AI and workers: automation, monitoring and fair change
Think through how AI affects jobs, workplace monitoring and the hidden labour behind models
A taste of a lesson
My company says AI will make us more productive. Should I be worried?
It is reasonable to ask questions rather than simply worry or simply trust. Productivity gains can mean less tedious work, or higher targets with the same pay, or fewer roles over time; which one happens is largely a choice. Useful questions to ask: which tasks will change, how will performance be measured, will there be training, and how will gains be shared? A union or staff representative can help ask these collectively. Exercise: list three tasks in your job you would happily hand to AI, and one you would not.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain the task based view of how AI changes jobs and why effects are uncertain
- Describe algorithmic management and workplace monitoring and their risks
- Recognise the hidden human labour behind AI systems
- Propose practices for introducing AI at work fairly
Lesson plan
- 1 Jobs as bundles of tasks Understand why AI tends to change jobs rather than simply replace them. Start
- 2 Managed by algorithm Examine how software assigns, paces and rates work. Start
- 3 Monitoring and trust Weigh the costs and claimed benefits of AI powered workplace monitoring. Start
- 4 The labour behind the models Recognise the human work that AI systems depend on. Start
- 5 Fair change in practice Design a fair approach to introducing AI in a workplace. Start
Try asking
About this tutor
For workers, managers, union representatives, HR staff and students interested in the human side of AI at work. You examine how AI changes tasks rather than whole jobs, why effects differ across occupations, and what is genuinely uncertain. You look at algorithmic management and productivity monitoring, the often invisible work of data labelling and content moderation that models depend on, and the quality of jobs that remain. You also consider what fair change looks like: consultation, training, sharing gains, and protecting dignity. Arguments from workers, employers and economists are all given fair hearing.
Reviews
4.5
2 ratingsSample
- Mateo G.Sample
The hidden labour lesson stayed with me. I had never thought about who rates the answers that train chatbots.
- Siobhan M.Sample
Balanced. As a shop steward I found the fair change commitments useful for our talks with management. Wanted more on specific consultation rights.
About the teacher
I teach AI ethics as practical judgment: privacy, fairness and accountability you can act on
9 tutors 327 lessons taught Sample
I teach the ethics of AI as something you do, not something you recite. My lessons cover privacy and personal data, bias and fairness, explainability and the effects of AI on work and high stakes decisions. I use real cases and simple methods so learners can reason through a new situation on their own. My background combines data analysis with...
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