibration, limitations and prohibited uses.

3 Stage three: fairness-aware reranking

Prediction determines estimated relevance; reranking determines how opportunity is distributed. A fairness-aware stage can optimise reciprocal score while enforcing exploration and exposure constraints. The unit of analysis should be the opportunity to appear in a meaningful position, not just total impression count. Exposure can be discounted by rank because a first profile and a twentieth profile are not equally likely to be considered (Singh and Joachims, 2018).

Murudo's existing low-impression lift is a reasonable baseline for comparison. A proposed reranker could reserve a small, measured portion of high-visibility positions for eligible, safe and underexposed profiles, subject to minimum reciprocal relevance. This avoids both extremes: a pure popularity ranking that starves newcomers and an indiscriminate rotation that ignores user choices. The fairness policy must declare whom it protects and why. Possible audits include exposure ratios by gender, age band, location and verification status; concentration measures across candidates; the share of eligible profiles receiving zero exposure; and opportunity conditional on relevant supply.

Sensitive-attribute audits require restricted access and minimum cohort sizes; audit variables need not become ranking features. Results should be reviewed intersectionally where samples permit because aggregates can hide local disparities. Sparse data may support uncertainty, not a parity claim.

4 Offline evaluation

Historical evaluation should use temporal splits: train on events before a cutoff, validate on a later period and test on a final untouched period. Random record splitting would leak a user's later behaviour into predictions about earlier interactions. The split should also keep paired events and impression context intact. Results must be stratified for cold-start users, established users, sparse locations and high-activity candidates.

Logged data reveal outcomes only for displayed profiles, so conventional accuracy estimates are selection-biased. Impression propensities should be logged for a limited exploration policy, and inverse-propensity or doubly robust estimators can be considered as described by Schnabel et al. (2016). Joachims et al. (2005) further justify controlling for position. Offline results should compare at least three policies: the current heuristic baseline, a reciprocal estimator without reranking, and the reciprocal estimator with fairness-aware reranking.

Offline measures should include directed calibration, reciprocal log loss, ranking utility, candidate coverage and exposure concentration. Accuracy must be reported with error patterns: a high-scored pair that never reciprocates wastes attention, while a hidden low-scored pair cannot disprove the model. Confidence intervals should accompany point estimates, with the final test set untouched during tuning.

5 Online outcomes and guardrails

The primary online unit should be an eligible impression, defined as a candidate actually rendered in a position where the viewer could reasonably consider the profile. From that denominator, Murudo could measure:

1. Reciprocal matches per eligible impression, indicating bilateral positive action.
2. First-message conversion per match, indicating whether the connection began.
3. Reply conversion per first message, indicating that communication became bilateral.
4. Time-to-reciprocal outcome, identifying whether one policy merely delays opportunity.
5. Exposure coverage and concentration, indicating how attention is distributed.

These measures should be paired with safety and experience guardrails: report and block rates per conversation, moderation-confirmed harm where available, rapid unmatch or conversation-abandonment patterns, latency, empty-feed rate, cold-start coverage, privacy incidents and demographic exposure disparities. A rise in matches accompanied by more reports is not an unqualified improvement. Nor should messages be scored for emotional content merely to produce a “quality” label; a reply event provides a less intrusive signal.

Controlled experiments are necessary for causal claims. Users could be assigned at viewer or stable market-cluster level, with current ranking as control. Each test should pre-register its primary metric, guardrails, minimum sample and harm stopping rule. Small markets may require longer tests or stepped rollout rather than decisive claims from noise.

Long-term interpretation must remain product-aware. Retention can be a health indicator for onboarding or safety, but it is not the ultimate dating outcome. The platform should report a portfolio of measures and supplement quantitative results with voluntary, privacy-conscious user research. If users say recommendations feel repetitive, unsafe or culturally misaligned, an unchanged match rate does not settle the question.

Ethics, privacy and governance

1 Sensitive data and purpose limitation

Dating data are intimate even when individual fields seem ordinary. Profiles may disclose age, gender, religion, ethnicity, intentions, family plans, approximate location and photographs; behaviour may reveal unstated preferences. Messages and reports add interpersonal context, while combination increases sensitivity and re-identification risk.

