AI in hiring, lending and housing decisions
See how AI is used in decisions that shape people's lives, and what fair, contestable use requires
A taste of a lesson
Our HR team wants a tool that ranks CVs. What is the first thing we should check?
Start with what it was trained to predict. Many ranking tools learn from past hiring decisions, which means they learn past preferences, including biased ones. Ask the vendor: what outcome does the score predict, on what data was it validated, and what are the selection rates by group in testing? Then plan your own adverse impact checks after rollout, and make sure a recruiter can override rankings easily. Involve your legal team early. Which outcome would you want the tool to predict: past hires, job performance, or something else?
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
- Describe how AI is used in hiring, lending, housing and insurance decisions
- Identify the main sources of error and unfairness in each setting
- Design meaningful human review, explanations and appeal routes
- Ask vendors and decision makers the right questions about automated tools
- Explain broad legal concepts on discrimination and automated decisions
Lesson plan
- 1 Where automated decisions appear Map the uses of AI in hiring, lending, housing and insurance. Start
- 2 What goes wrong Identify typical failure modes in high stakes people decisions. Start
- 3 Human review that is real Design human oversight that can actually catch and correct errors. Start
- 4 Explanations and appeals Give affected people understandable reasons and a route to challenge. Start
- 5 Law, audits and vendor questions Understand broad legal concepts and how to question tools before and after adoption. Start
Try asking
About this tutor
For HR staff, lenders, landlords, advocates and affected individuals who want to understand AI in high stakes decisions about people. You look at where automated tools appear: CV screening, video interview analysis, credit scoring, tenant screening and insurance pricing. For each, you learn what the system typically predicts, what data it uses, where errors and unfairness creep in, and what good practice looks like: human review that is real, explanations people can understand, a way to challenge decisions, and ongoing monitoring. You also learn broad legal concepts such as anti discrimination duties and rights around automated decisions, without legal advice, and how to ask vendors hard questions.
Reviews
4.7
3 ratingsSample
- Shanice R.Sample
I was refused a flat because of a screening report error. This helped me understand what happened and how to ask for a correction.
- Dmitri A.Sample
Balanced between employer and applicant views. I wanted more on insurance pricing, but hiring and lending were covered well.
- Paula C.Sample
The vendor question list is now part of our procurement process. The point about tracking overturn rates was simple and powerful.
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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