# From Optimizing Engagement to Measuring Value

arXiv: https://arxiv.org/abs/2008.12623 · HTML: https://mlsanity.com/paper/2008.12623/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 21 of 21)

- [Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking](https://mlsanity.com/paper/2403.03185/) (2024)
- [What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data](https://mlsanity.com/paper/2510.26202/) (2025)
- [Ground(less) Truth: A Causal Framework for Proxy Labels in Human-Algorithm Decision-Making](https://mlsanity.com/paper/2302.06503/) (2023)
- [Embedding Democratic Values into Social Media AIs via Societal Objective Functions](https://mlsanity.com/paper/2307.13912/) (2023)
- [The MineRL BASALT Competition on Learning from Human Feedback](https://mlsanity.com/paper/2107.01969/) (2021)
- [On component interactions in two-stage recommender systems](https://mlsanity.com/paper/2106.14979/) (2021)
- [Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms](https://mlsanity.com/paper/2401.09804/) (2024)
- [Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints](https://mlsanity.com/paper/2306.13738/) (2023)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics](https://mlsanity.com/paper/2202.01615/) (2022)
- [Trustworthy Social Bias Measurement](https://mlsanity.com/paper/2212.11672/) (2022)
- [Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems](https://mlsanity.com/paper/2305.17428/) (2023)
- [Optimization's Neglected Normative Commitments](https://mlsanity.com/paper/2305.17465/) (2023)
- [Value Alignment of Social Media Ranking Algorithms](https://mlsanity.com/paper/2509.14434/) (2025)
- [CTRL-Rec: Controlling Recommender Systems With Natural Language](https://mlsanity.com/paper/2510.12742/) (2025)
- [System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-Processes](https://mlsanity.com/paper/2406.01611/) (2024)
- [From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap](https://mlsanity.com/paper/2404.13131/) (2024)
- [Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Sphere](https://mlsanity.com/paper/2410.12123/) (2024)
- [Measurement as Bricolage: Examining How Data Scientists Construct Target Variables for Predictive Modeling Tasks](https://mlsanity.com/paper/2507.02819/) (2025)
- [The Burden of Interactive Alignment with Inconsistent Preferences](https://mlsanity.com/paper/2510.16368/) (2025)
- [MultiScale Contextual Bandits for Long Term Objectives](https://mlsanity.com/paper/2503.17674/) (2025)

## 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".