# bias Published articles for bias. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Two Goals I Missed in August DevFeed: [Two Goals I Missed in August]() Original publisher: [Read original article]() Author: Prasad Rao Published: 2026-08-27T16:39:00Z Content type: opinion Language: en Sources: [Big Tech Careers]() Topics: [Tech Careers](), [Amazon Bedrock AgentCore](), [Amazon Web Services](), [OpenAI]() Tags: [amazon-bedrock-agentcore](), [aws](), [bias](), [careers](), [claude](), [funding](), [hiring](), [linkedin](), [promotion](), [tech-careers]() ### AI overview The author reflects on missing a weekly publishing goal and completing none of three planned Claude certifications. The article also argues that LinkedIn highlights successes while concealing the setbacks behind them, creating a distorted view through survivorship bias. ### Source excerpt LinkedIn is lying to you. What you don't see behind the scene! ## The benefits of medical AI assistance vary based on user expertise DevFeed: [The benefits of medical AI assistance vary based on user expertise]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-08-04T09:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [Human-AI evaluation](), [AI Development](), [AI Chat]() Tags: [ai](), [artificial-intelligence](), [bias](), [computer-science-and-technology](), [decision-making](), [dermatological-diagnosis](), [diagnosing-skin-disease](), [diagnostics](), [electrical-engineering-and-computer-science-eecs](), [explainability](), [explainable-ai](), [health-care](), [human-computer-interaction](), [institute-for-medical-engineering-and-science-imes](), [jameel-clinic](), [laboratory-for-information-and-decision-systems-lids](), [large-language-model](), [machine-learning](), [marzyeh-ghassemi](), [medicine](), [research](), [technology-and-society](), [users]() ### AI overview A study found that AI assistance improved skin-disease diagnosis for non-experts and clinicians, but explainability affected users differently. Non-experts often deferred to LLM-based explanations even when the AI was wrong, while clinicians performed best with the model's prediction alone. ### Source excerpt Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors. ## Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart DevFeed: [Leveraging PyFixest for High-Cardinality Marketplace Modeling at Instacart]() Original publisher: [Read original article]() Author: Benjamin Knight Published: 2026-06-29T16:06:24Z Content type: article Language: en Sources: [Instacart]() Topics: [benchmarking](), [math](), [Algorithms, Complexity](), [Software](), [Programming](), [Routing (disambiguation)]() Tags: [benchmarking](), [bias](), [cardinality](), [data-science](), [delivery](), [efficiency](), [estimator](), [fixed-effects-model](), [instacart](), [linear-regression](), [measurement](), [memory](), [precision](), [pyfixest](), [regression](), [routing](), [speed](), [statistics](), [techniques]() ### AI overview This Instacart article explains why ordinary least squares regression becomes computationally impractical for marketplace experiments with high-cardinality categories. It presents the mathematical basis for using Fixest and Pyfixest, discusses switchback experiment designs for addressing treatment spillover, and describes benchmarks comparing processing speed, memory efficiency, and estimator precision. ### Source excerpt Benjamin S. Knight Scaling Marketplace experiments requires specialized statistical techniques. We examine why standard ordinary least squares regression (OLS) becomes computationally intractable when controlling for high-cardinality categories. We then dive into the underlying math and demonstrate how modern packages -- specifically Fixest and Pyfixest -- bypass these limitations. We conclude by benchmarking these methods to show their real-world impact on processing speed, memory efficiency, and estimator precision. At Instacart we strive to give our customers access to all the fresh foods and ingredients that they would normally get from a trip to the grocery store, but without the hassle of driving, finding parking, waiting in line, etc. Instacart's Marketplace team is responsible for surfacing customers' orders to shoppers, aligning Instacart's delivery windows with shoppers' projected availabilities as efficiently as possible. This entails a careful balancing act. If we offer delivery windows that are sooner / more popular, then we risk overextending shoppers' ability to fulfill those orders on time. If we are too conservative in our delivery option offerings, then we risk losing potential orders. Accurately measuring the impact of changes in our batching and routing algorithms requires thoughtful experiment design and software. Better predictions of future demand / time-to-fulfill allow Instacart to offer more convenient delivery windows.Experimentation on Marketplace One of our primary concerns in Marketplace is treatment spillage. For example, if we adjust our batching algorithm and increase the rate at which multiple orders are combined into batches in Brooklyn and Queens, then we face a real risk of also influencing the rate of batch creation / completion in Staten Island, the Bronx, and Manhattan. In this case the treatment impacts the control group -- a classic source of measurement bias as a consequence of violating the Stable Unit Treatment Value Assumpt ## How Do Committees Fail To Invent? DevFeed: [How Do Committees Fail To Invent?]