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Bias Mitigation

24 articles · Page 1 of 1
Huolala Tech
Huolala Tech
Aug 12, 2026 · Artificial Intelligence

Redefining Agent Evaluation: An Offline LLM-as-a-Judge Framework and Practice

The article presents a quantitative offline evaluation system for AI agents in real outbound-call scenarios, combining reference‑based scoring with pairwise GSB methods, addressing regression and optimization, mitigating systematic bias through judge model selection and majority‑vote adjustments, and delivering an objective, high‑efficiency benchmark.

Agent EvaluationBias MitigationGSB
0 likes · 16 min read
Redefining Agent Evaluation: An Offline LLM-as-a-Judge Framework and Practice
Smart Workplace Lab
Smart Workplace Lab
Jun 3, 2026 · Artificial Intelligence

Who Bears Hidden Liability When Using AI for Resume Screening? A Fairness Audit Guide

The article recounts a real‑world AI resume‑screening failure, explains how unchecked historical bias propagates, and presents a three‑step, audit‑able fairness protocol—including bias‑word scanning, manual calibration, and abnormal‑deviation routing—to reduce compliance risk by about 70% while preserving screening speed.

AI recruitmentBias MitigationHR compliance
0 likes · 6 min read
Who Bears Hidden Liability When Using AI for Resume Screening? A Fairness Audit Guide
Xiaolong Cloud Tech Team
Xiaolong Cloud Tech Team
Nov 11, 2025 · Artificial Intelligence

Designing Effective Few-Shot Prompts: Principles, Bias Mitigation, and Scaling Strategies

The article explains how few‑shot prompts—using only 2‑5 task examples—enhance model performance, outlines prompt structure and design principles, examines factors such as example quantity, ordering, label distribution and format, discusses bias sources and calibration, and presents methods for generating many examples with reinforced and unsupervised in‑context learning.

Bias MitigationLarge Language ModelsPrompt Engineering
0 likes · 11 min read
Designing Effective Few-Shot Prompts: Principles, Bias Mitigation, and Scaling Strategies
KooFE Frontend Team
KooFE Frontend Team
Nov 6, 2025 · Artificial Intelligence

Mastering Few-Shot Prompting: Principles, Bias Fixes, and Example Design

Few-shot prompting uses a handful of task examples within the prompt to guide large language models, improving performance, adaptability, and reducing data needs, while careful design of example quantity, order, label distribution, format, and bias mitigation—through calibration and advanced methods like reinforced and unsupervised ICL—optimizes results.

Bias MitigationPrompt Engineeringexample design
0 likes · 11 min read
Mastering Few-Shot Prompting: Principles, Bias Fixes, and Example Design
DataFunSummit
DataFunSummit
Jul 14, 2024 · Artificial Intelligence

Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This article surveys recent advances in applying causal inference to recommender systems, presenting three lines of work—causal embedding for interest‑conformity disentanglement, contrastive learning for long‑term and short‑term interest separation, and adversarial debiasing of duration bias in short‑video recommendation—along with experimental validation and insights.

Bias Mitigationcausal inferenceinterest disentanglement
0 likes · 24 min read
Causal Inference for Recommender Systems: Disentangling Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations
DataFunSummit
DataFunSummit
May 1, 2024 · Artificial Intelligence

Causal Solutions for Recommendation System Bias and Practical Applications

This article presents causal inference–based methods to address bias in recommendation systems, covering the transformation of recommendation problems into causal problems, selection bias mitigation through double‑robust and multi‑robust learning, individual treatment effect estimation, and a case study on attention bias in music recommendation.

Bias Mitigationcausal inferencedouble robust learning
0 likes · 12 min read
Causal Solutions for Recommendation System Bias and Practical Applications
Sohu Tech Products
Sohu Tech Products
Apr 10, 2024 · Artificial Intelligence

Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations

The presentation surveys recent causal‑inference research for recommendation systems, introducing the DICE framework to separate user interest from conformity, the CLSR model to disentangle long‑term and short‑term preferences, and the DVR approach with WTG metrics to debias short‑video recommendations, demonstrating improved accuracy, fairness, and interpretability.

Bias Mitigationcausal inferenceinterest disentanglement
0 likes · 23 min read
Causal Inference in Recommendation Systems: Disentangling Interests and Debiasing Short Video Recommendations
DataFunTalk
DataFunTalk
Apr 7, 2024 · Artificial Intelligence

Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations

This presentation reviews recent research on applying causal inference to recommendation systems, covering causal embedding for separating user interest and conformity, contrastive learning for disentangling long‑term and short‑term interests, and a debiasing framework for short‑video recommendation that uses watch‑time‑gain metrics and adversarial learning to mitigate duration bias.

