recursal/Fanatic-Fandom huggingface dataset:
Episode 5: The TRUE U Festival

**The TRUE U Festival** is the fifth session of Chad Memorial.

The party fights a spider house, goes to a festival, gets high, and solves racism.

## Synopsis

The Party once again finds themselves in a tavern (a tavern? in DND? unheard of) discussing their next moves now that they have returned to Irth. They happen to stumble upon a bulletin board where a few folks around town have posted their advertisements. The first one the party responded to was a man asking for help with his "house spider." The party arrives at his house only to realize that the house was a spider, and has taken hold of some unsuspecting sea elf who was leaning nonchalantly on the building. The party is able to save the woman and defeat the spider, which falls over and becomes a cool sideways house. The sea elf woman's name is Eleanor, who wrote one of the other adverts looking for a party to help her get to Chatona. Since the boat is not leaving for another week, the party decides to go to the TRUE U festival, which was advertised in the other pamphlet on the bulletin board. The pamphlet specifically mentioned beings from Hell, which the party found odd, especially since Virion fit the description exactly. At the festival, many booths were set up. Wesley was nearly hustled by a Tabaxi (named Tranquility), but the party was able to stop him and get Wesley's money back. Eleanor fought a fire genasi (Flare Cinders) to win a cool sword, but lost. Nova flirted with Randy the blacksmith. Wesley set up an anti-racism seminar and was able to solve racism. Some people got high, thanks to Jenny Plumeria. Everyone got their fortune read, and The Fortune Teller told Virion not to trust the booth on the right. The party went to the booth on the right to talk to Penumbra.

## Featured Characters

### The Party

* Nova von Starlight
* Wesley Waters
* Kat
* Rosalynn
* Morgana

### New

* Jenny Plumeria
* The Fortune Teller
* Penumbra
* Tranquility
* Eleanor

### Returning

* Esper Esso
* Meralda
* Helena
* Cass Erole
* Virion
* Dino
* Randy
* Aleksi Roscoe
* Cynthia

### Mentioned

* Seahawk

Template:Navbox Episodes
Ballys

Wondrous Item, uncommon

Like Heelys, but with a ball in the sole instead of a wheel.

Characters wearing Ballys may use a bonus action to increase their base movement speed by 5 feet until the start of their next turn. If a character moves through difficult terrain while wearing Ballys, they must make a DC 15 Dexterity saving throw or fall prone. Characters who have their speed reduced to 0 feet cannot benefit from Ballys.
Wesley Waters

**Wesley Waters** is a PC halfling bard.

He made his first appearance in Episode 1: Hell-o Part 1

## Background

Wesley was born in Triboar, Pokepilka. He died and went to Hell when he played a triangle at his resonant frequency.

## Personality

Wesley is a friend. He tries to make friends with everyone. He is not very intelligent so he is easily duped.
Chad

**Chad** (also known as **Chaddington Ladumbo**) is an NPC changeling fighter, originally from Hell.

He made his first appearance in Episode 1: Hell-o Part 1

## Background

Chad was originally created as part of The Curse, a plot by the Gods to make hell terrible again. He frequently disguised as a young human boy claiming to be still alive and accidentally in Hell. He would then claim that he had discovered a map to the Hellevator, and needed an adventuring party to help him escape. He did this in order to lure unsuspecting creatures into the Changeling Lair to be tortured.

He was frequently on the run from the Hell Police, tasked with counteracting The Curse and maintaining the order in Hell.

## Story

Chad was about his usual antics when he first met the party at the Hell Tavern - Detroit Location, and convinced them to join him on an expedition to the Hellevator after convincing them that he lost his band, Chad and the Backstabbers. He successfully fooled the party until they encountered Officer Janine, who informed the party of Chad's arrest warrant. Regardless, Chad was able to convince the party to follow him into the Changeling Lair, where he and many other changelings ambushed the party by disguising as them. The attack failed, and only Chad was left alive. The party debated on whether or not to spare him, but ultimately decided to kill him after he said the music they played was terrible. Wesley delivered the fatal blow and Morgana cut off his hands.

Wesley believes this was not the real Chad and the real Chad is still alive somewhere.

Chad made an appearance of the MC of Wesley, Morgana, and Nova Von Starlight's dream in Episode 2.5: Throcktholomew.

## Personality

In his disguised form, he acts timid and shy, but in actuality, Chad is bold and enjoys leading others to their downfall. He has a French accent.

