Subchapter S: Car Rentals

§ 5-290 Car Rental.

   (a)   Reservations. A motor vehicle rental business which reserves vehicles for consumers must make available either the vehicles reserved, or one which seats at least as many passengers as the one reserved and is suitable for the consumer's purposes, within one-half hour of the reserved time, either at the reserved location or at another location to which the consumer is transported without charge. This subdivision (a) does not apply if the consumer is told when the reservation is made that the reservation is not guaranteed. The business must fulfill all other terms of the reservation.

   (b)   Deceptive advertising. A motor vehicle rental business which rents to consumers may not advertise or represent that reservations can be made, unless it maintains procedures to comply with this Regulation.

   (c)   Records. A business which reserves vehicles for consumers must maintain procedures reasonably calculated to fulfill those reservations. This includes maintaining records of all reservations, and of the scheduled return times of all rented vehicles. These records must be preserved for at least 6 months, and shall be made available for inspection and copying by the Department of Consumer and Worker Protection.

   (d)   Required sign. A motor vehicle rental business which rents vehicles to consumers must conspicuously display on the business premises a sign or notice entitled "Department of Consumer and Worker Protection Car Rental Law." The sign shall be not less than twelve (12) inches by eighteen (18) inches in dimension, with letters thereon to be not less than one (1) inch high. The sign must inform consumers of their rights under 6 RCNY § 5-290(a) and it must contain the following words:

"To report complaints, contact the New York City Department of Consumer and Worker Protection, (Insert the Department's current address), Complaint Phone: (Insert the Department's current complaint telephone number)."

The following statement complies with this requirement:

If you have made a reservation for a car (or truck), that reservation must be honored at the price originally promised, within one-half hour of the time originally promised, unless you are told when you make the reservation that it is not guaranteed. To report complaints, contact the New York City Department of Consumer and Worker Protection, (Insert the Department's current address), Complaint Phone: (Insert the Department's current complaint telephone number).

(Added City Record 11/26/2021, eff. 12/26/2021)

Subchapter T: Automated Employment Decision Tools

§ 5-300 Definitions .

As used in this subchapter, the following terms have the following meanings:

   Automated Employment Decision Tool. "Automated employment decision tool" or "AEDT" means "Automated employment decision tool" as defined by § 20-870 of the Code where the phrase "to substantially assist or replace discretionary decision making" means:

      i.   to rely solely on a simplified output (score, tag, classification, ranking, etc.), with no other factors considered; or

      ii.   to use a simplified output as one of a set of criteria where the simplified output is weighted more than any other criterion in the set; or

      iii.   to use a simplified output to overrule conclusions derived from other factors including human decision-making.

   Bias Audit. "Bias audit" means "Bias audit" as defined by § 20-870 of the Code.

   Candidate for Employment. "Candidate for employment" means a person who has applied for a specific employment position by submitting the necessary information or items in the format required by the employer or employment agency.

   Category. "Category" means any component 1 category required to be reported by employers pursuant to subsection (c) of section 2000e-8 of title 42 of the United States Code as specified in part 1602.7 of title 29 of the Code of Federal Regulations, as designated on the Equal Employment Opportunity Commission Employer Information Report EEO-1.

   Code. "Code" means the Administrative Code of the City of New York.

   Distribution Date. "Distribution date" means the date the employer or employment agency began using a specific AEDT.

   Employment Decision. "Employment decision" means "Employment decision" as defined by § 20-870 of the Code.

   Employment Agency. "Employment agency" means "Employment agency" as defined by 6 RCNY § 5-249 .

   Historical data. "Historical data" means data collected during an employer or employment agency's use of an AEDT to assess candidates for employment or employees for promotion.

   Independent Auditor. "Independent auditor" means a person or group that is capable of exercising objective and impartial judgment on all issues within the scope of a bias audit of an AEDT. An auditor is not an independent auditor of an AEDT if the auditor:

      i.   is or was involved in using, developing, or distributing the AEDT;

      ii.   at any point during the bias audit, has an employment relationship with an employer or employment agency that seeks to use or continue to use the AEDT or with a vendor that developed or distributes the AEDT; or

      iii.   at any point during the bias audit, has a direct financial interest or a material indirect financial interest in an employer or employment agency that seeks to use or continue to use the AEDT or in a vendor that developed or distributed the AEDT.

   Impact Ratio. "Impact ratio" means either (1) the selection rate for a category divided by the selection rate of the most selected category or (2) the scoring rate for a category divided by the scoring rate for the highest scoring category.

