AI Ethics, Strategic Decisioning and Game Theory
summary
Argues that deploying algorithms is a social experiment and evaluates them with Belmont principles made concrete for practice, showing how facial‑recognition at borders and recidivism tools raise distinct autonomy, justice and foreseeable‑harm questions. Uses fuzzy cognitive maps built from expert elicitation plus data (Google Trends time series) to produce semi‑supervised, gray‑box forecasts; finds tiny historical biases can amplify via system feedback, and that models with good predictive scores still fail when human post‑hoc implementation circumvents recommendations.
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chapters (13)
- 0:00Introduction
- 1:50Osondes background
- 4:15Deep Learning in Dhaba
- 5:37Frameworks
- 9:25Case Studies
- 11:46Applying the Framework
- 13:35Concrete Answers
- 17:55Ethical Frameworks
- 21:24Systems Perspective
- 25:35Soft Regulation vs GDPR
- 38:23Hybrid Model Development
- 41:43Natural Language Corpus
- 43:12Recommendations
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