## Appearance

In undisguised form, Chad is a green lizard-like creature with long pointy ears and sharp teeth. In disguised form, he has blonde hair and green eyes, and looks like a human teenage boy. He stands at 162cm.
Models comparing

## Practice full name

Comparing a model that uses sensitive attributes with a model that does not make use of them

## About

It can be useful to compare some models to check with one is better. With "better" we certainly mean which of the two makes predictions with a lower error rate, but we also mean which of the two does not discriminate. It might therefore be interesting to compare the predictions of two models, one of which executes predictions also based on sensitive attributes. The comparison is made by checking and comparing different metrics inherent to the predictions.

## What the crowd is saying

Is this practice best or bad?

While there is a slight tendency towards positive responses, there is not a noticeable difference to consider this practice as "best".

## Involved topics

### What is a machine learning model?

A machine learning model is a program that has been trained to recognize certain types of patterns. The model is trained over a set of data, providing it an algorithm that it can use to reason over and learn from those data. Once the model is trained, it can be used to reason over data that it hasn't seen before, and make predictions about those data [1].

### What is a sensitive feature?

Sensitive features (or protected attributes) are attributes for which the machine learning algorithm could discriminate subjects. Some common examples of this attributes are age, disability, national origin, race/color, religion, sex. Some cases where discrimination has occurred are facial recognition and hiring. The theory holds that oversampling and undersampling techniques can cause discrimination [2].

## Sources

1. What is a machine learning model?
2. Handling Discriminatory Biases in Data for Machine Learning

**Other useful links**

* How to Compare Machine Learning Models and Algorithms

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
Finn Heneghan

**Finn Heneghan** (known mononymously as **Finn**) is an NPC and a bottom, and the co-shop owner of The Platsberg Bodega and Donut Shop.

## Background

Finn moved to Platsberg as a young child, before it was raided by Rogue Barbarians. He and Rowan McColgan were part of the few who stuck around in the ghost town instead of fleeing.
Szarlotka

Syraakyoos and Szarlotka

### Port of Szarlotka

It's a port! Direct trade routes to the islands of Chatona. Ships coming in from South Empire however, will be subject to heavy inspection. Significantly smaller than the Port of Syraakyoos, so some find it more convenient to go through the border crossing at the river and set sail out of Syraakyoos.

## Traditions

### The Apple Bowl

The Apple Bowl is an annual tradition in which Szarlotka residents line up on the side of the river to throw oranges at the residents of Syraakyoos.

## Current Goings-on

Mayor Apple Jack is concerned that, due to the low bearing of apples this year, that the wasting of apples at the Apple Bowl will cause some in the town to starve. However, she is concerned that cancelling the Apple Bowl will cause public outcry and panic.
Zoog

**Zoog** is an NPC changeling fighter, originally from Hell.

He made his first appearance in Episode 1: Hell-o Part 1

## Background

Zoog was explorer. Zoog travelled far, met many people. People like Zoog until Zoog got mad and things went boom. Then zoog kept exploring, cause people no like zoog anymore. Zoog was exploring mountain when it got very hot and made big boom. Zoog dried up and shriveled like fruit left in sun. Now Zoog is dead. But he live on in the afterlife. Still need moist skin to live. Keeps spray bottle for moisture and bad animals.

## Story

Zoog escaped hell along with The Party after successfully defeating Chad and going up the Hellevator. In Webster, he travelled with the party but did not participate in the Lifeblood reunion concert. The concert was attacked by a bugbear, which the party killed, and Zoog felt pity for the children of the bugbear, and decided to leave the party to raise them as his own. He left the party in Episode 4: Pro-Establishment Bear and now resides in the wilderness of Webster with his bugbear children.

## Personality

Zoog is generally quiet, unless he gets mad, then things go boom.
Great Reset

The **Great Reset** Event was a event for Ryan Trahan's Feed America fundraiser and his cross America to give Mr Beast a penny in just 30 days challenge.

### Steps

* First, a player had to go up to Ryan in Sea Cleaning Simulator and talk to him.

* After talking to Ryan, he would give the player a penny to find Mr Beast in the map.

* Mr Beast is located in Octoland which is the 4th zone in the game.

* The player had to complete a obby to get to Mr Beast.

* If the player completed the obby, they needed to talk to Mr Beast.

* When they agree with the GREAT RESET, they will be given the limited time egg once and the GREAT RESET will happen.

