Enrollment prediction services contracts
It is a clone of this request.
| Tracking # |
24-0057 |
| Submitted | Feb. 29, 2024 |
| Est. Completion | April 26, 2024 |
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Communications
From: Todd Feathers
To Whom It May Concern:
Pursuant to the New Mexico Inspection of Public Records Act, I hereby request the following records:
1) All active contracts and accomanying statements of work the university has entered into for predictive machine learning services that aid in the enrollment and recruitment of students. Specifically, this includes services that feed input data provided by the university about current and historical student enrollment into machine learning models in order to make predictions about the probability of future enrollment by individual students, students from geographic regions (e.g. zip code or high school), and/or students belonging to demographic groups (e.g. Black students or first-generation students). Popular providers of predictive enrollment services include, but are not limited to: EAB, RNL (Ruffalo, Noel, Levitz), Mainstay, Othot, and The College Board.
2) Documentation of the machine learning models included in the responsive contracts. Specifically, this includes documents created by the university or provided to the university by the vendor or a third party that describe:
a) Input data—the data the university provides for analysis by the model
b) Output data—the data the model generates for the university to use (e.g., a numerical score, a probability percentage, a letter grade, or other ranking)
c) Model accuracy—metrics such as AUC, false positive/negative rates, sensitivity, specificity, etc. that demonstrate how the model performs at its intended task
I ask that all fees be waived as I am a journalist and intend to used the requested records to publish articles in the public interest and not for any commercial purpose.
If you choose to impose fees, I request a detailed breakdown of the fees, including the hourly wage of each employee involved and an explanation of the employee hours required to fulfill the request.
If you choose to reject this request or redact portions of responsive documents, I ask that you provide a detailed breakdown of the statutory exemptions and associated case law underlying your decision to withhold each/any portions from public review.
The requested documents will be made available to the general public, and this request is not being made for commercial purposes.
In the event that there are fees, I would be grateful if you would inform me of the total charges in advance of fulfilling my request. I would prefer the request filled electronically, by e-mail attachment if available or CD-ROM if not.
Thank you in advance for your anticipated cooperation in this matter. I look forward to receiving your response to this request within 15 business days, as the statute requires.
Sincerely,
Todd Feathers
From: New Mexico State University
Good Afternoon, Mr. Feathers:
On Thursday, February 29, 2024, NMSU received your request (below) to inspect certain records. Specifically, your request stated:
"Pursuant to the New Mexico Inspection of Public Records Act, I hereby request the following records:
1) All active contracts and accomanying statements of work the university has entered into for predictive machine learning services that aid in the enrollment and recruitment of students. Specifically, this includes services that feed input data provided by the university about current and historical student enrollment into machine learning models in order to make predictions about the probability of future enrollment by individual students, students from geographic regions (e.g. zip code or high school), and/or students belonging to demographic groups (e.g. Black students or first-generation students). Popular providers of predictive enrollment services include, but are not limited to: EAB, RNL (Ruffalo, Noel, Levitz), Mainstay, Othot, and The College Board.
2) Documentation of the machine learning models included in the responsive contracts. Specifically, this includes documents created by the university or provided to the university by the vendor or a third party that describe:
a) Input data-the data the university provides for analysis by the model
b) Output data-the data the model generates for the university to use (e.g., a numerical score, a probability percentage, a letter grade, or other ranking)
c) Model accuracy-metrics such as AUC, false positive/negative rates, sensitivity, specificity, etc. that demonstrate how the model performs at its intended task"
We will need additional time to respond to your request. We will respond further on or before Friday, March 15, 2024
Pursuant to The Inspection of Public Records Act, a records custodian may charge reasonable fees for copying public records. New Mexico State University will provide the first 20 pages at no charge and charges $0.25 per printed page thereafter. Payment in advance may be required. If documents are requested electronically, a fee will be assessed for the actual cost of downloading, copying and/or transmitting of records including personnel time involved.
Sincerely,
Carol Gumm
University General Counsel
New Mexico State University
P. O. Box 30001 MSC 3UGC
Las Cruces, NM 88003-8001
Office: (575) 646-2446
Fax: (575) 646-3012
gencounsel@nmsu.edu<mailto:gencounsel@nmsu.edu>
NM STATE UNIVERSITY
BE BOLD. Shape the Future.
