Feel smarter, retweet scholarly articles you haven’t read! https://
x.com/emollick/statu
/emollick/status/1589006910480842752
…
ETHICS
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Sharing Scholarly Articles Without Reading Them Undermines Credibility
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The Importance of Explainable AI for User Adoption
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Le problème que je regrette, c'est qu'on arrivera pas à embarquer la masse avec des plateformes pensé par des devs. Les algo de reco c'est ouf. Il faut bien s'en servir et pas juste les jeter à la poubelle mais développer des approches d'XAI par exemple. https://
en.wikipedia.org/wiki/Explainab
le_artificial_intelligence
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Addressing data privacy and algorithmic issues in AI
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Mais on fait quoi ? Est ce que je suis en train de cracher sur des intiatives open source et faire l'apologie des plateformes tech, NON. J'alerte constamment sur la viligance de vos données et sur les dérives des algos d'IA, comme dans cette vidéo
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Platform moderation fees and fake news enforcement policy
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Now that we’ll have a fee for blue tick, how about a fine for fake news?
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Academic Review Process Rigor and Accountability Critique
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I learned more about the potential of overlap from your tweets than this metareview. That shows how lazy the metareview is. If the AC's gonna wield significant power to overturn three reviewers, they have to do more work than this. Read the paper and point out the exact overlap
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Team structure and ethical considerations in AI dataset creation
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I would say: smaller teams of full time people in a single org, simpler performance goals, more paid compute, no specific goals on ethical sourcing or social impact of the dataset creation process
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What Will Artificial Intelligence Do To Us?
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What Will #ArtificialIntelligence Do To Us? https://
currentaffairs.org/2022/11/what-w
ill-artificial-intelligence-do-to-us
… @curaffairs #AI #DeepLearning #Robots #BigData #Analytics #MachineLearning #100DaysofCode #IoT #serverless #womenwhocode #Rstats #Python #DigitalTransformation #TensorFlow #DataScience -
Why Tech Calls Machine Learning Systems ‘AI’
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Machine-learning systems are problematic. That’s why tech bosses call them ‘AI’ https://
theguardian.com/commentisfree/
2022/nov/05/machine-learning-systems-are-problematic-thats-why-tech-bosses-call-them-ai
… @guardian #AI #MachineLearning #DataScience #Robots #BigData #Analytics #100DaysofCode #IoT #serverless #womenwhocode #Python #DigitalTransformation #TensorFlow #DeepLearning -
Large AI Teams: BigScience Lessons on Project Organization
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yes, agree on the exhaustion feeling. BigScience was an experiment in trying to involve as many/diverse people as possible but a take-away is that too large teams can be counter-productive. Smaller projects like T0/ethics/RAIL were more successful inside BS than larger Bloom imo