* The player will lose their coins, areas, boats, upgrades but keep their pets. The player could repeatedly do this event until it is removed.
Validation on discriminated individuals only

## Practice full name

Perform validation by creating a partition that contains discriminated individuals only

## About

The model must always be validated. This practice requires that validation take place on subjects who may be subjected to discrimination: if discrimination is carried out in the validation phase, then the model will not be valid. The validation must be carried out only and exclusively on sensitive subjects.

## Participants opinion

Is this practice best or bad?

In the opinion of the participants, this practice has a tendency for which it could be considered as bad.

Looking at more details, there is much more confidence in the answers that classify the practice as bad: many participants said they were totally or very confident that this practice is bad, in several responses.

## Involved topics

### What is validation?

Validation is a procedure where a trained model is assessed with a testing data set. Validation is done after model training and it intends to locate an ideal model with the best execution. Some validation techniques are: K-fold Cross-Validation Method, Random Subsampling Validation Method and Leave-One-Out Cross-Validation Method.

K-fold Cross-Validation is used to detect the overfitting or fluctuations in the training data that is selected and learned as concepts by the model. It repeats cross validation process many times with different splits of the sample data in to K-parts.

With Random Subsampling Validation data is randomly partitioned into dis-joint training and test sets multiple times means multiple sets of data are randomly chosen from the dataset and combined to form a test dataset while remaining data forms the training dataset.

With Leave-One-Out Cross-Validation all the data except one record is used for training and that one record is used later only for testing [1].

## Sources

1. What is Model Validation.

**Other useful links**

* Automated directed fairness testing

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
Irth

## Chatona

their land consists of 4 islands, with their main island containing the Forest of Shitby and their capital, Ville de Chatona. The main population of the nation consists of druids, those of animal-like races, and seafarers, who often take up work transporting people from island to island. There is a druid college in Langia.

## Pokepilka

Those in the north of pokepilka, especially in and near the mountain city of Sproszkowany, are the richest and most elite nobles on all of Irth. The nation consists mainly of classes with a higher connection to some god, such as Aasimars, Clerics, Monks, and Paladins, though all classes have some representation in the large country. There is a significant class divide among the wealthy in the north and the working class such as the apple farmers in Szarlotka and fishermen in Lody. Podwodny is an underwater city filled with races that can breathe underwater. They're legally part of Pokepilka but generally autonomous.

## South Empire

The much more secular South Empire is a diverse nation consisting of a good mix of many races and classes. Most tieflings and halfings on Irth live in this country. This nation has frequent tensions with Pokepilka due to clashing ideals. Notable locations in the South Empire include the Rochester Institute of Wizardry in Rochester and a bard college in South Empire City. South Empire City is the largest city on Irth but also has an abundance of crime in some areas. Syracuse has an orange farm.
Nova von Starlight

**Nova von Starlight** is a PC Elf Bard.

They made their first appearance in Episode 1: Hell-o Part 1

## Background

Give it up for Nova von Starlight, the most striking rocker in their realm! They have a glam rocker flair and a falsetto that could purify water (figuratively). Once nothing more than a collection of bards from an elven forest city, Moonheathen has enraptured the hearts of many a creature, with their aggressive touring schedule and their soul-shattering ballads. Moonheathen features Nova von Starlight on vocals, Elysian Abyss on bass, Fate Fairchild on theremin, and Rainflower Wrath on lead guitar.

While studious and attentive growing up, Virion Leora, the soul who would become Nova von Starlight, disagreed with the way their elves taught. The system of education was surprisingly rigid and there was little room for avant-garde expressions of the self. Virion and their friends fundamentally disagreed, and set out to prove that there was a space for new forms of philosophy--song form, that is. And heavy use of magical practical effects in stage shows. One of which--a stray lightning bolt cast by Rainflower--fatally struck Nova dead in their final number for the night.

## Personality

Nova has a big personality, thinking very highly of themselves. They can be a little selfish but nonetheless they are a good friend.

## Appearance

Grey elf with long white hair and non-functional wings.
Maintenance considering data changes over time

## Practice full name

Perform maintenance and/or model evolution considering data changes over time

## About

In an already operating system, therefore deployed, nothing prevents the data from changing over time. Therefore, it would be advisable to carry out maintenance and evolution activities in order to adapt the system to any changes regarding the data and their meaning. For example, it may be necessary to re-run the training activity in order to allow the model to learn reasoning about new data if its has definitely changed over time.

