# fairness Published articles for fairness. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Testing Trust in Prediction Markets | Part 1 DevFeed: [Testing Trust in Prediction Markets | Part 1]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-03T13:15:00Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [integrity]() Tags: [data-insights](), [fairness](), [fraud](), [integrity](), [prediction-markets](), [prediction-markets-legislation](), [trust]() ### AI overview Part 1 examines how reported insider trading, irregularities, settlement issues, and operational design choices in prediction markets can undermine claims about fairness, market integrity, collective intelligence, and truth. It argues that gaps between provider claims and market realities can erode public trust and invite political or regulatory responses. ### Source excerpt This April, U.S. federal prosecutors charged an Army sergeant with using sensitive classified information to bet on Polymarket that U.S. forces would enter Venezuela and remove Maduro from power. About the same time, Kalshi disclosed that it had fined and suspended three congressional candidates for five years after they traded on prediction markets tied to [...] The post Testing Trust in Prediction Markets | Part 1 appeared first on Sift. ## 【kube-apiserver】APF 与 max-in-flight：公平排队、504 与 etcd lag 分列 DevFeed: [【kube-apiserver】APF 与 max-in-flight：公平排队、504 与 etcd lag 分列]() Original publisher: [Read original article]() Author: Liao Tonglang Published: 2026-08-28T00:00:00Z Content type: article Language: zh Sources: [土法炼钢 - 系统与基础设施]() Topics: [Kubernetes](), [API](), [SRE](), [Linux]() Tags: [apf](), [api](), [apiserver](), [distributed](), [etcd](), [fairness](), [fairqueuing](), [flowcontrol](), [k8s](), [kubernetes](), [lag](), [max-in-flight](), [priority](), [timeout](), [v1-30-3]() ### AI overview This article explains how Kubernetes v1.30.3 protects kube-apiserver from overload through API Priority and Fairness (APF) and the older max-in-flight limits. It distinguishes 429 responses, APF queue timeouts that can produce 504 responses before storage is reached, etcd latency, and admission webhook delays, and describes APF's FlowSchema, PriorityLevelConfiguration, fair queuing, and shuffle sharding mechanisms. ### Source excerpt 钉 K8s v1.30.3 的 API Priority and Fairness（APF）：FlowSchema 匹配、PriorityLevelConfiguration 公平排队（SFVR）、与旧 max-in-flight flag 的共存关系；429/timeout/504 在 APF 排队、etcd_request_duration_seconds、Admission Webhook 三轴的分列；pkg/util/flowcontrol 路径；APF 与简单 max-in-flight 的运维复杂度争论。 ## Task Queue Priority and Fairness: Your Task Queue, your way DevFeed: [Task Queue Priority and Fairness: Your Task Queue, your way]() Original publisher: [Read original article]() Author: Conna Lanzafane Published: 2026-05-12T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [SDKs]() Tags: [fairness](), [priority](), [product-news](), [queue](), [queues](), [scheduling](), [sdks]() ### AI overview Temporal Task Queue Priority and Fairness is generally available for all SDKs. Priority controls task execution order, while Fairness prevents a single Workflow, user, or tenant from monopolizing Workers. ### Source excerpt Task Queue Priority and Fairness are GA for all Temporal SDKs, helping teams prioritize critical work and prevent tenant starvation without custom infra. ## Aequitas Flow step-by-step: a Fair ML optimization framework DevFeed: [Aequitas Flow step-by-step: a Fair ML optimization framework]() Original publisher: [Read original article]() Author: Sérgio Jesus Published: 2024-08-12T14:35:43Z Content type: tutorial Language: en Sources: [Feedzai]() Topics: [Machine learning](), [Optimization](), [audit](), [Benchmark](), [pandas](), [datasets](), [interfaces]() Tags: [audit](), [benchmark](), [datasets](), [fairness](), [interfaces](), [machine-learning](), [ml](), [open-source](), [optimization](), [pandas](), [reproducibility](), [research](), [responsible-ai]() ### AI overview This tutorial introduces Aequitas Flow, an open-source framework for standardized experiments with fair machine-learning algorithms. It explains how the framework extends Aequitas fairness auditing with reproducible experimentation, support for custom datasets and methods, and workflows for practitioners selecting models and researchers running benchmarks. ### Source excerpt By Sérgio Jesus, Inês Silva, Pedro Saleiro, Hugo Ferreira, Pedro Bizarro In this blog post we will visit Aequitas Flow, an Open-Source framework designed to run complete and standardized experiments of Fair ML algorithms. We encourage you to try Aequitas Flow with the Google Colab Notebooks, which are available in the project's GitHub repository. This blog post is based on the paper by Sérgio Jesus, Pedro Saleiro, Inês Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, and Rayid Ghani. Table of Contents: 1. What is Aequitas Flow? - 1.1. For Practitioners selecting a model - 1.2. For Researchers running a benchmark 2. Install Aequitas Flow 3. The components of Aequitas Flow - 3.1. Experiment - 3.2. Optimizer - 3.3. Datasets - 3.4. Methods - 3.5. Audit 4. Conclusion What is Aequitas Flow? Aequitas Flow is the codename for the latest version of Aequitas, a well-established package for fairness auditing in the ML community. This version extends the package to include experimentation with Fair ML algorithms. Aequitas started as a software to diagnose and alert for disparities in ML