You are about to learn machine learning to make our lives better— your future systems and models may be going to be involved in the everyday decision-making, such as health care system or fraud detection. So, it is important that they work well in providing fair outcomes for everyone.
You are about to learn machine learning to make our lives better— your future systems and models may be going to be involved in the everyday decision-making, such as health care system or fraud detection. So, it is important that they work well in providing fair outcomes for everyone.
Imagine when the data you are using disproportionally represented, or lacks certain demographics, such as race, gender, political view, religion, etc. Or when the output is interpreted to favor some demographic. What is the consequence of the application?
In this lesson, you will:
- Raise your awareness of importance of fairness in ML
- Learn about fairness-related harms
- Learn about unfairness assessment and mitigation
Prerequisite
There is no prerequisite for this lesson, but it is a good idea to finish reading the [Introduction to Machine Learning](../[1-intro-to-ML/README.md)](https://github.com/microsoft/ML-For-Beginners/tree/main/Introduction/1-intro-to-ML) and \[History of Machine Learning\](../2-history-of-ML/README.md) first.
## Prerequisite
As a prerequisite, please take the "Responsible AI Principles" Learn Path and watch the video below on the topic:
Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
[](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
> Video: Microsoft's Approach to Responsible AI
Unfairness in data and algorithms
## Unfairness in data and algorithms
*"If you torture the data long enough, it will confess to anything." - Ronald Coase*
This sounds extreme but it is true that data can be manipulated to support any conclusion. And manipulation can happen unintentionally. As humans, we all have bias, and you just don’t consciously know when you introduce bias in data.
Fairness in AI and machine learning remains a complex sociotechnical challenge, meaning that it cannot be addressed from either purely social or technical perspectives.
Watch this video to learn about the fairness and socio-technical challenges:
\[](https://eus-streaming-video-rt-microsoft-com.akamaized.net/3c12a201-0657-4449-999c-f41b25df9616/31ce46d9-85b0-4e84-b93e-225478de_2250.mp4)
Fairness-related harms
## Fairness-related harms
What do you mean by unfairness? —it is negative impacts for group of people, such as those defined in terms of race, gender, age, or disability status.
Main fairness-related harms can be classified as:
@ -45,11 +52,13 @@ Let’s take a look at the examples.
Example of Allocation
One of the examples is a system for screening loan applications. The system tends to pick white men as good candidates than other groups. As a result, loans are withheld from certain applicants.
Another example is this experimental hiring tool that developed by a large corporation to screen candidates. The tool systemically discriminated against women by using the models were trained to prefer masculine languages. It resulted in penalizing candidates whose resumes contain words such as “women’s rugby team”.
Quality of service
Researchers found the three commercial gender classifiers had higher error rates that images of women with darker skin tones than the images of men with lighter skin tones.
Stereotyping
Stereotypical gender view was found in machine translation. When translating “he is a nurse and she is a doctor” into Turkish, a genderless language, which has one pronoun, “o” to convey a singular third person, then back into English yields the stereotypical and incorrect as “she is a nurse and he is a doctor”.
@ -57,17 +66,13 @@ Stereotypical gender view was found in machine translation. When translating “
An image labeling technology infamously mislabeled images of dark-skinned people as gorillas. Mislabeling is harmful not just because the system made a mistake because it specifically applied a label that has a long history of being purposefully used to denigrate demean black people.
An image labeling technology infamously mislabeled images of dark-skinned people as gorillas. Mislabeling is harmful not just because the system made a mistake because it specifically applied a label that has a long history of being purposefully used to denigrate demean Black people.
Over- or under- representation
## Over- or under- representation
Skewed image search results can be a good example of this harm. When searching images of professions with an equal or higher percentage of men than women, such as engineering, or CEO results heavily skewed toward images of men than reality.
@ -83,26 +88,28 @@ Each case varies severities, for instance, unfairly labeling someone as a crimin
| Photo labeling | | | | | |
Detecting unfairness
## Detecting unfairness
There are many reasons why the system behaves unfairly— the reasons include societal biases reflected of the datasets used to train them. For example, the hiring unfairness was caused by the historical data, by using the patterns in resumes submitted to the company over a 10-year period, and the problem was that the majority came from men, a reflection of male dominance across the tech industry.
There are many reasons why the system behaves unfairly— the reasons include societal biases reflected of the datasets used to train them. For example, the hiring unfairness was caused by the historical data, by using the patterns in resumes submitted to the company over a 10-year period, and the problem was that the majority came from men, a reflection of male dominance across the tech industry.
Inadequate data points about a certain group of people can be the reason. For example, image classifiers have higher rate error for images of dark-skinned women because there aren’t enough dataset with darker skin tones to train.
Wrong assumptions made during the development cases unfairness too. For example, facial analysis system to predict who is going to commit a crime based on images of people’s faces. The assumption to make it believe this system is capable of doing this could lead substantial harms for people who are misclassified.
Understand your models and build fairness
## Understand your models and build fairness
Although many aspects of fairness are not captured in quantitative fairness metrics, and it is not possible to fully remove bias from a system to guarantee fairness, you are still responsible to detect and to mitigate fairness issues as much as possible.
When you are working with machine learning models, it is important to understand your models with interpretability and assess and mitigate unfairness.
Let’s use the loan selection example to isolate the case to figure out each factors level of impact on the prediction.
