--- title: Our Paper Got Accepted to AIES 2026. Here’s What It... description: A research paper accepted to AIES 2026 challenges a foundational assumption in algorithmic recourse: that weighted proximity (a weighted distance metric)... canonical: https://daily.dev/posts/our-paper-got-accepted-to-aies-2026-here-s-what-it-found-with-numbers--dvp0ak95u twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Our Paper Got Accepted to AIES 2026. Here’s What It Found, With Numbers. | daily.dev og:description: A research paper accepted to AIES 2026 challenges a foundational assumption in algorithmic recourse: that weighted proximity (a weighted distance metric)... og:url: https://daily.dev/posts/our-paper-got-accepted-to-aies-2026-here-s-what-it-found-with-numbers--dvp0ak95u og:image: https://api.daily.dev/og/posts/DvP0aK95U.png og:image:alt: Our Paper Got Accepted to AIES 2026. Here’s What It Found, With Numbers. og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Our Paper Got Accepted to AIES 2026. Here’s What It Found, With Numbers. **[Medium](https://daily.dev/sources/medium_js)** · 4 min read · 0 upvotes · 0 comments ## Summary A research paper accepted to AIES 2026 challenges a foundational assumption in algorithmic recourse: that weighted proximity (a weighted distance metric) reliably predicts human preferences for loan decision explanations. Across two user studies with 101 participants, global weighted proximity matched actual human choices only 53.21% of the time — barely above chance. Personalized proximity improved to 56.91% overall, but in the more realistic Study 2 it fell to 48.17%, meaning it predicted preferences worse than random. The findings suggest that widely deployed recourse systems optimizing for this metric may be actively steering people toward worse outcomes, raising serious concerns for GDPR-compliant AI transparency systems. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### Does weighted proximity in algorithmic recourse actually predict what people want when rejected for a loan? Not reliably. In a study with 101 participants across two phases, global weighted proximity matched actual human choices only 53.21% of the time, barely above chance. Personalized proximity, using weights learned from each person's own prior choices, averaged 56.91%, but one of the two sub-studies scored just 48.17%, meaning it pointed away from what people wanted more often than toward it. _daily.dev surfaces research like this for engineers building or evaluating explainability and recourse systems._ ### Can an algorithmic recourse system be GDPR compliant but still fail the people it's meant to help? Yes. A recourse system can satisfy transparency obligations like GDPR's on paper while optimizing for a metric, weighted proximity, that does not track what people actually want to change about their situation. Research testing this metric against real human choices found it matched preferences only slightly better than a coin flip, suggesting compliance and actual helpfulness can diverge. _engineers weighing compliance against real user outcomes can track findings like this on daily.dev._ ## Similar posts on daily.dev - [AI Papers to Read in 2025](https://daily.dev/posts/ai-papers-to-read-in-2025-kzq7ahqog) · Towards Data Science · 6 upvotes · 0 comments - [Measuring What Matters with Jules](https://daily.dev/posts/measuring-what-matters-with-jules-bwyrmpwn7) · Google Developers · 1 upvotes · 0 comments - [How we measured AI writing across arXiv, and where the measurement breaks](https://daily.dev/posts/how-we-measured-ai-writing-across-arxiv-and-where-the-measurement-breaks-ygnpa2wiv) · Hacker News · 0 upvotes · 0 comments --- Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#ethical-ai](https://daily.dev/tags/ethical-ai), [#explainable-ai](https://daily.dev/tags/explainable-ai) [View this post on daily.dev](https://daily.dev/posts/our-paper-got-accepted-to-aies-2026-here-s-what-it-found-with-numbers--dvp0ak95u) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Our Paper Got Accepted to AIES 2026. 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Research testing this metric against real human choices found it matched preferences only slightly better than a coin flip, suggesting compliance and actual helpfulness can diverge. engineers weighing compliance against real user outcomes can track findings like this on daily.dev."}}]} ```