models' decisions depending on sensitive attributes, such as race, gender, or age. To achieve this, the package runs a Bias Audit, in which it calculates several metrics across all data groups, determined by the sensitive attributes, and compares them to identify any gap in performance. While some other packages already implement Fair ML methods, they also introduce an overhead of technical knowledge to configure, evaluate, and deploy these models. Because of this, we extended Aequitas to enable users to conduct experiments using a wide variety of fair ML methods from the literature in an intuitive user experience. Aequitas Flow was built on the principles of extensibility and reproducibility: it allows users to incorporate their own datasets and methods in the framework using familiar interfaces of sci-kit-learn and pandas. It ensures their work can be replicated by performin ## HEAL: A framework for health equity assessment of machine learning performance DevFeed: [HEAL: A framework for health equity assessment of machine learning performance]() Original publisher: [Read original article]() Author: Google AI (noreply@blogger.com) Published: 2024-03-15T18:22:00Z Content type: article Language: en Sources: [Google Research]() Topics: [Machine learning](), [Artificial Intelligence](), [Google](), [AI Research]() Tags: [ai](), [fairness](), [google](), [health](), [machine-learning](), [research](), [responsible-ai]() ### AI overview Google Research presents HEAL, a framework for quantitatively assessing whether machine-learning health technologies perform equitably. The framework evaluates model performance in relation to pre-existing health disparities and focuses on populations with the worst health outcomes, illustrated through a dermatology AI model case study. ### Source excerpt Posted by Mike Schaekermann, Research Scientist, Google Research, and Ivor Horn, Chief Health Equity Officer & Director, Google Core Health equity is a major societal concern worldwide with disparities having many causes. These sources include limitations in access to healthcare, differences in clinical treatment, and even fundamental differences in the diagnostic technology. In dermatology for example, skin cancer outcomes are worse for populations such as minorities, those with lower socioeconomic status, or individuals with limited healthcare access. While there is great promise in recent advances in machine learning (ML) and artificial intelligence (AI) to help improve healthcare, this transition from research to bedside must be accompanied by a careful understanding of whether and how they impact health equity. Health equity is defined by public health organizations as fairness of opportunity for everyone to be as healthy as possible. Importantly, equity may be different from equality. For example, people with greater barriers to improving their health may require more or different effort to experience this fair opportunity. Similarly, equity is not fairness as defined in the AI for healthcare literature. Whereas AI fairness often strives for equal performance of the AI technology across different patient populations, this does not center the goal of prioritizing performance with respect to pre-existing health disparities. Health equity considerations. An intervention (e.g., an ML-based tool, indicated in dark blue) promotes health equity if it helps reduce existing disparities in health outcomes (indicated in lighter blue). In "Health Equity Assessment of machine Learning performance (HEAL): a framework and dermatology AI model case study", published in The Lancet eClinicalMedicine, we propose a methodology to quantitatively assess whether ML-based health technologies perform equitably. In other words, does the ML model perform well for those with the worst he ## Why Playing Team Sports Makes Me a Better Developer DevFeed: [Why Playing Team Sports Makes Me a Better Developer]() Original publisher: [Read original article]() Author: Eevis Panula Published: 2023-01-03T08:57:50.675000Z Content type: opinion Language: en Sources: [Eevis Blog]() Topics: [Sports](), [Job]() Tags: [blog-post](), [coaching](), [developer](), [fairness](), [sports](), [team](), [work]() ### AI overview The author reflects on how years of team sports, coaching, and organizational involvement have influenced their work as a developer. The article focuses on teamwork, sportsmanship, collaboration, and learning from diverse teammates, while noting that these benefits may also come from other activities. ### Source excerpt I've played team sports since I was something like ten. First, it was floorball - this fantastic indoor sport, where you have a stick, a ball, and goals. Something similar to hockey, but less violent. And after all, completely different. Then I moved to another city and soon realized that it's not as fun as it used to be with the team I had played with for 10 years. I switched to ultimate frisbee, which is something I still play. I also did a tour in the roller derby world, loved the sport but got a brain injury. I realized that if I want to be able to do my job, I need to let this awesome thing go. I'm sometimes still sad about it. So, I have a history (and hopefully future) with team sports. I've done some coaching during the years and participated in different boards - be it a club or national level organization. So I could say I've gotten a lot from this and also learned a ton. One day, while