mlsanityData to 7 Oct 2026

From Optimizing Engagement to Measuring Value

arXiv 2008.12623 ↗First posted 21 Aug 2020cs.SI cs.LG stat.MLcited by 21

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Abstract (arXiv)

Most recommendation engines today are based on predicting user engagement, e.g. predicting whether a user will click on an item or not. However, there is potentially a large gap between engagement signals and a desired notion of "value" that is worth optimizing for. We use the framework of measurement theory to (a) confront the designer with a normative question about what the designer values, (b) provide a general latent variable model approach that can be used to operationalize the target construct and directly optimize for it, and (c) guide the designer in evaluating and revising their operationalization. We implement our approach on the Twitter platform on millions of users. In line with established approaches to assessing the validity of measurements, we perform a qualitative evaluation of how well our model captures a desired notion of "value".

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Aug 2020.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 5; 2024: 6; 2025: 6.

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  1. 2024Correlated Proxies: A New Definition and Improved Mitigation for Reward Hackingcited by 50
  2. 2023Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Makingcited by 25
  3. 2023Embedding Democratic Values into Social Media AIs via Societal Objective Functionscited by 25
  4. 2021The MineRL BASALT Competition on Learning from Human Feedbackcited by 24
  5. 2025What's In My Human Feedback? Learning Interpretable Descriptions of Preference Datacited by 22
  6. 2021On component interactions in two-stage recommender systemscited by 14
  7. 2024Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platformscited by 13
  8. 2023Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraintscited by 11
  9. 2024Accounting for AI and Users Shaping One Another: The Role of Mathematical Modelscited by 11
  10. 2022Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metricscited by 8
  11. 2022Trustworthy Social Bias Measurementcited by 7
  12. 2023Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systemscited by 7
9 more
  1. 2023Optimization's Neglected Normative Commitmentscited by 4
  2. 2025Value Alignment of Social Media Ranking Algorithmscited by 4
  3. 2025CTRL-Rec: Controlling Recommender Systems With Natural Languagecited by 4
  4. 2024System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processescited by 2
  5. 2024From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gapcited by 1
  6. 2024Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Spherecited by 1
  7. 2025Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Taskscited by 1
  8. 2025The Burden of Interactive Alignment with Inconsistent Preferencescited by 1
  9. 2025MultiScale Contextual Bandits for Long Term Objectivescited by 0