() Original publisher: [Read original article]() Author: Alex Russell Published: 2025-08-11T00:00:00Z Content type: opinion Language: en Sources: [Alex Russell]() Topics: [Development](), [Web platform](), [W3C]() Tags: [bias](), [browsers](), [consensus](), [development](), [engineering](), [standards](), [web-platform](), [webdev]() ### AI overview The article applies Conway's Law to standards development organizations, arguing that a "fifth column" problem can arise when delegates conceal their intentions through objections or silence. It examines how open membership, veto-heavy rules, and poor information can obstruct progress in standards working groups and affect developers' perceptions of standards. ### Source excerpt Mel Conway's seminal paper "How Do Committees Invent?" (PDF) is commonly paraphrased as Conway's Law: Organizations which design systems are (broadly) constrained to produce designs which are copies of the communication structures of these organizations. This is deep organisational insight that engineering leaders ignore at their peril, and everyone who delivers code for a living benefits from a (re)read of "The Mythical Man-Month", available at fine retailers everywhere. Conway's Law is generally invoked to describe organisations working to solve well-defined problems, in which everyone is working towards a solution. But what if there are defectors? And what if they can prevent forward progress without paying any price? This problem is rarely analysed for the simple reason that such an organisation would be deranged. But what if that happened regularly? I was reminded of the possibility while chatting with a colleague joining a new (to them) Working Group at the W3C. The most cursed expressions of Conway's Law regularly occur in Standards Development Organisations (SDOs); specifically, when delegates refuse to communicate their true intentions, either through spurious objection or tactical silence. This special case is the fifth column problem. Contents How Does This Happen? Mayfly Half-Lives The Web Platform's Power Structure But Why? Problem Misstatements Closed For Business Survivorship Bias and The Problem of Big, Old Rooms Marketing Gridlock As Thoughtfulness When Is A "Consensus" Not Consensus? Defences Against Fifth Columns Expressed in Conwayist terms, the fifth column problem describes the way organisations mirror the miscommunication patterns of their participants when they fail to deliver designs of any sort. This pathology presents when the goal of the majority is antithetical to a small minority with a veto. Reticence of certain SDO participants to consider important problems is endemic, in part, due to open membership. Unlike corporate environments wh ## The right way to interview DevFeed: [The right way to interview]() Original publisher: [Read original article]() Published: 2025-05-27T00:00:00Z Content type: opinion Language: en Sources: [Gaurav Sarma's Blog]() Topics: [bug](), [Job]() Tags: [bias](), [bug](), [career](), [experience](), [hiring](), [interview](), [pair](), [technical-interview]() ### AI overview The article argues that technical interviews should primarily use bug-squashing or pair-programming exercises, allowing interviewers to observe how candidates reason, respond to mistakes, challenge hypotheses, and work in practice. It also notes that these interviews require more preparation and may introduce subjective judgment and interviewer bias, while data-structures-and-algorithms questions can provide a more objective signal. ### Source excerpt Original Tweet - https://x. com/sarmag77/status/1938429909338362348 The best kind of interview is the bug squash or pair programmer round where both the interviewer and the candidate team up to solve a common problem... ## Powershell Users Like To Vomit DevFeed: [Powershell Users Like To Vomit]() Original publisher: [Read original article]() Published: 2025-01-05T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [PowerShell](), [data](), [Bash](), [Bluesky]() Tags: [bash](), [bias](), [data](), [number](), [powershell](), [quotes](), [social-media]() ### AI overview This satirical commentary examines how a small, biased Bluesky poll can produce misleading conclusions about PowerShell and Bash users. It argues that data should be questioned, especially when samples are non-random and quotes are taken out of context. ### Source excerpt In a stunning new study, PowerShell users insist that they like to vomit. How can this be? It's all about the data, and why you absolutely should question the data. ## Is There a Power Play Overhang? DevFeed: [Is There a Power Play Overhang?]