## What the crowd is saying

Is this practice best or bad?

In the opinion of the participants, this practice has a great tendency for which it can be considered as best.

Looking at more details, there is much more confidence in the answers that classify the practice as best.

## Involved topics

### Software maintenance

Even after deployment, software systems may need to undergo changes, for example to fix problems or improve the system. This activity is called software maintenance. It's a crucial activity, harder than develop a system and harder than design a maintainable system [1].

### Software evolution

Software evolution is all programming activity that is intended to generate a new software version from an earlier operational version. Evolution may arise during software development (where the design evolves), during software maintenance (after deployment, in the continuing process of adapting the system to the changing needs of its users) and when the system's continuous evolution made it too complex to maintain: the system may requiring a more extensive restructuring and redesign, or even a full reimplementation or replacement [1].

### What is a machine learning model?

A machine learning model is a program that has been trained to recognize certain types of patterns. The model is trained over a set of data, providing it an algorithm that it can use to reason over and learn from those data. Once the model is trained, it can be used to reason over data that it hasn't seen before, and make predictions about those data [2].

### What is model training?

Model training is a process in which a machine learning algorithm is fed with sufficient training data to learn from. To do this, it's necessary a training dataset, which consists of the sample output data and the corresponding sets of input data that have an influence on the output. The training model is used to run the input data through the algorithm to correlate the processed output against the sample output [3].

## Sources

1. Software Maintenance and Evolution
2. What is a machine learning model
3. What is model training

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
Weight assignment with random values

## About

The assignment of weights allows to adjust the importance of the features in the prediction phase: the assignment of random values that will change from one learning session to another one will allow you to experiment with different predictions that will take into account weight assignments that would hardly have been try it manually. Therefore, for each new learning session, new different weights are generated. For example, in a learning session the patient's patience is indicated as the most relevant attribute, while in another session the most relevant attribute could become the color of the skin.

## What the crowd is saying

Is this practice best or bad?

In the opinion of the participants, this practice has a great tendency for which it can be surely considered as bad.

Looking at more details, many participants think it is definitely, or almost, a bad practice. Only very few participants think the opposite, so there are sufficient reasons to consider this practice certainly a bad one.

## Involved topics

### What is a weight?

Weight is the parameter within a neural network that transforms input data within the network's hidden layers. As an input enters the node, it gets multiplied by a weight value and the resulting output is either observed, or passed to the next layer in the neural network. Often the weights of a neural network are contained within the hidden layers of the network [1].

### Weight initialization

Weight initialization is an important design choice when developing deep learning neural network models. Historically, weight initialization involved using small random numbers, although over the last decade, more specific heuristics have been developed that use information, such as the type of activation function that is being used and the number of inputs to the node. These more tailored heuristics can result in more effective training of neural network models using the stochastic gradient descent optimization algorithm [2].

## Sources

1. Weight (Artificial Neural Network)
2. Weight Initialization for Deep Learning Neural Networks

**Other useful links**

* Weight Initialization Techniques in Neural Networks
* Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
* Random Initialization For Neural Networks : A Thing Of The Past

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
Hyperparameters tuning

## About

Hyperparameters can be seen as configuration variables, for example in a neural network it regulates the number of hidden layers present. Setting up hyperparameters allows you to regulate the learning process, then the way the model learns on data. The values of the hyperparameters are adjusted by observing how the model learns. It is important to tune these values, as the predictions themselves depend on how the model learns.

## What the crowd is saying

Is this practice best or bad?

In the opinion of the participants, this practice has a tendency for which it could be considered as best.

Looking at more details, there is much more confidence in the answers that classify the practice as best.

## Involved topics

### What is an hyperparameter

Hyperparameters are parameters whose values control the learning process and determine the values of model parameters that a learning algorithm ends up learning. Hyperparameters are used by the learning algorithm when it is learning but they are not part of the resulting model [1].

### Hyperparameter tuning

Hyperparameter tuning is choosing a set of optimal hyperparameters for a learning algorithm. The optimal hyperparameters values help to avoid under-fitting (training and test error are both high) and over-fitting (Training error is low but test error is high) [2].

### What is model training?

Model training is a process in which a machine learning algorithm is fed with sufficient training data to learn from. To do this, it's necessary a training dataset, which consists of the sample output data and the corresponding sets of input data that have an influence on the output. The training model is used to run the input data through the algorithm to correlate the processed output against the sample output [3].