Bias Mitigationcausal inferenceinterest disentanglement
0 likes · 23 min read
Causal Inference for Recommendation Systems: Disentangling User Interest, Conformity, Long‑Term/Short‑Term Interests, and Debiasing Short‑Video Recommendations
DataFunTalk
DataFunTalk
Oct 1, 2023 · Artificial Intelligence

Research and Product Applications of Causal Inference for Solving Recommendation System Bias

In this talk, senior researcher Dai Quanyu from Huawei Noah's Ark Lab presents his work on applying causal inference to identify and correct various biases in recommendation systems, detailing underlying theoretical frameworks, bias‑mitigation algorithms such as inverse propensity weighting and robust learning, and real‑world product deployments.

AIBias MitigationRecommendation Systems
0 likes · 3 min read
Research and Product Applications of Causal Inference for Solving Recommendation System Bias
21CTO
21CTO
Mar 10, 2023 · Artificial Intelligence

Inside OpenAI’s Robotics: Lilian’s Journey, AGI Vision, and AI Safety Insights

The interview with OpenAI Robotics researcher Lilian reveals the team’s gender makeup, her work on robot hands, reinforcement‑learning breakthroughs, applied AI safety projects, bias mitigation efforts, and how personal learning blogs fuel continuous innovation in artificial intelligence.

AGIBias MitigationOpenAI
0 likes · 11 min read
Inside OpenAI’s Robotics: Lilian’s Journey, AGI Vision, and AI Safety Insights
JD Cloud Developers
JD Cloud Developers
Feb 27, 2023 · Artificial Intelligence

How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking

The article explains JD’s Explore & Exploit (EE) module, its bias‑related challenges, the iterative optimization loop, model debiasing techniques for position and popularity bias, personalized bias modeling, causal inference methods, online AB results, and offline evaluation metrics, highlighting significant improvements in search diversity and efficiency.

Bias MitigationEE moduleRecommendation Systems
0 likes · 16 min read
How JD’s Explore & Exploit Module Tackles Position and Popularity Bias in Search Ranking
DataFunSummit
DataFunSummit
Feb 23, 2023 · Artificial Intelligence

An Introduction to Causal Inference: Concepts, Methods, and Real‑World Applications

This article provides a comprehensive overview of causal inference, explaining its definition, the distinction between correlation and causation, classic pitfalls such as Simpson's paradox, key metrics like ATE and ATT, experimental designs, bias mitigation techniques, and practical case studies from content platforms and the Titanic dataset.

A/B testingBias Mitigationcausal inference
0 likes · 22 min read
An Introduction to Causal Inference: Concepts, Methods, and Real‑World Applications
DataFunTalk
DataFunTalk
Jan 25, 2023 · Artificial Intelligence

Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds

This article presents a comprehensive overview of the background, current multi‑path recall methods, and a series of practical optimizations—including dual‑tower models, enhanced vectors, an unbiased IPW‑based framework, and a travel‑state‑aware deep recall model—applied to Feizhu's homepage recommendation system, with both offline and online experimental results demonstrating click‑through rate improvements.

Bias MitigationRecommendation Systemsdual-tower model
0 likes · 17 min read
Optimizing Vector Recall for Feizhu's Homepage "You May Like" Recommendation Feeds
Alimama Tech
Alimama Tech
Dec 14, 2022 · Artificial Intelligence

Contrastive Image Representation Learning with Debiasing for CTR Prediction

The article proposes a three-stage contrastive learning framework—pre‑training, fine‑tuning, and debiasing—to generate unbiased, fine‑grained image embeddings for mobile Taobao CTR prediction, achieving higher accuracy, fairness, and a 4‑5% CTR lift in large‑scale offline and online evaluations.

Bias MitigationCTR predictioncontrastive learning
0 likes · 14 min read
Contrastive Image Representation Learning with Debiasing for CTR Prediction
Zhuanzhuan Tech
Zhuanzhuan Tech
Oct 14, 2022 · Artificial Intelligence

Exploitation and Exploration in Recommendation Systems: Bias Types, Mitigation Strategies, and Diversity Optimization

The article explains how recommendation systems balance exploitation and exploration, details various bias sources such as selection, exposure, conformity, and position bias, presents mitigation techniques like feature input, bias towers, and greedy algorithms, and discusses diversity‑focused exploration using DPP methods.