() Original publisher: [Read original article]() Published: 2024-05-08T09:43:25Z Content type: opinion Language: en Sources: [Thomas Young]() Topics: [Artificial Intelligence](), [Development]() Tags: [agent](), [ai](), [bias]() ### AI overview The post examines risks from increasingly capable AI, focusing on whether adding agent-like abilities could create a sudden increase in the risk of losing control or extinction. It reframes agency overhang as "power play overhang" and argues that people may be too narrowly imagining how dangerous AI could develop. ### Source excerpt This post is about risks in the development of increasingly capable AI, in particular the risk of losing control to AI and extinction risk. I'll suggest that a key question is, "When do we need to take this kind of risk seriously?" We'll look at the issue of 'agency overhang', which suggests that adding agent-like abilities to AI could result in a sudden and surprising increase in these kinds of risks. I'll draw on intuitions about humans taking administrative and political control (with reference to the 'Dictator Book Club') and rephrase agency overhang as 'power play overhang'. I'll finish by suggesting that a lot of people may be making a subtle but important mistake in imagining just one fairly specific path to dangerous AI. Normalcy Bias From Wikipedia: Normalcy bias, or normality bias, is a cognitive bias which leads people to disbelieve or minimize threat warnings. Examples cited there include failure to react to natural disasters such as a tsunami or a volcanic eruption. In terms of effects: About 80% of people reportedly display normalcy bias in disasters.[3] Normalcy bias has been described as "one of the most dangerous biases we have". They can't do that! There's a powerful cliché, found in many books and films, in which we witness some scene early in the development of a totalitarian regime. In this cliché we see the surprise and disbelief of normal people as the regime begins to tighten its grip. A great example of this is in "The Handmaid's Tale" when, in the early episodes, many characters express disbelief at the rapid changes in society, such as women losing their jobs and bank accounts, saying things like "They can't do that!" as the totalitarian regime takes control. Other examples relate to historical events, such as: "The Lives of Others" Set in 1980s East Germany, this shows how the Stasi (secret police) gradually infiltrates every aspect of citizens' lives. "The Diary of a Young Girl" Anne Frank documents her surprise and disbelief at the in ## Intervening on early readouts for mitigating spurious features and simplicity bias DevFeed: [Intervening on early readouts for mitigating spurious features and simplicity bias]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-02-02T17:49:00Z Content type: article Language: en Sources: [Google Research]() Topics: [AI Research](), [Deep learning](), [responsible-ai](), [generalization in machine learning](), [Model Development](), [Training AI Models]() Tags: [bias](), [dataset](), [deep-learning](), [icml](), [machine-learning](), [ml-fairness](), [research](), [responsible-ai](), [supervised-learning](), [training]() ### AI overview Google Research describes methods for detecting and reducing spurious features and simplicity bias in deep learning models. Early readouts expose confidently wrong predictions associated with spurious features, while feature forgetting helps models identify more predictive features and generalize to unseen domains. ### Source excerpt Posted by Rishabh Tiwari, Pre-doctoral Researcher, and Pradeep Shenoy, Research Scientist, Google Research Machine learning models in the real world are often trained on limited data that may contain unintended statistical biases. For example, in the CELEBA celebrity image dataset, a disproportionate number of female celebrities have blond hair, leading to classifiers incorrectly predicting "blond" as the hair color for most female faces -- here, gender is a spurious feature for predicting hair color. Such unfair biases could have significant consequences in critical applications such as medical diagnosis. Surprisingly, recent work has also discovered an inherent tendency of deep networks to amplify such statistical biases, through the so-called simplicity bias of deep learning. This bias is the tendency of deep networks to identify weakly predictive features early in the training, and continue to anchor on these features, failing to identify more complex and potentially more accurate features. With the above in mind, we propose simple and effective fixes to this dual challenge of spurious features and simplicity bias by applying early readouts and feature forgetting. First, in "Using Early Readouts to Mediate Featural Bias in Distillation", we show that making predictions from early layers of a deep network (referred to as "early readouts") can automatically signal issues with the quality of the learned representations. In particular, these predictions are more often wrong, and more confidently wrong, when the network is relying on spurious features. We use this erroneous confidence to improve outcomes in model distillation, a setting where a larger "teacher" model guides the training of a smaller "student" model. Then in "Overcoming Simplicity Bias in Deep Networks using a Feature Sieve", we intervene directly on these indicator signals by making the network "forget" the problematic features and consequently look for better, more predictive features. This substanti ## "Practical Math" Preview: Collect Sensitive Survey Responses Privately DevFeed: ["Practical Math" Preview: Collect Sensitive Survey Responses Privately]() Original publisher: [Read original article]() Published: 2022-05-14T09:40:49Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [math](), [Statistics](), [Code](), [Software]() Tags: [bias](), [code](), [collect](), [differential-privacy](), [mathematics](), [politics](), [practical](), [practical-math](), [privacy](), [programming](), [python](), [random](), [randomized-algorithm](), [research](), [responses](), [survey]() ### AI overview This chapter preview explains randomized response, a survey technique for estimating aggregate statistics about sensitive questions while preserving respondents' privacy. Respondents use private coin flips to introduce randomization and maintain plausible deniability. ### Source excerpt This is a draft of a chapter from my in-progress book, Practical Math for Programmers: A Tour of Mathematics in Production Software. Tip: Determine an aggregate statistic about a sensitive question, when survey respondents do not trust that their responses will be kept secret. Solution: import random def respond_privately(true_answer: bool) -> bool: '''Respond to a survey with plausible deniability about your answer.''' be_honest = random.random() < 0.5 random_answer = random.random() < 0. ## Charley Pride (1934-2020) DevFeed: [Charley Pride (1934-2020)]() Original publisher: [Read original article]() Author: Adam Published: 2020-12-13T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [legacy](), [digital](), [Internet]() Tags: [bias](), [covid-19](), [digital](), [drm](), [legacy]() ### AI overview A personal remembrance of Charley Pride discusses his death from complications of Covid-19 and the DRM-protected edition of one of his albums, including how the protection could be bypassed and the differing prices of album editions. ### Source excerpt Early DRM artist recently passed away. ## Bias in word embeddings DevFeed: [Bias in word embeddings]() Original publisher: [Read original article]() Author: adriancolyer Published: 2020-12-08T14:32:00Z Content type: article Language: en Sources: [Adrian Colyer]() Topics: [Embeddings](), [Machine learning](), [Algorithms, Complexity]() Tags: [algorithm](), [bias](), [dataset](), [embeddings](), [machine-learning](), [model](), [train](), [uncategorized](), [word-embeddings]() ### AI overview This article summarizes research on bias in word embeddings, explaining how bias in training text can be encoded in embeddings, transferred to later algorithms, and produce socially discriminatory decisions. It also discusses detecting, measuring, and mitigating that bias. ### Source excerpt Bias in word embeddings, Papakyriakopoulos et al., FAT*'20 There are no (stochastic) parrots in this paper, but it does examine bias in word embeddings, and how that bias carries forward into models that are trained using them. There are definitely some dangers to be aware of here, but also some cause for hope as we ... Continue reading Bias in word embeddings ## Mitigating Social Bias in Knowledge Graphs DevFeed: [Mitigating Social Bias in Knowledge Graphs]() Original publisher: [Read original article]() Author: Adam Published: 2020-12-04T00:00:00Z Content type: opinion Language: en Sources: [Shostack & Friends Blog]() Topics: [Graphs](), [Embeddings](), [amazon]() Tags: [academic](), [amazon](), [bias](), [embeddings](), [graph](), [graphs](), [knowledge-graph](), [knowledge-graphs](), [paper]() ### AI overview The article discusses an Amazon team's paper on mitigating social bias in knowledge graph embeddings. It views the approaches as a useful starting point while noting that they are not panaceas. ### Source excerpt Something to consider ## 98. Книги, которые меня изменили DevFeed: [98. Книги, которые меня изменили]() Original publisher: [Read original article]() Published: 2018-08-21T06:26:38Z Content type: opinion Language: ru Sources: [Blog by Michael Dubakov]() Topics: [Agile]() Tags: [agile](), [bias]() ### AI overview A personal essay about books that had a lasting influence on the