# accountability Published articles for accountability. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Code is cheap, reliability isn't DevFeed: [Code is cheap, reliability isn't]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-02-16T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [reliability](), [Artificial Intelligence](), [Large Language Model](), [Code]() Tags: [accountability](), [ai](), [code](), [llms](), [ownership](), [reliability]() ### AI overview An interview with Swizec Teller examines production ownership and reliability in the AI era, including SLAs, useful logs, accountability, and the continuing need for human responsibility when using LLMs for coding. ### Source excerpt Humans of Reliability talks with Swizec Teller about owning production in the AI era. ## Unlocking Your Potential: The Mentee's Journey DevFeed: [Unlocking Your Potential: The Mentee's Journey]() Original publisher: [Read original article]() Author: Magda Miu Published: 2024-10-24T05:00:00Z Content type: tutorial Language: en Sources: [Magda Miu]() Topics: [Learning](), [meetings]() Tags: [accountability](), [article](), [best-practices](), [career](), [journey](), [magdamiu](), [mentee](), [mentoring](), [mentorship](), [relationships](), [remote](), [skills](), [tech-leadership]() ### AI overview A guide to being an effective mentee. It covers the benefits of mentorship, choosing a mentor, setting objectives, preparing for meetings, seeking feedback, maintaining a mentoring relationship, and adapting mentorship to remote work. ### Source excerpt Being a mentee is like having a seasoned explorer by your side, offering invaluable insights and tips as you navigate your career journey. It's a chance to learn from the wisdom and experiences of someone who's been there, done that, and is eager to share their knowledge. This article, just like the one about being a ... Continue reading Unlocking Your Potential: The Mentee's Journey ## Dysfunctional without knowing: 5 signs that your team might have a problem DevFeed: [Dysfunctional without knowing: 5 signs that your team might have a problem]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2023-02-25T06:00:00Z Content type: opinion Language: en Sources: [Tobias Mende]() Topics: [trust](), [function]() Tags: [accountability](), [collaboration-culture-psychological-safety-remote-work-technical-leadership-engineering-excelle](), [leadership](), [opinion](), [results](), [teams](), [trust]() ### AI overview The article describes five signs that a team may be dysfunctional without recognizing it: absence of trust, fear of conflict, lack of commitment, avoidance of accountability, and inattention to results. It explains how these problems build on one another and can be overlooked when a team appears harmonious or productive. ### Source excerpt Dysfunctional without knowing: 5 signs that your team might have a problem In the last 10+ years, I have worked in and with many teams. And I was fortunate to experience working in many fantastic teams. Teams where people cared for each other, trusted each other and were committed to deliver the best results together. People, where conflicts and disagreements were beneficial and important to achieve better results. ## 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.