Bias MitigationDiversityExploration-Exploitation
0 likes · 7 min read
Exploitation and Exploration in Recommendation Systems: Bias Types, Mitigation Strategies, and Diversity Optimization
DataFunTalk
DataFunTalk
Jun 24, 2022 · Artificial Intelligence

Explore‑and‑Exploit (EE) in JD Search: Bias Mitigation, Model Iteration, and Evaluation

The talk presents JD Search's Explore‑and‑Exploit (EE) module, detailing its bias‑mitigation pipeline—including position, popularity, and exposure debiasing—model architecture upgrades with SVGP and causal inference, online AB metrics, offline evaluation methods, and future research directions to improve search diversity and long‑term value.

Bias MitigationSVGPexplore‑exploit
0 likes · 17 min read
Explore‑and‑Exploit (EE) in JD Search: Bias Mitigation, Model Iteration, and Evaluation
DataFunSummit
DataFunSummit
May 26, 2022 · Artificial Intelligence

Exploring Contrastive Learning in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how contrastive learning can alleviate data sparsity and distribution bias in recommendation systems, detailing its theoretical advantages, recent research progress in computer vision and NLP, and a multi‑task self‑supervised framework applied to Kuaishou's short‑video ranking pipeline with significant offline and online performance gains.

AIBias MitigationKuaishou
0 likes · 21 min read
Exploring Contrastive Learning in Kuaishou Recommendation Systems
Kuaishou Tech
Kuaishou Tech
Feb 24, 2022 · Artificial Intelligence

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

This article presents a comprehensive overview of how causal inference techniques are applied to identify and correct various biases in Kuaishou's recommendation pipeline, covering background theory, recent research, practical implementations such as popularity debias, causal embedding decoupling, and video completion‑rate debias, along with experimental results and future challenges.

Bias MitigationEmbeddingKuaishou
0 likes · 19 min read
Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems
DataFunTalk
DataFunTalk
Feb 21, 2022 · Artificial Intelligence

Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems

This talk explains how recommendation bias arises from popularity and position effects, introduces causal inference concepts and three inference levels, reviews recent research such as DICE and Huawei’s causal embedding, and details Kuaishou’s practical applications—including popularity debias, causal representation decoupling, and video completion‑rate debias—along with experimental results and future challenges.

Bias MitigationKuaishoumachine learning
0 likes · 20 min read
Causal Inference for Bias Mitigation in Kuaishou Recommendation Systems
DataFunTalk
DataFunTalk
Apr 16, 2021 · Artificial Intelligence

Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688

This article presents a comprehensive overview of Alibaba's 1688 live‑streaming recommendation system, detailing core challenges such as heterogeneous behavior modeling, multi‑objective optimization, and bias mitigation, and describing four successive model iterations—from feature‑engineered GBDT to attention‑based heterogeneous networks and transformer architectures—along with experimental results and practical insights.

Bias MitigationRecommendation SystemsTransformer
0 likes · 22 min read
Live Streaming Recommendation Ranking Model Evolution and Multi‑Objective Learning at Alibaba 1688
Meituan Technology Team
Meituan Technology Team
Aug 20, 2020 · Artificial Intelligence

Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation

The winning KDD Cup 2020 debiasing solution builds a heterogeneous item‑to‑item graph with click‑co‑occurrence and multimodal similarity edges, uses multi‑hop random walks to generate unbiased candidate samples, trains LightGBM with a popularity‑weighted loss, and aggregates scores to lift low‑popularity items, thereby eliminating selection and popularity bias and achieving first place among 1,895 teams.

AdvertisingBias MitigationGraph Modeling
0 likes · 23 min read
Debiasing Competition Solution: Multi‑hop i2i Graph Modeling for Advertising Recommendation
Tencent Cloud Developer
Tencent Cloud Developer
Dec 3, 2019 · Artificial Intelligence

Feature Engineering Practices for Short‑Video Recommendation Systems

Effective short‑video recommendation relies on meticulous feature engineering that transforms raw signals—numerical counts, categorical IDs, content and user embeddings, context and session data—through bucketization, scaling, crossing, and smoothing, then selects and evaluates them via filtering, wrapping, regularization, and importance analysis to mitigate business biases and improve multi‑objective ranking performance.

Bias MitigationEmbeddingdata preprocessing
0 likes · 32 min read
Feature Engineering Practices for Short‑Video Recommendation Systems
JD.com Experience Design Center
JD.com Experience Design Center
Jun 28, 2019 · R&D Management

How Cognitive Biases Skew User Research—and How to Counteract Them

This article explains common cognitive biases that affect user research—such as friendliness, social desirability, bandwagon, Hawthorne, anchoring, and peak‑end effects—and provides practical strategies like combining backend data, reducing participant concerns, minimizing external influences, and probing deeper to obtain more reliable, objective insights.

Bias MitigationR&DUX
0 likes · 11 min read
How Cognitive Biases Skew User Research—and How to Counteract Them