Assessment methods
## Assessment methods
1. Identify the harms (and benefits)
2. Identify the affected groups
3. Define fairness metrics
Identify the harms (and benefits)
### Identify the harms (and benefits)
What are the harms and benefits associated with lending? Think false negatives and false positive scenarios:
**False negatives** (reject, but Y=1) - when an applicant will be capable of repaying loan is rejected. This is an adverse event because the resources of the loans are withheld from qualified applicants.
@ -111,7 +118,7 @@ Identify the affected groups
The next step is to determine which groups are likely to be affected. For example, in case of a credit card application, where you see women are receiving much lower credit limits compared with their spouses who shares assets, the affected groups can be defined by the gender identity.
Define fairness metrics
### Define fairness metrics
You have identified harms and affected groups, in this case, gender. Now, use the quantified factors to disaggregate metrics. For example, when you have the data below, by examining this table, we see the women has the largest false positive rate and men has the smallest, and the opposite for false negatives.
@ -123,25 +130,27 @@ You have identified harms and affected groups, in this case, gender. Now, use th
Also, note that this table also tells us that the non-binary people have much smaller count. It means the data is less certain and possibly has a larger error bars, so you need to be more careful how you interpret these numbers.
So, in this case, we have 3 groups and 2 metrics. When we are thinking about how our system affects the group of customers/loan applicants, this may be sufficient, but when you want to define larger number of groups, you may want to distill this to smaller sets of summaries. To do that, you can add more metrics, such as largest difference, and smallest ratio of each false negative/positive rates.
✅ Discussion: What other groups are likely to be affected for loan application?
Mitigating unfairness
## Mitigating unfairness
To mitigate the fairness issue, explorer the model to generate various mitigated models and compare them navigate tradeoffs between accuracy and fairness to select the model with the desired trade off.
This intro lesson does not dive deeply into the details of algorithmic unfairness mitigation, such as post-processing and reductions approach, but introducing a tool that you may want to try.
Fairlearn
## Fairlearn
\[Fairlearn\](https://fairlearn.github.io/) is an open-source Python package that allows you to assess your systems' fairness and mitigate unfairness.
The tool may help you to assesses how a model's predictions affect different groups, enables comparing multiple models by using fairness and performance metrics, and supply a set of algorithms to mitigate unfairness in binary classification and regression.
- Learn how to use the different components by checking out the Fairlearn's [GitHub](https://github.com/fairlearn/fairlearn/), [user guide](https://fairlearn.github.io/main/user_guide/index.html), [examples](https://fairlearn.github.io/main/auto_examples/index.html), and [sample notebooks](https://github.com/fairlearn/fairlearn/tree/master/notebooks).
- Learn [how to](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml) enable fairness assessment of machine learning models in Azure Machine Learning.
- See the [sample notebooks](https://github.com/Azure/MachineLearningNotebooks/tree/master/contrib/fairness) for more fairness assessment scenarios in Azure Machine Learning.
## 🚀 Challenge
To avoid biases to be introduced in the first place, we should:
@ -150,35 +159,27 @@ To avoid biases to be introduced in the first place, we should:
- invest in datasets that reflect the diversity in our society
- develop better methods for detecting and correcting bias when it occurs
Open-end discussion: what else should we consider?
In this lesson, you have learned fairness/unfairness in ML.
In this lesson, you have learned about fairness/unfairness in ML.
Watch this workshop to dive deeper into the topics:
■ YouTube: Fairness-related harms in AI systems: Examples, assessment, and mitigation by Hanna Wallach and Miro Dudik [Fairness-related harms in AI systems: Examples, assessment, and mitigation - YouTube](https://www.youtube.com/watch?v=1RptHwfkx_k)
Also, read:
■ Microsoft’s RAI resource center: [Responsible AI Resources – Microsoft AI](https://www.microsoft.com/en-us/ai/responsible-ai-resources?activetab=pivot1%3aprimaryr4)
■ Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/en-us/research/theme/fate/)
> ✅ Learn more about Responsible AI by following this [Learning Path](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/?WT.mc_id=academic-15963-cxa)
[](https://youtu.be/dnC8-uUZXSc "Microsoft's Approach to Responsible AI")
| 02 | [Introduction](Introduction/README.md) | The History of Machine Learning | Learn the history underlying this field | [lesson](Introduction/2-history-of-ML/README.md) | Amy |
| 03 | [Introduction](Introduction/README.md) | The Ethics of Machine Learning | What are the important ethical issues that students should consider when building and applying ML models? | [lesson](Introduction/3-ethics/README.md) | Tomomi |
| 03 | [Introduction](Introduction/README.md) | Fairness and Machine Learning | What are the important philosophical issues around fairness that students should consider when building and applying ML models? | [lesson](Introduction/3-fairness/README.md) | Tomomi |
| 04 | Introduction to Regression | [Regression](Regression/README.md) | Get started with Python and Scikit-Learn for Regression models | [lesson](Regression/1-Tools/README.md) | Jen |
| 05 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Visualize and clean data in preparation for ML | [lesson](Regression/2-Data/README.md) | Jen |
| 06 | North American Pumpkin Prices 🎃 | [Regression](Regression/README.md) | Build Linear and Polynomial Regression models | [lesson](Regression/3-Linear/README.md) | Jen |
Jen Looper
3 years ago