## Sources

1. Parameters and Hyperparameters in Machine Learning and Deep Learning
2. Evaluating Machine Learning Models using Hyperparameter Tuning
3. What Is Model Training?

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
Data balancing on sensitive features

## Practice full name

Make data balancing activities based on sensitive features

## About

Having an unbalanced dataset is not such a remote possibility. Given that, we want to reduce the probability of executing discriminations, so we could think to perform data balancing activities only on sensitive attributes, increasing the reliability of predictions on these attributes.

## What the crowd is saying

Is this practice best or bad?

It is not possible to establish it from the replies received.

## Involved topics

### What is a balanced dataset?

A balanced dataset is a dataset where each output class is represented by the same number of input samples. Balancing can be performed using a specific technique: the most popular are oversampling and undersampling [1].

Oversampling is a technique wherein the number of training samples of the minority class is increased so as to balance the number of samples of both majority and minority class. Undersampling refers to removing training samples from the data which belong to that class of the target feature that has the majority over the other class [2].

### What is a sensitive feature?

Sensitive features (or protected attributes) are attributes for which the machine learning algorithm could discriminate subjects. Some common examples of this attributes are age, disability, national origin, race/color, religion, sex. Some cases where discrimination has occurred are facial recognition and hiring. The theory holds that oversampling and undersampling techniques can cause discrimination [3].

## Sources

1. How to balance a dataset in Python
2. Imbalanced Data and How to Balance It
3. Handling Discriminatory Biases in Data for Machine Learning

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
List of heroes

* Roles
* Heroes List

## Roles

List of Extraordinary Ones heroes, by role. The original Chinese names are shown inside parentheses. There are, currently, 93 released heroes (Updated: August 11, 2022), with Viatin being the latest, released on June 1, 2022. This list DO NOT INCLUDE anime collaboration heroes from the original Chinese server.
Best practices

A best practice is a standard or set of guidelines that is known to produce good outcomes if followed [source].

The following practices are classified as "best". The classification is based on a set of responses obtained by approximately 120 people working with fair critical machine learning systems.

* Fairness requirements emerged with interviews
* Use of composite models
* Hyperparameters tuning
* Fair hypothesis respect through ML algorithm
* Validation and testing on a balanced dataset
* Model training on a balanced dataset
* Fairness as a priority aspect during analysis phase
* Weight assignment for underfitting and overfitting
* Weight assignment to make model more precise
* Maintenance considering data changes over time
Eleanor

## **Eleanor McCree**

**Eleanor McCree** is a PC sea elf pirate.

She made her first appearance in Episode 5: The TRUE U Festival

## Background

Eleanor is both a pirate and a shipwright. She designed and built ships since she was young, always enticed by shipwrecks she saw, and decided one day to go exploring. She built a boat and put together a crew and got someone who had Pirate Experience to be captain. Eleanor's motley crew of pirates (Goldwin Addington, Sylvia Nall, Dale Wither, Wendy Grant, and Maybelle Nebula) would travel to caves and islands and other ships and rob them. her boat was super well crafted and weathered a lot of storms. her goal was to one day craft a ship so excellent that it could weather any storm, no matter how bad. But then, she and her entire crew died in a shipwreck and they all went to Hell.

In hell, Eleanor, along with several others including Seahawk and Flare Cinders was lured in by Chad to the Changeling Lair. Eleanor's companian Flare was able to wipe out several changelings with a fireball, but Chad escaped unscathed.
Thomas

* Fever Code
* Scorched
* Maze Runner
* Scorch Trials
* Death Cure Prelude
* Death Cure
* After Pt. I
* After Pt. II

"*I just... feel like I need to save everyone. To redeem myself.*"
— Thomas to Joan, *The Maze Runner*
**Thomas**, originally named **Stephen**, is the last male Glader and one of the creators of the Maze along with Teresa Agnes. He is the protagonist of the series and presumably named after Thomas Edison.
Use of composite models

## Practice full name

Use composite models (combination of other models, ensemble learning)

## About

Using composite models allows you to combine predictions from multiple models. Then there would be "opinions" from several predictors, combining them together in order to have a more precise and reasoned prediction. Adopting combinations of models that ensure compliance with software fairness could be a nice technique for avoiding discriminations in system predictions.

## What the crowd is saying

Is this practice best or bad?