Zimbabwe's Cyber and Data Protection Act defines personal and sensitive information broadly, including racial or ethnic origin, religious beliefs, sex life, gender, age, marital status and family status (Government of Zimbabwe, 2021). Section 7 requires data to be adequate, relevant, not excessive, accurate where necessary and retained no longer than needed. Sections 8 and 9 address fair, lawful and purpose-specific processing. Section 11 sets conditions for sensitive-data processing, while section 25 addresses decisions based solely on automated processing, including profiling, that produce legal or similarly significant effects. This paper does not decide the legal effect of a dating rank, but the provisions make documentation, consent and human governance materially relevant.

The 2024 regulations add duties concerning controller licensing, processing notifications, security, data-protection officers and automated decisions (Government of Zimbabwe, 2024). Review should precede any new model using sensitive features or verification biometrics: is the purpose clear, processing necessary, lawful basis established, objection possible and a less intrusive signal available?

Murudo's public policy states that approximate location supports discovery, that information personalises results and that users have controls over profile data, location visibility, discovery pause, access, correction, export and deletion (ForetellHQ, 2026e). Model documentation should connect each feature to these public purposes. Reusing verification images, messages or reports for compatibility modelling would be a materially different purpose requiring specific review; this paper does not recommend it.

2 Preference learning and discrimination

Learning from individual choice does not make an algorithm neutral. Swipes can reflect social prejudice, unequal profile presentation, cultural stereotypes and the previous model's exposure decisions. The current code's use of religion and ethnicity in learned affinity is therefore a governance-sensitive design choice, even though smoothing and gradual confidence reduce instability (ForetellHQ, 2026b). Automating observed preferences can transform many private decisions into systematic exclusion at scale.

Autonomy and anti-discrimination are not simple opposites because categories, filters, defaults and order shape options before choice (Hutson et al., 2018). Murudo can preserve likes and passes while declining to maximise sensitive-attribute sorting. Options include excluding ethnicity from scoring, limiting sensitive factors to appropriate user-controlled filters, shadow evaluation, bounded effects, personalisation reset and exclusion audits.

Deleting protected attributes does not solve fairness: location, language, name, photographs or interests may act as proxies. Bias can enter through collection, labels, features and evaluation (Mehrabi et al., 2021). Audits should test direct and proxy pathways; sensitive audit data require restricted, purpose-limited access and aggregation that protects small communities.

3 Transparency, contestability and autonomy

Meaningful transparency need not reveal the formula, but should explain eligibility, approximate distance, activity, prior actions, reciprocal signals and efforts to avoid repetition or underexposure. “Shown because you are nearby and share stated intentions” is more defensible than “The algorithm says you are compatible.”

Users should be able to update preferences, reset learned personalisation, pause discovery and correct data. Reset must cease using derived preference statistics, not merely change visible settings. Export and deletion processes should disclose what derived, safety or audit records remain and for how long. Human review should cover account, moderation and verification decisions affecting participation.

The United States Federal Trade Commission's 2026 action against Match and OkCupid illustrates the practical importance of aligning behaviour with privacy promises. The complaint alleged that OkCupid shared millions of user photographs and related data with an unrelated third party contrary to representations made to users (Federal Trade Commission, 2026). The case is not evidence about Murudo, nor does United States enforcement define Zimbabwean obligations. It is a comparative warning that intimate-platform data cannot be treated as a reusable asset detached from stated purpose, user expectation and contractual safeguards.

4 Operational governance

Murudo should register each data-science use, including owner, purpose, inputs, review, retention, model version, affected surfaces and monitoring. Material ranking changes need a model card and experiment record covering hypothesis, metrics, cohorts, trade-offs, approval, dates, results, incidents and rollback.

NIST's voluntary AI Risk Management Framework groups governance around mapping context, measuring risk, managing risk and governing the lifecycle (NIST, 2023). Adapted to Murudo, this means mapping who is affected by a ranking, measuring calibration and exposure as well as accuracy, managing harmful outcomes through limits and rollback, and assigning accountable owners. The framework is guidance rather than Zimbabwean law, but it offers a practical structure for multidisciplinary review.

