Robot | Path | Permission |
GoogleBot | / | ✔ |
BingBot | / | ✔ |
BaiduSpider | / | ✔ |
YandexBot | / | ✔ |
Title | Fairlearn |
Description | Improve fairness of AI Get Started User Guide API Docs Example Notebooks Contributor Guide FAQ About Us Improve fairness of AI systems Fairlearn is an open-source, community-dri |
Keywords | N/A |
WebSite | fairlearn.org |
Host IP | 185.199.108.153 |
Location | - |
Site | Rank |
US$2,840,871
Last updated: 2023-04-29 08:04:43
fairlearn.org has Semrush global rank of 3,725,725. fairlearn.org has an estimated worth of US$ 2,840,871, based on its estimated Ads revenue. fairlearn.org receives approximately 327,793 unique visitors each day. Its web server is located in -, with IP address 185.199.108.153. According to SiteAdvisor, fairlearn.org is safe to visit. |
Purchase/Sale Value | US$2,840,871 |
Daily Ads Revenue | US$2,623 |
Monthly Ads Revenue | US$78,671 |
Yearly Ads Revenue | US$944,044 |
Daily Unique Visitors | 21,853 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
fairlearn.org. | A | 1800 | IP: 185.199.108.153 |
fairlearn.org. | NS | 3600 | NS Record: ns22.domaincontrol.com. |
fairlearn.org. | NS | 3600 | NS Record: ns21.domaincontrol.com. |
Get Started User Guide API Docs Example Notebooks Contributor Guide FAQ About Us Improve fairness of AI systems Fairlearn is an open-source, community-driven project to help data scientists improve fairness of AI systems. Learn about AI fairness from our guides and use cases. Assess and mitigate fairness issues using our Python toolkit. Join our community and contribute metrics, algorithms, and other resources. Get Started FAIRNESS IS SOCIOTECHNICAL Fairness of AI systems is about more than simply running lines of code. In each use case, both societal and technical aspects shape who might be harmed by AI systems and how. There are many complex sources of unfairness and a variety of societal and technical processes for mitigation, not just the mitigation algorithms in our library. Throughout this website, you can find resources on how to think about fairness as sociotechnical, and how to use Fairlearn’s metrics and algorithms while considering the AI system’s broader societal context. |
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