 

Impact Ratio =	selection rate for a category
	selection rate of the most selected category
OR
Impact Ratio =	scoring rate for a category
	scoring rate of the highest scoring category

 

   Machine learning, statistical modeling, data analytics, or artificial intelligence. "Machine learning, statistical modeling, data analytics, or artificial intelligence" means a group of mathematical, computer-based techniques:

      i.   that generate a prediction, meaning an expected outcome for an observation, such as an assessment of a candidate's fit or likelihood of success, or that generate a classification, meaning an assignment of an observation to a group, such as categorizations based on skill sets or aptitude; and

      ii.   for which a computer at least in part identifies the inputs, the relative importance placed on those inputs, and, if applicable, other parameters for the models in order to improve the accuracy of the prediction or classification.

   Scoring Rate. "Scoring Rate" means the rate at which individuals in a category receive a score above the sample's median score, where the score has been calculated by an AEDT.

   Screen. "Screen" means to make a determination about whether a candidate for employment or employee being considered for promotion should be selected or advanced in the hiring or promotion process.

   Selection Rate. "Selection rate" means the rate at which individuals in a category are either selected to move forward in the hiring process or assigned a classification by an AEDT. Such rate may be calculated by dividing the number of individuals in the category moving forward or assigned a classification by the total number of individuals in the category who applied for a position or were considered for promotion.

      Example: If 100 Hispanic women apply for a position and 40 are selected for an interview after use of an AEDT, the selection rate for Hispanic women is 40/100 or 40%.

   Simplified output. "Simplified output" means a prediction or classification as specified in the definition for "machine learning, statistical modelling, data analytics, or artificial intelligence." A simplified output may take the form of a score (e.g., rating a candidate's estimated technical skills), tag or categorization (e.g., categorizing a candidate's resume based on key words, assigning a skill or trait to a candidate), recommendation (e.g., whether a candidate should be given an interview), or ranking (e.g., arranging a list of candidates based on how well their cover letters match the job description). It does not refer to the output from analytical tools that translate or transcribe existing text, e.g., convert a resume from a PDF or transcribe a video or audio interview.

   Test data. "Test data" means data used to conduct a bias audit that is not historical data.

(Added City Record 4/6/2023, eff. 5/6/2023)

§ 5-301 Bias Audit.

   (a)   An employer or employment agency may not use or continue to use an AEDT if more than one year has passed since the most recent bias audit of the AEDT.

   (b)   Where an AEDT selects candidates for employment or employees being considered for promotion to move forward in the hiring process or classifies them into groups, a bias audit must, at a minimum:

      (1)   Calculate the selection rate for each category;

      (2)   Calculate the impact ratio for each category;

      (3)   Ensure that the calculations required in paragraphs (1) and (2) of this subdivision separately calculate the impact of the AEDT on:

         i.   Sex categories (e.g., impact ratio for selection of male candidates vs female candidates),

         ii.   Race/Ethnicity categories (e.g., impact ratio for selection of Hispanic or Latino candidates vs Black or African American [Not Hispanic or Latino] candidates), and

         iii.   intersectional categories of sex, ethnicity, and race (e.g., impact ratio for selection of Hispanic or Latino male candidates vs. Not Hispanic or Latino Black or African American female candidates).

      (4)   Ensure that the calculations in paragraphs (1), (2), and (3) of this subdivision are performed for each group, if an AEDT classifies candidates for employment or employees being considered for promotion into specified groups (e.g., leadership styles); and

      (5)   Indicate the number of individuals the AEDT assessed that are not included in the required calculations because they fall within an unknown category.

Example: An employer wants to use an AEDT to screen resumes and schedule interviews for a job posting. To do so, the employer must ensure that a bias audit of the AEDT was conducted no more than a year before the planned use of the AEDT. This bias audit is necessary even though the employer is not using the AEDT to make the final hiring decision, but only to screen at an early point in the application process. The employer asks the vendor for a bias audit. The vendor provides historical data regarding applicant selection that the vendor has collected from multiple employers to an independent auditor who will conduct a bias audit as follows:

 

Sex Categories
	# of Applicants	# Selected	Selection Rate	Impact Ratio
Male	1390	667	48%	1.00
Female	1181	555	47%	0.979

 

Race/Ethnicity Categories
	# of Applicants	# Selected	Selection Rate	Impact Ratio
Hispanic or Latino	408	204	50%	0.97
White (Not Hispanic or Latino)	797	412	52%	1.00
Black or African American (Not Hispanic or Latino)	390	170	44%	0.84
Native Hawaiian or Pacific Islander (Not Hispanic or Latino)	119	52	44%	0.85
Asian (Not Hispanic or Latino)	616	302	49%	0.95
Native American or Alaska Native (Not Hispanic or Latino)	41	18	44%	0.85
Two or More Races (Not Hispanic or Latino)	213	96	45%	0.87