Raw-data access should follow least privilege. Analysts should use de-identified events and only the geographic precision required. Message content, verification submissions and precise coordinates should be excluded from routine ranking analysis. Retention schedules must cover derived features as well as source tables; dashboards need minimum cohort thresholds and logged exports.

Governance must include the decision not to build. A predictive feature may be too invasive, opaque or exclusionary. The appropriate system is the least intrusive one that produces adequate, well-evaluated opportunities for connection.

Discussion and limitations

The case study demonstrates that data science can improve the mechanics surrounding romantic introduction without resolving the uncertainty of attraction. Murudo's current approach has several pragmatic strengths: it is interpretable, computationally bounded, begins with explicit eligibility, incorporates mutual interest, records exposure and includes a small opportunity-oriented adjustment. These qualities make it easier to audit than a large end-to-end model. The same transparency reveals unresolved questions, including whether fixed weights are calibrated, whether sensitive preference learning provides sufficient benefit to justify its risks and whether the fair-exposure rule improves opportunity in different local markets.

Cold start remains fundamental. New users lack behavioural evidence, and people in lower-supply locations may never generate enough events for stable personalisation. This is not merely a modelling inconvenience. Sparse data can become unequal product quality if the system performs best only for dense markets or already popular profiles. Separate cold-start reporting, exploration and content-based priors are therefore part of fairness.

Selection bias limits historical conclusions. A match occurs only after both people were eligible, shown and willing to act. Unshown pairs have no observable counterfactual outcome. Even impression logs may miss whether the profile was meaningfully viewed. Temporal validation and propensity-aware evaluation reduce some bias, but only controlled exposure can support strong causal claims. Experiments themselves must be governed because exploration allocates real people's attention.

Feedback loops create a second limitation. If the model promotes profiles that already attract likes, their additional exposure generates more evidence of popularity. If it learns religion or ethnicity patterns from those exposures, it may harden current sorting. A fairness-aware reranker can change opportunity, but no universal mathematical definition determines the socially correct allocation. Policy must be developed with affected communities and examined for unintended effects.

Cultural validity also constrains transfer. Much cited dating research uses United States or other non-Zimbabwean samples, and several studies focus on heterosexual markets. Their concepts help identify technical problems but do not establish the preferences, harms or success criteria of Murudo's users. Local, voluntary research should include varied genders, orientations, ages and locations without forcing every community into statistically convenient categories. Small samples should produce cautious uncertainty, not confident stereotypes.

The paper itself has clear limitations. It is based on a repository snapshot and public documents dated through 13 July 2026. It does not verify production configuration, observe user experience, test the scorer, inspect private data or calculate any performance metric. It does not establish legal compliance. The proposed probabilities, fairness audits and experiments are an evaluation agenda, not validated improvements. These boundaries are necessary to avoid turning technical plausibility into an unsupported product claim.

Despite these limitations, the socio-technical framing changes what “better matching” means. A successful system does not maximise the number of swipes extracted from attention. It creates more relevant and reciprocal opportunities, distributes those opportunities responsibly, reduces avoidable harm and gives users meaningful control over intimate data. Quantitative evidence is valuable only when its denominator, causal limitations and human consequences are visible.

Conclusion

Murudo illustrates both the promise and limits of data science in dating. Profile data, behavioural feedback, distance, activity, verification, mutual interest and impression history can organise a large candidate space and reduce poor ordering. Reciprocal estimation can focus ranking on plausible two-sided outcomes, while fairness-aware reranking can prevent pure popularity from monopolising exposure. Funnel and cohort analytics can identify operational problems, and controlled experiments can test changes more credibly than intuition.

None of these capabilities predicts love. Pre-meeting features cannot capture the interaction-specific information through which chemistry and relationships develop. Treating a rank as destiny would overstate the evidence and weaken user autonomy.

The appropriate design goal is therefore accountable opportunity. Murudo should preserve auditable eligibility rules, evaluate a reciprocal model against its current heuristic, measure matches, replies and exposure together, and enforce safety, privacy and fairness guardrails. Sensitive attributes should face a necessity and consent review rather than automatic inclusion. Model changes should be documented, monitored and reversible. Under this approach, data science serves the people using the platform: it helps create better chances to meet while remaining honest about what cannot be known in advance.

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