I was doing some ladder drills (they're for agility on the field), I started thinking if there has been anything I've used from my sports background in my day-to-day job. After pondering for a while, I realized that yes, there is. In this blog post, I will share some of those thoughts. Many other activities may provide just the same benefits. However, my background is in team sports, so that's why my angle is from the perspective of team sports. Team Spirit (of the Game) If you're not familiar with ultimate frisbee, you might wonder what the heck the title of this section means. Let me explain. In ultimate frisbee, there is this thing called "Spirit of the Game," which has to do with, for example, fairness of the game and sportsmanship. It's something that makes the sport possible to play without any referees (yup, you read it right!). During the years, being part of a team has indeed taught me to be a better team member. Especially being part of teams that comply with such philosophy as the Spirit of the Game. I know how to work in a group, and also that different people h ## Reasoning about asyncio.Semaphore DevFeed: [Reasoning about asyncio.Semaphore]() Original publisher: [Read original article]() Author: Guido van Rossum (noreply@blogger.com) Published: 2022-10-05T06:39:00Z Content type: article Language: en Sources: [Guido van Rossum]() Topics: [Concurrency](), [Concurrent Programming](), [await]() Tags: [await](), [fairness](), [implementing](), [performance](), [reasoning](), [semantics](), [synchronization]() ### AI overview The article explains asyncio synchronization primitives through a restaurant queuing analogy. It maps exclusive access and cancellation to a Lock, then explains why multiple concurrently seated guests require a Semaphore. It also discusses challenges involving fairness, correctness, semantics, and performance. ### Source excerpt In Silicon Valley is a very exclusive fast-food restaurant, which is always open. There is one table, where one guest at a time is served an absolutely fabulous hamburger. When you arrive, you wait in line until the table is available. Then the host takes you to the table and, this being America, you are asked a seemingly endless series of questions about how you would like your hamburger to be cooked and served. But today we're not talking about culinary delights. We're talking about the queuing system used by the restaurant. If you are lucky to arrive at the restaurant when the table is available and there are no other guests waiting, you are seated right away. Otherwise, the host gives you a buzzer (from an infinite stack of buzzers!) and you are free to roam the neighborhood until your buzzer goes off. It is the host's job to ensure that guests are seated in order of arrival. When it is your turn, the host will cause your buzzer go off and you make your way back to the restaurant, where you will be seated. If you change your mind, you can return the buzzer to the host, who will take it back without lifting an eyebrow. If your buzzer has already gone off, the host will buzz the next guest, if any. Guests are always polite and don't abscond with their buzzers. The host is always fair and doesn't seat another guest ahead of you even if you take your time making it back. The above description fits that of a Lock. A guest arriving corresponds to the acquire() call; leaving is a release() call. Changing your mind is like getting cancelled while waiting in acquire(). You can change your mind before or after your buzzer goes off, i.e., you can be cancelled before or after the lock has awakened your call (but before you return from acquire()). One day the restaurant expands, hiring extra sous-chefs and opening several new tables. There is still only one host, whose job is not really changed. However, since multiple guests can be seated concurrently, a Semaphore must now ## What is it like to interview with Apollo? (part 1 of 2) DevFeed: [What is it like to interview with Apollo? (part 1 of 2)]() Original publisher: [Read original article]() Author: Chris Shaw Published: 2021-08-13T14:19:33Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [consistency]() Tags: [alignment](), [fairness](), [hiring](), [interview](), [interviewing](), [interviews](), [job](), [life-at-apollo](), [onboarding](), [process](), [recruiting](), [skills]() ### AI overview Apollo's first article about its interview process explains how the company prepares interviewers and candidates, defines roles through missions, outcomes, and competencies, and designs interviews for consistency and fairness. ### Source excerpt We've all been there. You're interviewing for a company that has a job you're very interested in. But you have no idea what to expect in the interview process. You're wondering; what will this process actually be like? How much time will it take? Should I prepare? How much should I prepare? Will I get a chance to ask my questions so I can learn more about the company? ## Thoughts from AAAI 2019 DevFeed: [Thoughts from AAAI 2019]() Original publisher: [Read original article]() Author: Loren Puchalla Fiore Published: 2019-05-13T00:00:00Z Content type: opinion Language: en Sources: [Jane Street]() Topics: [Machine Learning & Artificial Intelligence](), [Deep neural networks](), [Feature Engineering]() Tags: [ai](), [conference](), [fairness](), [feature-engineering](), [machine-learning](), [neural-networks]() ### AI overview Jane