Algorithmic Fairness and Bias in Machine Learning Systems
In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming mo...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04036.pdf |
| _version_ | 1797775987228803072 |
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| author | Chandra Rushil Sanjaya Karun Aravind AR Abbas Ahmed Radie Gulrukh Ruzieva kumar T. S. Senthil |
| author_facet | Chandra Rushil Sanjaya Karun Aravind AR Abbas Ahmed Radie Gulrukh Ruzieva kumar T. S. Senthil |
| author_sort | Chandra Rushil |
| collection | DOAJ |
| description | In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming more and more prevalent in decision-making processes across a variety of disciplines. This abstract gives a general explanation of the idea of algorithmic fairness, the difficulties posed by bias in machine learning systems, and different solutions to these problems. Algorithmic bias and fairness in machine learning systems are crucial issues in this regard that demand the attention of academics, practitioners, and policymakers. Building fair and unbiased machine learning systems that uphold equality and prevent discrimination requires addressing biases in training data, creating fairness-aware algorithms, encouraging transparency and interpretability, and encouraging diversity and inclusivity. |
| first_indexed | 2024-03-12T22:43:34Z |
| format | Article |
| id | doaj.art-c31753d0f4eb48449564fe57658a168f |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T22:43:34Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-c31753d0f4eb48449564fe57658a168f2023-07-21T09:28:35ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013990403610.1051/e3sconf/202339904036e3sconf_iconnect2023_04036Algorithmic Fairness and Bias in Machine Learning SystemsChandra Rushil0Sanjaya Karun1Aravind AR2Abbas Ahmed Radie3Gulrukh Ruzieva4kumar T. S. Senthil5Assistant Professor, Symbiosis Law School, Nagpur, Symbiosis International (Deemed University), Pune, India and (secondary affiliation of first author) Research Scholar, Gujarat National Law UniversityAssistant Professor, Symbiosis Law School, Nagpur, Symbiosis International (Deemed University), Pune, India and (Secondary affiliation of 2nd author) Research Scholar, VIT School of Law, Vellore Institute of TechnologyAssistant Professor, Prince Shri Venkateshwara Padmavathy Engineering CollegeCollege of pharmacy, The Islamic universityTashkent State Pedagogical UniversityAssistant professor, Department of mechanical Engineering, K. Ramakrishnan college of technologyIn recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming more and more prevalent in decision-making processes across a variety of disciplines. This abstract gives a general explanation of the idea of algorithmic fairness, the difficulties posed by bias in machine learning systems, and different solutions to these problems. Algorithmic bias and fairness in machine learning systems are crucial issues in this regard that demand the attention of academics, practitioners, and policymakers. Building fair and unbiased machine learning systems that uphold equality and prevent discrimination requires addressing biases in training data, creating fairness-aware algorithms, encouraging transparency and interpretability, and encouraging diversity and inclusivity.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04036.pdf |
| spellingShingle | Chandra Rushil Sanjaya Karun Aravind AR Abbas Ahmed Radie Gulrukh Ruzieva kumar T. S. Senthil Algorithmic Fairness and Bias in Machine Learning Systems E3S Web of Conferences |
| title | Algorithmic Fairness and Bias in Machine Learning Systems |
| title_full | Algorithmic Fairness and Bias in Machine Learning Systems |
| title_fullStr | Algorithmic Fairness and Bias in Machine Learning Systems |
| title_full_unstemmed | Algorithmic Fairness and Bias in Machine Learning Systems |
| title_short | Algorithmic Fairness and Bias in Machine Learning Systems |
| title_sort | algorithmic fairness and bias in machine learning systems |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04036.pdf |
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