Data Quality

Data quality refers to the degree to which data used to train, test, and deploy an AI system is accurate, relevant, complete, consistent, and free from bias, errors, or other issues that could affect the performance or outcomes of the AI system.

All Terms

J

K

N

O

Q

U

V

W

X

Y

Z

1

A

2

B

3

C

4

D

5

E

6

F

7

G

8

H

9

I

12

L

13

M

16

P

18

R

19

S

20

T

E

A

A

A

A

A

AI Risk Management

A

R

A

Governance Artifact

G

Artificial Intelligence

A

Artificial General Intelligence

A

A

A

Autonomous System

A

A

Auditing or Audibility of AI Systems

A

Credo AI Audit Trail

C

A

Bias (Social vs. Statistical):

B

Conversational AI

C

Conformity Assessment

C

D

E

E

F

Foundational Model

F

General-purpose AI (GPAI)

G

Generative AI or genAI

G

H

Human-Centered Design

H

Human-in-the-loop

H

Human-on-the-loop

H

Impact Assessment

I

I

Interpretability

I

L

Machine Learning

M

M

Multi-Stakeholder Collaboration

M

P

Credo AI Policy Center

C

Credo AI Policy Pack

C

P

Programmatic RAI Assessments

P

R

Responsible AI License

R

A

R

Rulemaking Process

R

S

Social-technical Systems

S

S

Technical Evidence

T

T

Transparency Report

T

T

A

Project Failure Rate

P

Project Rejection Stage

P

Sunk Project Cost

S

B

Compliance Risk

C

T

AI GRC Project Rejection Rate

A

Transformative AI (TAI)

T