In the opinion of the participants, this practice has a great tendency for which it can be considered as best.

Looking at more details, there is much more confidence in the answers that classify the practice as best.

## Involved topics

### What is a machine learning model?

A machine learning model is a program that has been trained to recognize certain types of patterns. The model is trained over a set of data, providing it an algorithm that it can use to reason over and learn from those data. Once the model is trained, it can be used to reason over data that it hasn't seen before, and make predictions about those data [1].

### Composite models / Ensemble learning

Composite models refers to the combination of different machine learning models to achieve the best result [2]. This technique is also called "Ensemble learning". In ensemble learning, an agent takes a number of learners and combines their predictions to make a prediction for the ensemble. The algorithms being combined are called base-level algorithms. Random forests are an example of an ensemble method, where the base-level algorithm is decision tree, and the predictions from the individual trees are averaged or are used to vote for a prediction [3].

## Sources

1. What is a machine learning model
2. Composite AI: What Is It, and Why You Need It
3. Ensemble Learning

==How to contribute==\*You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
Episode 6: Shedding Armor

**Shedding Armor** is the sixth session of Chad Memorial.

Virion gets a makeover. Nova fucks. Some poachers come looking for Virion.

## Synopsis

The Party went to the booth on the right, initially suspicious of the elf-appearing creature offering them makeovers. To alleviate their fear, the "elf" (named Penumbra) lets down their disguise to reveal they are also a changeling as well. They are happy to see Virion, as they initally believed they were the only changeling to escape Hell. Penumbra gave Virion advice as how to fit in better as a changeling on Irth, and also gave makeovers to some of the rest of the party. The festival ended and the party returned to the tavern for drinks. Nova and Randy, meanwhile, went up to the bedroom. After a good night's rest, the party began travelling towards Eleanor's ship in Syraakyoos. After a day of walking, the party set up camp for an evening. Eleanor, Wesley, and Kat each had a shift that went by nearly uneventfully, but then when Virion took watch a fog rolled over the campsite and two poachers came out of the fog to attack them. The poachers wanted to kidnap Virion because changelings could go for huge prices on the black market due to their rarity. The party was able to come to Virion's rescue, killing one poacher and tying up and stripping the other, likely leaving him to die. Morgana sleepwalked and cut off the poachers' hands, taking one hand and 4 fingers from each. The party carried on their merry way.

## Featured Characters

### The Party

* Nova von Starlight
* Wesley Waters
* Kat
* Rosalynn
* Morgana
* Eleanor

### Returning

* Virion
* Dino
* Randy
* Aleksi Roscoe
* Cynthia
* The Fortune Teller
* Penumbra

Template:Navbox Episodes
Unclassified practices

Unclassified practices are all the practices for which it is not possible to affirm they are "best" or "bad".

The classification is based on a set of responses obtained by approximately 120 people working with fair critical machine learning systems. The following practices are classified as "unclassified" as due to the answers it is not possible to categorize them with enough certainty.

* Models comparing
* Data balancing on sensitive features
Remove sensitive features

## About

One way to work around the discrimination could be to remove sensitive features from the dataset. Doing this would therefore prohibit the model from reasoning about such attributes. Removing sensitive attributes from the dataset means making predictions without taking those characteristics into account.

## What the crowd is saying

Is this practice best or bad?

In the opinion of the participants, this practice has a great tendency for which it can be considered as bad.

Looking at more details, there is much more confidence in the answers that classify the practice as bad.

## Involved topics

### What is a sensitive feature?

Sensitive features (or protected attributes) are attributes for which the machine learning algorithm could discriminate subjects. Some common examples of this attributes are age, disability, national origin, race/color, religion, sex. Some cases where discrimination has occurred are facial recognition and hiring. The theory holds that oversampling and undersampling techniques can cause discrimination [1].

## Sources

1. Handling Discriminatory Biases in Data for Machine Learning

**Other useful links**

* Towards Fair Classifiers Without Sensitive Attributes

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
Fairness Guru Wiki

# **Welcome to the Fairness Guru Wiki!**

Fairness Guru stems from a thesis and makes it easy for you to find out everything you need to know about fairness in machine learning systems.

This wiki was born from a thesis concerning fairness in artificial intelligence systems and it aims to provide support to anyone who wants to approach the problem of fairness by providing best and bad practices collected through a survey. The wiki was built based on the opinions of domain experts who expressed their thoughts about the practices that the wiki lists.

