What is an AEDT,

or Automated Employment Decision Tool?

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Defining AEDTs


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Automated decision tools (ADTs) are processes that rely on computerized components to make or influence a decision.

An automated employment decision tool (AEDT) is an ADT used in the process of employment — either hiring an employee or promoting one.

The legal definition of the law is lengthy but you can read it by hovering over here

A key component of ADTs are algorithms.

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What are Algorithms?

They're just instructions.

All ADTs use computer programs that are made up of algorithms — detailed sets of automated, computerized instructions. These algorithms work together to complete a task.

Just think about how you buy an avocado. What factors do you consider?

I want one that's affordable.

Hopefully it's organic, but less than $1.50.

I'm going to make some guacamole to eat tonight,
so I want the ripest one I can find.

Hover over the avocados to find the best one.

First non-ideal avocado cut in half Second non-ideal avocado cut in half Third non-ideal avocado cut in half Best avocado cut in half jumping up and down
All of these calculations you're doing in your head are an algorithm.

You’ve taken some variables (the price, how ripe it is, and is it organic or not), you've given those variables weights (buying a cheap, ripe avocado is more important than an organic avocado), you've analyzed a set of data points (the pile of avocados), and you've reached a decision (which avocado should you buy).

What kind of algorithms do ADTs use?

It depends. They can sometimes be a bit more complicated than the ones we use in our everyday decisions. Some ADTs involve multiple algorithms, hundreds of thousands of data points, and variables that are given different weights.

But really, they all use the same building blocks for the instructions — variables, weights, and data points.

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Examples

Learn about real examples of ADTs and AEDTs.

New York Public Library uses an algorithm to stock books.

A library needs books, and librarians need to pick them. But how should they do that? Libraries have limited resources and they can't really test out new books by buying and returning them. How can librarians tell which books will be popular with their patrons? What else should they consider when they're picking titles for their library branch?

Libraries use ESP to guess what we want to read.

No, not extra-sensory perception.

ESP is an algorithm that uses library and non-library data to recommend books it thinks will be popular. Here's how the system works:

1. Data

ESP is fed circulation data (like which books have been checked out) and sales data from a book warehouse called Baker & Taylor. It uses book reviews, but only in aggregate (total number of reviews, not whether they were good or bad).

2. Predict

The ESP algorithm looks for historical patterns in the data and makes suggestions for what books to stock at which library branches. For example it might predict that the young adult book Twilight will be popular in a specific branch because that branch has lots of teenagers that checked out Dracula and Harry Potter.

3. Pick

Then, it's up to the librarian to choose whether or not to follow the algorithm's advice.

ESP augments a librarian's choices by highlighting gaps in a librarian's knowledge and experience. On the flip side, it could sway librarians away from trusting their own instincts on what their branch needs.

A common example of an AEDT: resume screening.

Employers get a vast number of applications for a small number of jobs. Reviewing each application manually might not be possible in some cases. But at the same time, employers want to make sure that all qualified candidates have a shot at the job. How can employers quickly filter a large pile of resumes to find the most promising candidates?

Employers often rely on AEDT software to find the best resumes.

Here's how the most basic kind of resume screening works:

1. Data

First, the resume screener tool needs (unsurprisingly!) your resume, often in a PDF or other document format.

Second, the employer gets to choose which keywords to look for. They'll choose keywords that they want to see in job applications, like "administrative experience" or "nursing."

2. Scan

The AEDT then extracts as many words from the resume as possible, sometimes using text recognition to recognize text in images. The AEDT is also usually smart enough to understand close synonyms, like "video editing" and "film editing."

3. Pick

Finally, the AEDT gives the employer a ranked list or subset of the job applications to consider further.

Sometimes though, this process is automated. An employer can choose to automatically invite to interview (or reject) any candidate that does (or doesn't) meet their chosen keywords.

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What does NYC law say about AEDTs?

Knowing that an AEDT was used in your job application can help you figure out if you were discriminated against for a job.

The law says all NYC employers must notify all NYC residents when they use an AEDT.

The law says that employers have to send a notice 10 days before they use an AEDT.

The law also says that employers have to let you ask them to use something else other than the AEDT.

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Credits

Modified from Automating NYC, originally made by Aki Younge, Deepra Yusuf, Elyse Voegeli, and Jon Truong. Modified freely under a GNU license.

Thanks to Font Awesome and Feather Icons Library for free fonts and icons. All other fonts and visuals are created by the original authors.