NOTICE: THIS MESSAGE IS INTENDED ONLY FOR THE USE OF THE INDIVIDUAL OR ENTITY TO WHICH IT IS ADDRESSED AND MAY CONTAIN INFORMATION THAT IS CONFIDENTIAL, EXEMPT FROM DISCLOSURE UNDER APPLICABLE LAW, AND PROTECTED BY THE ATTORNEY-CLIENT PRIVILEGE. If the reader of this message is not the intended recipient or agent responsible for delivering the message to the intended recipient, you are hereby notified that any review, dissemination or copying of this communication is strictly prohibited. If you have received this electronic transmission in error, please do not read it, delete it from your system without copying it, and notify the sender by reply e-mail or by calling 575.646.2446, so that our address record can be corrected. Thank you - University General Counsel, New Mexico State University.
From: New Mexico State University
Good Morning, Mr. Feathers:
On Thursday, February 29, 2024, NMSU received your request (below) to inspect certain records. Specifically, your request stated:
"Pursuant to the New Mexico Inspection of Public Records Act, I hereby request the following records:
1) All active contracts and accomanying statements of work the university has entered into for predictive machine learning services that aid in the enrollment and recruitment of students. Specifically, this includes services that feed input data provided by the university about current and historical student enrollment into machine learning models in order to make predictions about the probability of future enrollment by individual students, students from geographic regions (e.g. zip code or high school), and/or students belonging to demographic groups (e.g. Black students or first-generation students). Popular providers of predictive enrollment services include, but are not limited to: EAB, RNL (Ruffalo, Noel, Levitz), Mainstay, Othot, and The College Board.
2) Documentation of the machine learning models included in the responsive contracts. Specifically, this includes documents created by the university or provided to the university by the vendor or a third party that describe:
a) Input data-the data the university provides for analysis by the model
b) Output data-the data the model generates for the university to use (e.g., a numerical score, a probability percentage, a letter grade, or other ranking)
c) Model accuracy-metrics such as AUC, false positive/negative rates, sensitivity, specificity, etc. that demonstrate how the model performs at its intended task"
Your request is "excessively burdensome or broad" (see 14-2-10 NMSA), therefore NMSU will need additional time to respond to your request. We will respond further on or before Friday, April 12, 2024
Pursuant to The Inspection of Public Records Act, a records custodian may charge reasonable fees for copying public records. New Mexico State University will provide the first 20 pages at no charge and charges $0.25 per printed page thereafter. Payment in advance may be required. If documents are requested electronically, a fee will be assessed for the actual cost of downloading, copying and/or transmitting of records including personnel time involved.
Sincerely,
Carol Gumm
University General Counsel
New Mexico State University
P. O. Box 30001 MSC 3UGC
Las Cruces, NM 88003-8001
Office: (575) 646-2446
Fax: (575) 646-3012
gencounsel@nmsu.edu<mailto:gencounsel@nmsu.edu>
NM STATE UNIVERSITY
BE BOLD. Shape the Future.
NOTICE: THIS MESSAGE IS INTENDED ONLY FOR THE USE OF THE INDIVIDUAL OR ENTITY TO WHICH IT IS ADDRESSED AND MAY CONTAIN INFORMATION THAT IS CONFIDENTIAL, EXEMPT FROM DISCLOSURE UNDER APPLICABLE LAW, AND PROTECTED BY THE ATTORNEY-CLIENT PRIVILEGE. If the reader of this message is not the intended recipient or agent responsible for delivering the message to the intended recipient, you are hereby notified that any review, dissemination or copying of this communication is strictly prohibited. If you have received this electronic transmission in error, please do not read it, delete it from your system without copying it, and notify the sender by reply e-mail or by calling 575.646.2446, so that our address record can be corrected. Thank you - University General Counsel, New Mexico State University.
From: Muckrock Staff
To Whom It May Concern:
I'm following up on the following New Mexico Inspection of Public Records Act request, copied below, and originally submitted on Feb. 29, 2024. You had previously indicated that it would be completed on April 12, 2024. I wanted to check on the status of my request, and to see if there was a new estimated completion date.
Thanks for your help, and let me know if further clarification is needed.
From: New Mexico State University
A separate email will also be sent:
Friday, April 12, 2024
Mr. Feathers:
On Thursday, February 29, 2024, NMSU received your request (below) to inspect certain records. I have submitted through the link provided below, seven documents responsive to number one of your request.