author. It discusses several works, including The Count of Monte Cristo, The Master and Margarita, Remarque's writing about war, and Peopleware, which the author says shaped their view of management and prepared them to embrace Agile methodologies. ### Source excerpt За свою жизнь я прочитал не так уж и много, около 1000 книг (половина из них пришлась на детский возраст). Наверное, большинство из них... ## Deep Probabilistic Modelling with Gaussian Processes #NIPS2017 DevFeed: [Deep Probabilistic Modelling with Gaussian Processes #NIPS2017]() Original publisher: [Read original article]() Published: 2017-12-04T12:00:00Z Content type: tutorial Language: en Sources: [Alex Korbonits]() Topics: [Tutorial](), [VAE](), [Machine learning](), [Deep learning](), [Neural Network](), [NeurIPS]() Tags: [analysis](), [bias](), [conference](), [data](), [deep-learning](), [gaussian](), [inference](), [modelling](), [neural-network](), [neurips](), [probabilistic](), [research](), [supervised-learning](), [theory](), [tutorial](), [unsupervised-learning](), [videos]() ### AI overview Lecture notes from a NeurIPS 2017 tutorial introduce deep probabilistic modelling with Gaussian processes, covering probabilistic neural networks, uncertainty, graphical models, and the computational challenge of inference. ### Source excerpt Lecture notes from Neil Lawrence's NIPS 2017 tutorial on deep probabilistic modelling with Gaussian processes -- from GPs to deep GPs and variational inference. ## Notes from NIPS 2017 on geometric deep learning, GAN theory, reinforcement learning, fairness, and Bayesian deep learning DevFeed: [Notes from NIPS 2017 on geometric deep learning, GAN theory, reinforcement learning, fairness, and Bayesian deep learning]() Original publisher: [Read original article]() Published: 2017-12-04T12:00:00Z Content type: opinion Language: en Sources: [Alex Korbonits]() Topics: [NeurIPS](), [Deep learning](), [Machine learning](), [Graphs](), [Reinforcement learning]() Tags: [3d](), [bias](), [deep-learning](), [fairness](), [graphs](), [ml](), [neurips](), [reinforcement-learning]() ### AI overview Personal notes from attending NIPS 2017 cover geometric deep learning on manifolds and graphs, Bayesian deep learning, fairness and bias, theory, deep reinforcement learning, and GANs. The article also reflects on how those research themes developed over the following years. ### Source excerpt Notes from NIPS 2017 -- covering geometric deep learning, GAN theory, reinforcement learning, fairness in ML, Bayesian deep learning, and more. ## One definition of algorithmic fairness: statistical parity DevFeed: [One definition of algorithmic fairness: statistical parity]() Original publisher: [Read original article]() Published: 2015-10-19T09:00:00Z Content type: opinion Language: en Sources: [Jeremy Kun]() Topics: [Algorithms](), [Algorithms, Complexity]() Tags: [algorithm](), [algorithms](), [bias](), [conditional-probability](), [discrimination](), [fairness](), [machine-learning](), [mathematics](), [programming](), [python](), [research](), [reverse-tokenism](), [self-fulfilling-prophecy](), [statistics]() ### AI overview The article examines statistical parity as one mathematical definition of algorithmic fairness. It explains the protected-group and population model, defines bias as the difference in positive classification rates between the complement and the protected group, and discusses the definition's intuitive basis and limitations. ### Source excerpt If you haven't read the first post on fairness, I suggest you go back and read it because it motivates why we're talking about fairness for algorithms in the first place. In this post I'll describe one of the existing mathematical definitions of "fairness," its origin, and discuss its strengths and shortcomings. Before jumping in I should remark that nobody has found a definition which is widely agreed as a good definition of fairness in the same way we have for, say, the security of a random number generator. ## Alexa Toolbar and the Problem of Experiment Design DevFeed: [Alexa Toolbar and the Problem of Experiment Design]() Original publisher: [Read original article]() Published: 2007-01-08T00:00:00Z Content type: article Language: en Sources: [Peter Norvig]() Topics: [Statistics](), [Internet](), [trust](), [data](), [Search engine optimization (SEO)]() Tags: [alexa](), [amazon](), [bias](), [https](), [internet](), [logs](), [party](), [ranking](), [search-engine-optimization](), [seo](), [statistics](), [trust]() ### AI overview The article examines whether Alexa traffic rankings accurately represent internet usage. It argues that the rankings are affected by selection bias because they measure users who installed the Alexa toolbar, a group that may be disproportionately interested in SEO, and illustrates the concern with comparisons between site logs and Alexa rankings. ### Source excerpt Can you trust statistics about internet usage?