@ -27,16 +27,15 @@ Learn more about Responsible AI by following this [Learning Path](https://docs.m
## Unfairness in data and algorithms
*"If you torture the data long enough, it will confess to anything." - Ronald Coase*
> "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. Such manipulation can sometimes happen unintentionally. As humans, we all have bias, and you it is often difficult to consciously know when you are introducing bias in data.
Guaranteeing fairness in AI and machine learning remains a complex sociotechnical challenge. This means that it cannot be addressed from either purely social or technical perspectives.
[](https://eus-streaming-video-rt-microsoft-com.akamaized.net/3c12a201-0657-4449-999c-f41b25df9616/31ce46d9-85b0-4e84-b93e-225478de_2250.mp4)
> Watch this video to learn about the fairness and socio-technical challenges:
## Fairness-related harms
### Fairness-related harms
What do you mean by unfairness? "Unfairness" encompasses negative impacts, or "harms", for a group of people, such as those defined in terms of race, gender, age, or disability status.
@ -48,9 +47,9 @@ The main fairness-related harms can be classified as:
- Denigration
- Over- or under- representation
Let’s take a look at the examples.
Let’s take a look at the examples—
## Example of Allocation
#### Allocation
Consider a given system for screening loan applications. The system tends to pick white men as better candidates over other groups. As a result, loans are withheld from certain applicants.
@ -58,34 +57,32 @@ Another example would be an experimental hiring tool developed by a large corpor
✅ Do a little research to find a real-world example of something like this
## Quality of service
#### Quality of service
Researchers found that several 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
#### 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”.
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.
There five main types of harms are not mutually exclusive, and a single system can exhibit more than one type of harms.
Each case varies severities, for instance, unfairly labeling someone as a criminal is a much more severe harm than mislabeling an image but it's important to remember that even relatively non severe harms can make people feel alienated or singled out and the cumulative impact can be extremely oppressive.
✅ Discussion: Revisit some of the examples and see if they show different harms.
✅ **Discussion**: Revisit some of the examples and see if they show different harms.
| | Allocation | Quality of service | Stereotyping | Denigration | Over- or under- representation |
@ -99,31 +96,39 @@ Inadequate data points about a certain group of people can be the reason. For ex
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.
**False positives** (accept, but Y=0) - when the applicant does get the loan but eventually defaults. As the result, the applicant will be sent to the debt collection agencies, and possibly affects their future loan applications.
Identify the affected groups
#### 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.
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.
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.
@ -136,7 +141,7 @@ Also, note that this table also tells us that the non-binary people have much sm
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?
✅ **Discussion**: What other groups are likely to be affected for loan application?
## Mitigating unfairness
@ -144,16 +149,18 @@ To mitigate the fairness issue, explorer the model to generate various mitigated
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.
- Learn how to enable fairness assessment of machine learning models in [Azure Machine Learning](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml).
- 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:
@ -168,22 +175,24 @@ What else should we consider?
## Review & Self Study
In this lesson, you have learned about fairness/unfairness in ML.
Watch this workshop to dive deeper into the topics:
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)
📺 **Fairness-related harms in AI systems: Examples, assessment, and mitigation** by Hanna Wallach and Miro Dudik
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)
[](https://youtu.be/1RptHwfkx_k "Microsoft researchers Hanna Wallach and Miroslav Dudík will guide you through how AI systems can lead to a variety of fairness-related harms. ")
> Video: Microsoft researchers Hanna Wallach and Miroslav Dudík will guide you through how AI systems can lead to a variety of fairness-related harms.
■ Microsoft’s FATE research group: [FATE: Fairness, Accountability, Transparency, and Ethics in AI - Microsoft Research](https://www.microsoft.com/en-us/research/theme/fate/)
[](https://youtu.be/tL7t2O5Iu8E "Eric Horvitz, Technical Fellow and Director of Microsoft Research Labs, talks about some of the benefits AI and machine learning are bringing and why it is essential for companies to establish ethical principles to make sure AI is properly governed.")
> Video: Eric Horvitz, Technical Fellow and Director of Microsoft Research Labs, talks about some of the benefits AI and machine learning are bringing and why it is essential for companies to establish ethical principles to make sure AI is properly governed.
Tomomi ❤ Imura
4 years ago
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