INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
The increasing use of artificial intelligence (AI) in robotics has led to concerns about the potential for bias and unfairness in AI decision-making. As AI algorithms are only as good as the data they are trained on, if the data is biased, the algorithm may also be biased, resulting in unfair or discriminatory decisions. This paper examines the problem of bias and fairness in AI robotics, and potential solutions for addressing these issues.
We first provide an overview of the sources of bias in AI robotics, including biased data, biased models, and biased decision-making. We then discuss the potential implications of bias and unfairness in AI decision-making, including the perpetuation of social inequality and discrimination. We also review recent research on methods for detecting and mitigating bias in AI decision-making, including fairness constraints, causal reasoning, and counterfactual analysis.
Finally, we explore some of the challenges and limitations associated with addressing bias and fairness in AI robotics, such as the trade-off between fairness and accuracy, the difficulty of defining and measuring fairness, and the potential for unintended consequences. We conclude by highlighting the importance of continued research into the problem of bias and fairness in AI robotics, and the need for interdisciplinary collaboration to develop more effective solutions.
Keywords:
Artificial Intelligence, Fairness, Discrimination, Machine Learning, Data Bias, Algorithmic Bias, Ethical AI, Social Justice, Data Science, Transparency, Accountability, Explainability, Diversity and Inclusion, Sensitivity Analysis, Causal Inference, Model Validation, Governance.
Cite Article:
"Problem & Solution for Bias and Fairness in AI Robotics", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.g470-g475, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304664.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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