flowchart LR
id2-->id3
id4 --> id5
subgraph Collection
id1[Data collection]-->id2[Data cleaning and pre-processing]
end
subgraph Modelling
id3[Exploratory data analysis]--> id4[Modelling]
end
subgraph Postprocessing
id5[Postprocessing]
end
🗓️ Week 11
Ethical issues of AI and ethical AI: an overview
DS101 – Fundamentals of Data Science
04 Dec 2023
A few stories
For more on Delphi, see (Piper 2021) and (Noor 2021)
A few stories (2)
Cooking time
- These are actual recipes suggested by the recipe bot, Savey Meal-bot , of a New Zealand supermarket chain Pak ‘n’ Save
- Bot based on ChatGPT 3.5
- Included notice “You must use your own judgement before relying on or making any recipe produced by Savey Meal-bot”
- New warning notice now appended to meal planner that the recipes aren’t reviewed by a human being.
- For more on the topic, see (Loe 2023), (McClure 2023) and (Doyle 2023)
Common AI issues and risks
…Artificial Intelligence (AI) entails several potential risks, such as opaque decision-making, gender-based or other kinds of discrimination, intrusion in our private lives or being used for criminal purposes. European commission white paper, 2020
- Algorithmic bias or unfairness
- Lack of explainability
- Lack of security/ unsafe algorithms (e.g susceptibility to adversarial attacks (Goodfellow, Shlens, and Szegedy 2015) (Goodfellow, Ian et al. 2017) (Boesch 2023))
Common AI issues and risks
- Privacy issues
- Copyright concerns (see (Kafka 2023),(Naughton 2023a),(Professor Goetz Richter 2019), (Beaumont-Thomas 2023))
- Misinformation/deepfakes
The technical response: making algorithms transparent and explainable
- need to justify decisions made by “black box” algorithms (especially in sensitive contexts/applications)
- need to foster trust in decisions made by algorithms
- need to verify soundness of decisions and/or understand source of errors and biases
AI explainability refers to easy-to-understand information explaining why and how an AI system made its decisions.
Some of the post-hoc model-agnostic explanation methods:
- LIME (Local Interpretable Model-Agnostic Explanations) (Ribeiro, Singh, and Guestrin 2016)
- based on the idea of measuring the effect of local perturbations of feature values on the model
- see this page for more on LIME
- Shapley values:
- based on collaborative game theory
- measure of feature importance
- see this video or this post for a simple explanation of Shapley values
See this page for more on explainable AI (XAI). For an application to LIME and Shapley values in finance (credit default estimation), see (Gramegna and Giudici 2021)
The technical response: making algorithms transparent and explainable
Example from (Ribeiro, Singh, and Guestrin 2016):
- logistic regression classifier trained on training set of 20 images of wolves and huskies (10 of each category).
- The task is to distinguish images of both categories.
- Features extracted from images with a type of neural net
- Then the model is tested on a test set of 10 images (5 of each category): the model misclassifies one instance of husky for a wolf and a wolf for a husky.
- LIME explanations show that the misclassified husky was on a snowy background and that the misclassified wolf was not on snowy background and that the model was actually detecting background patterns (and not husky/wolf patterns as intended!).