### The importance of software fairness

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans, and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop ML algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision making may be inherently prone to unfairness, even when there is no intention for it. An example was the intelligent hiring software of Amazon, which discriminated women for tech positions (source). Such episodes should not repeat themselves, so it is good that this topic is going to be explored.

### Let's start

* What is Fairness
* Our Fairness study
* Check out the wiki catalog

### Credits

Realized by Francesco Abate.

Thank to the SeSa Lab of the University of Salerno and to the survey participants for the reviews given to the wiki.
Our Fairness study

## Objective

The objective of this empirical study is to understand whether data scientists, software engineers and other figures are aware of the problem of software fairness and, if so, how they treat it from a personal and professional point of view. Therefore, opinions will be collected from various working figures whose it is expected to be able to understand, in general, how important fairness is when designing a system that makes use of machine learning techniques, which techniques are used to ensure fairness compliance and how respondents approach the problem.

### Research question

In particular, the study tries to answer the following research question: what are the best and bad practices that influence the levels of software fairness in the development of a fair critical solution?

The question is going to be answered by analyzing the data obtained through a particular survey, focusing on the questions relating to the root causes and practices, also observing whether the vision of these aspects may change based on the job role assumed by the interviewee.

## Survey

The survey has been filled by about 120 people who claimed to be experts in the topics of software engineering, data science and software fairness.

This wiki provides visualizations of the data collected through the answers given by the experts, so that anyone who reads can draw their own conclusions.
Characters

The following is a complete pictorial list of the named characters in AMC's *The Walking Dead*. They are grouped by families or groups of survivors and roughly in order of first appearances.

Key

* A character with an "Unknown" status was alive when last seen but was either injured, lost, unsecured, absent in later episodes, or written out of the show.
Episode 4: Pro-Establishment Bear

**Pro-Establishment Bear** is the fourth session of Chad Memorial.

The cast holds a concert with Lifeblood and meets a DILF. Zoog leaves the party.

## Synopsis

The Party dealt with the aftermath of getting dumped into a random tavern. As it turns out, Meralda and the party they saved happened to be a locally famous metal band named Lifeblood. As such, the group was immediately stormed by fans. To celebrate their return, the band decided to hold a concert, with the help of the party. However, more pressingly, one of the members of the band, who happened to be the lead singer, was still frozen. A bartender recommended taking Helena to the blacksmith, Randy, so that she could be thawed out, and cooked "very, very, very, very rare." The party left her with the blacksmith and began to advertise the concert around town. They managed to get most of the town to show up. The band and party played, and the concert was a hit! But then, all of a sudden, a bugbear emerged from the woods. This was the bugbear that killed the band and sent them to hell in the first place. She was upset because the music was too loud and had a message that she didn't want her kids in the woods to hear. The party responded by murdering the bugbear, and Morgana cut off her hands. Kat attempted to throw a green potion at the bugbear but it had no effect. After the session, Zoog decided that the bugbear's kids need good parental authority in their life, since their mom was murdered and all. Zoog said "Zoog dad now" and vanished into the woods with his new bugbear children.

## Featured Characters

### The Party

* Zoog
* Nova von Starlight
* Wesley Waters
* Kat
* Rosalynn
* Morgana

### New

* Randy
* Aleksi Roscoe
* Cynthia

### Returning

* Esper Esso
* Meralda
* Helena
* Cass Erole
* Virion
* Dino

Template:Navbox Episodes
Fair hypothesis respect through ML algorithm

## Practice full name

Select a specific ML algorithm is order to respect specific Fair-Critical Hypothesis

## About

The choice of the algorithm depends on various assumptions concerning the type of problem to be solved and some technicalities concerning different characteristics of the dataset. These are a series of assumptions that the choice of the algorithm must be able to comply with: this practice requires the designer of the machine learning model to make assumptions concerning fair aspects, so that the choice of the algorithm is undertaken considering the fair aspects involved.

## What the crowd is saying

Is this practice best or bad?

In the opinion of the participants, this practice has a great tendency for which it can be considered as best.

Looking at more details, there is much more confidence in the answers that classify the practice as best.

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
What is Fairness

## About Fairness

Recent studies have shown a new type of defect that refers to discrimination carried out by intelligent systems.

Fairness topic has started to attract the attention of researchers in AI, Software Engineering and Law communities, with more than twenty different notions of fairness proposed in the last few years. Yet, there is no clear agreement on which definition to apply in each situation.

