New Discussion Paper on "Dynamics of Data Work in AI Implementation Processes":

In their study, Lea Schneidemesser, Daniel Schneiß, and Jana Heim investigate the role of data work in the implementation of #AI systems in traditional industries, exploring the labor and power dynamics embedded in these processes.

More: 🔗 https://doi.org/10.34669/WI.DP/50

#socialscience #research #work #labor ##DataWork #TechAndSociety #FutureOfWork #openaccess #publication @WZB_Berlin @FOKUSpublic @tuberlin

🏅 Weizenbaum Researcher @milamiceli was included on TIME's list of Most Influential People in Artificial Intelligence of 2025. Congratulations on this distinction and your incredible work for and with data workers, Mila! 🎉 #TIME100AI #AI #DataWork

Find out more 👉 https://time.com/collections/time100-ai-2025/7305825/milagros-miceli/

Who is really fueling your AI? 🤔 It's not just code & algorithms. Behind every large language model are millions of people, often in invisible roles. Join us and the Data Workers’ Inquiry for a talk and panel to hear directly from data workers! September 17th, with @milamiceli & @superrr

🔗 Register here: https://www.weizenbaum-institut.de/en/news/detail/who-is-really-fueling-your-ai/

#AI #DataWork

New publication: “Ethics of Data Work”

How can fairer working conditions for data workers be created? A new Discussion Paper outlines guidelines for the use of data work in academic research:

https://www.weizenbaum-institut.de/en/news/detail/ethics-of-data-work/

Authors: T Yang, @strippel, A Keiner, @dylan, A Chávez, K Kauffman, M Pohl, C Sinders, @milamiceli

#DataWork #FairWork #ResearchEthics #DigitalLabor #ResponsibleResearch #AIethics #LaborRights #research #openaccess @towardsfairwork

"The production of artificial intelligence (AI) requires human labour, with tasks ranging from well-paid engineering work to often-outsourced data work. This commentary explores the economic and policy implications of improving working conditions for AI data workers, specifically focusing on the impact of clearer task instructions and increased pay for data annotators. It contrasts rule-based and standard-based approaches to task instructions, revealing evidence-based practices for increasing accuracy in annotation and lowering task difficulty for annotators. AI developers have an economic incentive to invest in these areas as better annotation can lead to higher quality AI systems. The findings have broader implications for AI policy beyond the fairness of labour standards in the AI economy. Testing the design of annotation instructions is crucial for the development of annotation standards as a prerequisite for scientific review and effective human oversight of AI systems in protection of ethical values and fundamental rights."

https://journals.sagepub.com/doi/10.1177/20539517251351320

#AI #GenerativeAI #DataWork #DataLabour #AIPolicy #PoliticalEconomy #DataLabeling #AIEthics #DataAnnotation

Tech life is rarely dull. Partly because so many "great advice" articles I see don't accord with my experiences.

For example, in one of my feeds is a piece: "SQL Query Optimization for Data Engineers" of which half the things in it would be bad advice in my work.

Data engines and query optimisers vary so much that many "expert" assumptions prove false on them.

There's really no substitute for:
- understanding how your platform really works;
- trying out multiple ways.
#datawork #SQL