1) All active contracts and accompanying statements of work the university has entered into for predictive machine learning services that aid in the enrollment and recruitment of students. Specifically, this includes services that feed input data provided by the university about current and historical student enrollment into machine learning models in order to make predictions about the probability of future enrollment by individual students, students from geographic regions (e.g. zip code or high school), and/or students belonging to demographic groups (e.g. Black students or first-generation students). Popular providers of predictive enrollment services include, but are not limited to: EAB, RNL (Ruffalo, Noel, Levitz), Mainstay, Othot, and The College Board.
a. Response: 7 documents provided
Pending Item:
2) Documentation of the machine learning models included in the responsive contracts. Specifically, this includes documents created by the university or provided to the university by the vendor or a third party that describe:
a) Input data—the data the university provides for analysis by the model
b) Output data—the data the model generates for the university to use (e.g., a numerical score, a probability percentage, a letter grade, or other ranking)
c) Model accuracy—metrics such as AUC, false positive/negative rates, sensitivity, specificity, etc. that demonstrate how the model performs at its intended task”
The records responsive to number two on your request involves multiple departments and we require additional time to respond. We will respond on or before Friday, April 26, 2024.
Anne M. Comeau-Phillips
Administrative Assistant
575-646-2431
Administration and Finance
acomeau.nmsu.edu
BE BOLD. Shape the Future.®
New Mexico State University
This message and all its attachments may contain information that is CONFIDENTIAL and PRIVILEGED. It is for the sole use of the intended recipient(s). Any unauthorized review, use, disclosure or distribution is prohibited. If you received this message in error, please notify the sender by replying via email and delete the message immediately.
Please consider the environment before printing this email.
From: New Mexico State University
Friday, April 12, 2024
Mr. Feathers:
On Thursday, February 29, 2024, NMSU received your request (below) to inspect certain records. I have submitted through the link provided below, seven documents responsive to number one of your request.
1. All active contracts and accompanying statements of work the university has entered into for predictive machine learning services that aid in the enrollment and recruitment of students. Specifically, this includes services that feed input data provided by the university about current and historical student enrollment into machine learning models in order to make predictions about the probability of future enrollment by individual students, students from geographic regions (e.g. zip code or high school), and/or students belonging to demographic groups (e.g. Black students or first-generation students). Popular providers of predictive enrollment services include, but are not limited to: EAB, RNL (Ruffalo, Noel, Levitz), Mainstay, Othot, and The College Board.
* Response: 7 documents provided
Pending Item:
2) Documentation of the machine learning models included in the responsive contracts. Specifically, this includes documents created by the university or provided to the university by the vendor or a third party that describe:
a) Input data-the data the university provides for analysis by the model
b) Output data-the data the model generates for the university to use (e.g., a numerical score, a probability percentage, a letter grade, or other ranking)
c) Model accuracy-metrics such as AUC, false positive/negative rates, sensitivity, specificity, etc. that demonstrate how the model performs at its intended task"
The records responsive to number two on your request involves multiple departments and we require additional time to respond. We will respond on or before Friday, April 26, 2024.
Anne M. Comeau-Phillips
Administrative Assistant
575-646-2431
Administration and Finance
acomeau.nmsu.edu
BE BOLD. Shape the Future.(r)
New Mexico State University
This message and all its attachments may contain information that is CONFIDENTIAL and PRIVILEGED. It is for the sole use of the intended recipient(s). Any unauthorized review, use, disclosure or distribution is prohibited. If you received this message in error, please notify the sender by replying via email and delete the message immediately.
[Ieaf-bwcolorizedWEB (3).gif]Please consider the environment before printing this email.
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From: New Mexico State University
Friday, April 26, 2024
Mr. Feathers:
On Thursday, February 29, 2024, NMSU received your request (below) to inspect certain records. On April 12, 2024 we provided 7 documents responsive to #1 "All active contracts . . ." of your request. After reaching out to numerous departments for records related to #2, NMSU does not have records responsive to the request. The 7 contracts were not related to "machine learning models" but provided other services.
2) Documentation of the machine learning models included in the responsive contracts. Specifically, this includes documents created by the university or provided to the university by the vendor or a third party that describe:
a) Input data-the data the university provides for analysis by the model
b) Output data-the data the model generates for the university to use (e.g., a numerical score, a probability percentage, a letter grade, or other ranking)
c) Model accuracy-metrics such as AUC, false positive/negative rates, sensitivity, specificity, etc. that demonstrate how the model performs at its intended task"
With this transmittal, we consider the matter closed.
Anne M. Comeau-Phillips
Administrative Assistant
575-646-2431
Administration and Finance
acomeau.nmsu.edu
BE BOLD. Shape the Future.(r)
New Mexico State University
This message and all its attachments may contain information that is CONFIDENTIAL and PRIVILEGED. It is for the sole use of the intended recipient(s). Any unauthorized review, use, disclosure or distribution is prohibited. If you received this message in error, please notify the sender by replying via email and delete the message immediately.
[Ieaf-bwcolorizedWEB (3).gif]Please consider the environment before printing this email.
-
image001