There have been cases where intelligent systems discriminated people mainly on race and sex. Some episodes of discrimination are the following:

* An algorithm used by more than 200 million people in U.S. hospitals to predict which patients would need more medical care came to prefer white patients over black ones because of treatment costs, which are significantly lower than costs incurred by white peoples. For this reason, the algorithm considered black patients to be healthier than white patients (source);
* The US police used an algorithm to analyze data and predict crimes that could happen in the future. The problem is that this algorithm had a tendency to judge black people as predisposed to commit crimes, therefore it detected greater risk in committing violent crimes (source);
* In 2015, Amazon realized that the intelligent software they used to filter the best job candidates was discriminating them on their gender. This system was trained on a large number of resumes obtained in 10 years: most of them came from men, so the system came to think that male candidates were more suitable for positions inherent to technology. This issue led Amazon to close the project, according to the information they provided (source).

IT literature does not propose a single definition of software fairness: several have been proposed, which vary according to the problem being discussed (source).

* Group fairness, also called statistical parity, when subjects in the protected and unprotected groups are equally likely to be involved in a positive prediction. The idea behind this definition is that each subject must have an equal chance of obtaining a positive prediction;
* Predictive parity, also called outcome test, when the subjects of the protected and unprotected groups have the same probability of being involved in a true positive prediction, therefore given a positive prediction it must be found in reality with a particular probability equal for each subject. The idea behind this definition lies in the fact that we want to ensure a certain reliability in the predictions, fair for each subject;
* Test-fairness, when the subjects of the protected and unprotected groups have the same probability of being involved in a true positive or a true negative prediction, so regardless of the result of the prediction, each subject must have the same probability as to whether the prediction should be truthful;
* Fairness through unawareness, when sensitive features are not involved in the prediction.

## Useful links

* Software Fairness
* Fairness Definitions Explained
* "Ignorance and Prejudice" in Software Fairness
* Fairness testing: testing software for discrimination
Model training on sensitive features

## Practice full name

Base model training mostly on sensitive features

## About

Training is mainly based on sensitive features, so the model will learn to think according to those features. Any prediction that the model will make on the dataset will mainly take into account the sensitive features, so the judgment will depend on the values assumed by those variables. In this way, it would become more relevant, for example, the color of the skin and the sex of a person, overshadowing other attributes that characterize the subject.

## What the crowd is saying

Is this practice best or bad?

In the opinion of the participants, this practice has a tendency for which it could be considered as bad.

Looking at more details, there is much more confidence in the answers that classify the practice as bad.

## Involved topics

### What is a machine learning model?

A machine learning model is a program that has been trained to recognize certain types of patterns. The model is trained over a set of data, providing it an algorithm that it can use to reason over and learn from those data. Once the model is trained, it can be used to reason over data that it hasn't seen before, and make predictions about those data [1].

### What is model training?

Model training is a process in which a machine learning algorithm is fed with sufficient training data to learn from. To do this, it's necessary a training dataset, which consists of the sample output data and the corresponding sets of input data that have an influence on the output. The training model is used to run the input data through the algorithm to correlate the processed output against the sample output [2].

### What is a sensitive feature?

Sensitive features (or protected attributes) are attributes for which the machine learning algorithm could discriminate subjects. Some common examples of this attributes are age, disability, national origin, race/color, religion, sex. Some cases where discrimination has occurred are facial recognition and hiring. The theory holds that oversampling and undersampling techniques can cause discrimination [3].

## Sources

1. What is a machine learning model?
2. What Is Model Training?
3. Handling Discriminatory Biases in Data for Machine Learning

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.
Sensitive attributes

A root cause is the fundamental reason for the occurrence of a problem [source]

Here follows the analysis of 12 sensitive attributes to understand if they can cause discrimination in a machine learning system. The classification is based on a set of responses obtained by approximately 120 people working with fair critical machine learning systems.

## Crowd suggestions

Many participants suggested other sensitive attributes that could lead to discrimination. Here's the updated list:

## How to contribute

* You can use the "comments" section of the wiki. You can find it at the bottom of the article page that you want to edit. Suggest here your edits, the administrator will evaluate whether to accept them and report them on the page.

* You can send an email to *abatefrancesco98@gmail.com* setting as subject the page you want to edit.

Any interesting information will be reported on this page.