 

Intersectional Categories
			# of Applicants	# Selected	Selection Rate	Impact Ratio
Hispanic or Latino		Male	205	90	43.9%	0.841
		Female	190	82	0.827	43.2%
Non/Hispanic or Latino	Male	White	412	215	52.2%	1.000
		Black or African American	226	95	42.0%	0.806
		Native Hawaiian or Pacific Islander	87	37	42.5%	0.815
		Asian	321	167	52.0%	0.997
		Native American or Alaska Native	24	11	45.8%	0.878
		Two or More Races	115	52	45.2%	0.866
	Female	White	385	197	51.2%	0.981
		Black or African American	164	75	45.7%	0.876
		Native Hawaiian or Pacific Islander	32	15	46.9%	0.898
		Asian	295	135	45.8%	0.877
		Native American or Alaska Native	17	7	41.2%	0.789
		Two or More Races	98	44	44.9%	0.860

 

Note: The AEDT was also used to assess 250 individuals with an unknown sex or race/ethnicity category. Data on those individuals was not included in the calculations above.

   (c)   Where an AEDT scores candidates for employment or employees being considered for promotion, a bias audit must, at a minimum:

      (1)   Calculate the median score for the full sample of applicants;

      (2)   Calculate the scoring rate for individuals in each category;

      (3)   Calculate the impact ratio for each category;

      (4)   Ensure that the calculations required in paragraphs (1), (2), and (3) of this subdivision separately calculate the impact of the AEDT on:

         i.   Sex categories (i.e., impact ratio for selection of male candidates vs female candidates),

         ii.   Race/Ethnicity categories (e.g., impact ratio for selection of Hispanic or Latino candidates vs Black or African American [Not Hispanic or Latino] candidates), and

         iii.   intersectional categories of sex, ethnicity, and race (e.g., impact ratio for selection of Hispanic or Latino male candidates vs. Not Hispanic or Latino Black or African American female candidates); and

      (5)   Indicate the number of individuals the AEDT assessed that are not included in the required calculations because they fall within an unknown category.

   (d)   Notwithstanding the requirements of paragraphs (2) and (3) of subdivision (b) and paragraphs (3) and (4) of subdivision (c), an independent auditor may exclude a category that represents less than 2% of the data being used for the bias audit from the required calculations for impact ratio. Where such a category is excluded, the summary of results must include the independent auditor's justification for the exclusion, as well as the number of applicants and scoring rate or selection rate for the excluded category.

Example: An employer uses an AEDT to score applicants for "culture fit." To do so, the employer must ensure that a bias audit of the AEDT was conducted no more than a year before the use of the AEDT. The employer provides historical data on "culture fit" score of applicants for each category to an independent auditor to conduct a bias audit as follows:

 

Sex Categories
	# of Applicants	Scoring Rate	Impact Ratio
Male	92	54.3%	1.00
Female	76	44.7%	0.82

 

Race/Ethnicity Categories
	# of Applicants	Scoring Rate	Impact Ratio
Hispanic or Latino	28	64.2%	1.00
White (Not Hispanic or Latino)	40	37.5%	0.58
Black or African American (Not Hispanic or Latino)	32	50.0%	0.78
Native Hawaiian or Pacific Islander (Not Hispanic or Latino)	8	62.5%	0.97
Asian (Not Hispanic or Latino)	24	41.7%	0.65
Native American or Alaska Native (Not Hispanic or Latino)	16	62.5%	0.97
Two or More Races (Not Hispanic or Latino)	20	50.0%	0.78

Intersectional Categories
			# of Applicants	Scoring Rate	Impact Ratio
Hispanic or Latino		Male	16	75%	1.00
		Female	12	50%	0.67
Non/Hispanic or Latino	Male	White	20	35%	0.47
		Black or African American	20	50%	0.67
		Native Hawaiian or Pacific Islander	4	75%	1.00
		Asian	12	58.3%	0.78
		Native American or Alaska Native	8	62.5	0.83
		Two or More Races	12	50%	0.67
	Female	White	20	40%	0.53
		Black or African American	12	50%	.67
		Native Hawaiian or Pacific Islander	4	50%	0.67
		Asian	12	25%	0.33
		Native American or Alaska Native	8	62.5%	0.83
		Two or More Races	8	50%	0.67

 

Note: The AEDT was used to assess 15 individuals with an unknown sex or race/ethnicity category. Data on these individuals was not included in the calculations above.

(Added City Record 4/6/2023, eff. 5/6/2023)