Street colleagues reflect on themes and projects presented at AAAI 2019, including neural-network interpretability, adversarial weaknesses in interpretation techniques, classical AI, AI for social good, social AI, and fairness in machine learning. ### Source excerpt At Jane Street, for the last several years, we have been increasingly interested in machine learning and its many use cases. This is why it was exciting when earlier this year myself and a few of my colleagues had the opportunity to attend the AAAI 2019 conference. We'd like to take this space to share with you some of the interesting projects and themes we saw at the conference. ## NIPS 2017 Summary DevFeed: [NIPS 2017 Summary]() Original publisher: [Read original article]() Published: 2017-12-11T00:00:00Z Content type: article Language: en Sources: [Alex Korbonits]() Topics: [NeurIPS](), [AI Research](), [Deep learning](), [Deep neural networks](), [Reinforcement learning]() Tags: [ai-research](), [deep-learning](), [fairness](), [machine-learning](), [model](), [model-interpretability](), [neurips](), [open-source](), [papers](), [reinforcement-learning](), [research]() ### AI overview A summary of key themes from NIPS 2017, including rapid progress in Bayesian deep learning, growing attention to model interpretability, bias, and fairness, the need for more theory in deep learning, advances in deep reinforcement learning, and ongoing questions about GANs. ### Source excerpt Key takeaways from NeurIPS 2017: Bayesian deep learning, model interpretability, fairness, and the state of AI research. ## 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. ## What does it mean for an algorithm to be fair? DevFeed: [What does it mean for an algorithm to be fair?]() Original publisher: [Read original article]() Published: 2015-07-13T09:00:00Z Content type: opinion Language: en Sources: [Jeremy Kun]() Topics: [Algorithms](), [Algorithms, Complexity](), [Machine learning](), [Programming]() Tags: [accountability](), [algorithm](), [algorithms](), [data-mining](), [deep-learning](), [discrimination](), [disparate-impact](), [fairness](), [google](), [law](), [machine-learning](), [mathematics](), [neural-networks](), [transparency]() ### AI overview The article examines algorithmic fairness and argues that algorithms trained on historical human data can facilitate illegal discrimination and reinforce social prejudices. It uses targeted loan advertising, Google autocomplete, and predictive policing as examples, though the supplied text is incomplete. ### Source excerpt In 2014 the White House commissioned a 90-day study that culminated in a report (pdf) on the state of "big data" and related technologies. The authors give many recommendations, including this central warning. Warning: algorithms can facilitate illegal discrimination! Here's a not-so-imaginary example of the problem. A bank wants people to take loans with high interest rates, and it also serves ads for these loans. A modern idea is to use an algorithm to decide, based on the sliver of known information about a user visiting a website, which advertisement to present that gives the largest chance of the user clicking on it. ## Optimally Stacking the Deck--Texas Hold 'Em DevFeed: [Optimally Stacking the Deck--Texas Hold 'Em]() Original publisher: [Read original article]() Published: 2012-04-09T09:00:26Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [math](), [Programming]() Tags: [card-games](), [fairness](), [gambling](), [permutations](), [poker](), [steepest-ascent](), [texas-hold-em]() ### AI overview The article solves a combinatorial puzzle about arranging a standard two-player Texas Hold 'Em deck so that one player wins regardless of where the deck is cut. It explains how cutting the deck normally supports fairness and frames optimal stackings as a measure of game complexity. ### Source excerpt Main Theorem: There exist optimal stackings for standard two-player Texas Hold 'Em. A Puzzle is Solved (and then some!) It's been quite a while since we first formulated the idea of an optimal stacking. In the mean time, we've gotten distracted with graduate school, preliminary exams, and the host of other interesting projects that have been going on here at Math ∩ Programming. And so months later, after traversing the homotopic hills of topology and projective plains of algebra, we've finally found time to solve the problem. ## In Place Uniform Shuffle DevFeed: [In Place Uniform Shuffle]() Original publisher: [Read original article]() Published: 2012-03-18T20:33:42Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithms](), [Python]() Tags: [algorithm](), [fairness](), [permutations](), [python]() ### AI overview A Python implementation of an in-place uniform shuffle is presented. The article explains why uniform randomness matters for shuffling and derives a process that selects a random remaining element at each position, using constant extra space and linear time. ### Source excerpt Problem: Write a program that shuffles a list. Do so without using more than a constant amount of extra space and linear time in the size of the list. Solution: (in Python) import random random.seed() def shuffle(myList): n = len(myList) for i in xrange(0, n): j = random.randint(i, n-1) # randint is inclusive myList[i], myList[j] = myList[j], myList[i] Discussion: Using a computer to shuffle a deck of cards is nontrivial at first glance for the following reasons.