src/fairness/constants/local_constants.py

"""
Copyright 2024 Infosys Ltd.”

Use of this source code is governed by MIT license that can be found in the LICENSE file or at
MIT license https://opensource.org/licenses/MIT

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

"""


"""
fileName: local_constants.py
description: Local constants for usecase  module
"""

DELTED_SUCCESS_MESSAGE="Successfully deleted the usecase :"
USECASE_ALREADY_EXISTS= "Usecase with name PLACEHOLDER_TEXT already exists"
USECASE_NOT_FOUND_ERROR="Usecase id PLACEHOLDER_TEXT Not Found"
USECASE_NAME_VALIDATION_ERROR="Usecase name should not be empty"
SPACE_DELIMITER=" "
MITIGATED_MODEL_LOCAL_PATH = '../output/mitigated_model/'
PLACEHOLDER_TEXT="PLACEHOLDER_TEXT"
F_Desc ="Fairness and Bias refers to ensuring that artificial intelligence (AI) systems are designed and implemented in a fair and unbiased manner. Fairness and bias are required to ensure that AI systems do not perpetuate or amplify discrimination or prejudice. It is essential to develop and implement algorithms that provide fair outcomes for all individuals, regardless of their background or characteristics. It involves minimizing any discriminatory or biased outcomes that may arise due to biased training data or algorithmic biases. "
D_Desc ="Fairness should be addressed at every stage of the AI lifecycle, including data collection, preprocessing, algorithm development, model training, and deployment. Ensuring fairness is an ongoing process that requires continuous monitoring, evaluation, and improvement."
Obj_Desc =  "The objective of this report is to identify fairness of the dataset by analyzing potential biases using metrics that might favor or disadvantage certain groups or individuals."