[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119723-en":3,"doc-seo-119723-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119723,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",6,"Technology","Responsible Design Patterns for Machine Learning Pipelines","Integrating ethical practices into AI development is essential to achieve safe, fair, and responsible operation across the entire life cycle of AI systems. Responsible AI applies ethical principles from design and data handling through deployment and monitoring to reduce risks and harms, including algorithmic bias. The paper proposes a comprehensive framework that embeds responsible design patterns into machine learning pipelines, adding new RDPs and a bias-mitigation pattern validated through expert survey evidence, guiding data scientists and policymakers toward ethical, observable production AI.","Responsible Design Patterns for Machine Learning  \nPipelines  \nSaud Hakem Al Harbi 1,2,3 , Lionel Nganyewou Tidjon 2 , and Foutse Khomh, Senior Member, IEEE 1  \n1Department of Computer Engineering, Polytechnique Montreal, Quebec, Canada  \n2Department of Engineering, CertKOR AI, Montreal, Quebec, Canada  \n3Tabiah University, Medina, Saudi Arabia  \narXiv :2306 .0 1788v 3 [ cs . SE] 7 Jun 2023  \nAbstract—Integrating ethical practices into the AI development process for artificial intelligence (AI) is essential to ensure safe, fair, and responsible operation. AI ethics involves applying ethical principles to the entire life cycle of AI systems. This is essential to mitigate potential risks and harms associated with AI, such as algorithm biases. To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines to guarantee ethical and fair outcomes. In this paper, we propose a comprehensive framework incorporating RDPs into ML pipelines to mitigate risks and ensure the ethical development, deployment, and post-deployment of AI systems. Our framework comprises new responsible AI design patterns for ML pipelines and a bias mitigation pattern validated through a survey of AI ethics and data experts on real-world scenarios. The framework guides data scientists and policymakers to implement ethical practices in AI development, deploy and monitor responsible AI systems in production.  \nIndex Terms—Responsible AI, Operational AI, AI Ethics, ML pipeline, Bias Mitigation, Design Patterns, AI Observability, AI Post-Deployment, AI Engineering, and Software Engineering.  \nI. INTRODUCTION  \nArtificial intelligence (AI) development is gaining prominence, requiring the integration of ethical practices into developing AI systems, particularly in the context of machine learning pipelines. As a result, responsible AI and ethical practices in AI development have emerged as crucial considerations in recent years. As AI technologies play an increasingly central role in our lives, adopting AI ethics becomes imperative. AI ethics entails the application of ethical principles throughout the lifecycle of AI systems, ensuring their operation in a safe, fair, and responsible manner. Extensive research has been conducted on this subject, with numerous studies focused on identifying optimal practices and approaches for incorporating ethical considerations into AI development [1]–[6] .  \nThe growing recognition of AI’s potential risks and harms is a critical driver for adopting AI ethics. These risks encompass algorithmic biases that can lead to unfair treatment of individuals or groups, privacy breaches arising from collecting and processing personal data, and the potential for AI systems to cause harm to people or the environment. Organizations and governments worldwide are progressively embracing AI ethics principles and frameworks to address these risks 123.  \n1IBM  \n2AI Google  \n[3](3 www.industry.gov.au)[ www.industry.gov.au](3 www.industry.gov.au)  \nThe European Commission recently proposed new AI regulations that aim to ensure that AI systems are transparent and accountable and operate in a safe and ethical manner [7] . In addition, the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems has developed a set of ethical principles for AI that are widely recognized and used [8] . Incorporating AI ethics into developing and deploying AI systems can have numerous benefits, such as enhancing safety, protecting human rights, and fostering trust and confidence in AI technologies. However, challenges such as more standardization and potential conflicts between different ethical principles exist. Despite these challenges, AI ethics adoption is essential for ensuring that AI technologies are developed and used responsibly and ethically. By considering ethical principles during the design, development, and deployment of AI systems, organizations and governments can ensure that these systems are safe, fair, ","cbCaincXeddWOdmw","https://ap.wps.com/l/cbCaincXeddWOdmw","pdf",490562,1,21,"English","en",105,"# Introduction\n## AI ethics and lifecycle responsibility\n## Risks and harms motivating responsible design\n## Compliance and governance considerations\n## Related regulations and guiding principles","[{\"question\":\"What problem does the paper target in machine learning pipelines?\",\"answer\":\"It targets the need to embed ethical practices into the full AI life cycle, from design through deployment and post-deployment, to reduce risks such as algorithmic bias.\"},{\"question\":\"What does the proposed framework include?\",\"answer\":\"It includes a comprehensive set of responsible design patterns for ML pipelines, plus a bias mitigation pattern validated via a survey of AI ethics and data experts using real-world scenarios.\"},{\"question\":\"How does compliance support responsible AI in the paper’s view?\",\"answer\":\"Compliance ensures AI systems follow laws, regulations, and ethical standards by incorporating ethical principles in design and development, performing audits and assessments, and applying governance and accountability to enable transparency and fairness for stakeholders.\"}]","Responsible Design Patterns for Machine Learning Pipelines | 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problem does the paper target in machine learning pipelines?","Question",{"text":75,"@type":76},"It targets the need to embed ethical practices into the full AI life cycle, from design through deployment and post-deployment, to reduce risks such as algorithmic bias.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed framework include?",{"text":80,"@type":76},"It includes a comprehensive set of responsible design patterns for ML pipelines, plus a bias mitigation pattern validated via a survey of AI ethics and data experts using real-world scenarios.",{"name":82,"@type":73,"acceptedAnswer":83},"How does compliance support responsible AI in the paper’s view?",{"text":84,"@type":76},"Compliance ensures AI systems follow laws, regulations, and ethical standards by incorporating ethical principles in design and development, performing audits and assessments, and applying governance and accountability to enable transparency and fairness for 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