{"id":4732,"date":"2023-09-28T12:31:16","date_gmt":"2023-09-28T11:31:16","guid":{"rendered":"https:\/\/www.calligo.io\/?p=4732"},"modified":"2024-01-18T14:09:22","modified_gmt":"2024-01-18T14:09:22","slug":"ai-explainability-balancing-human-machine-collaboration-and-potential","status":"publish","type":"post","link":"https:\/\/www.calligo.io\/insights\/beyond-data-podcast\/ai-explainability-balancing-human-machine-collaboration-and-potential\/","title":{"rendered":"AI Explainability – Balancing Human-Machine Collaboration and Potential"},"content":{"rendered":"\n

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Listen on Spotify<\/strong><\/a><\/div>\n<\/div>\n\n\n\n

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Artificial Intelligence (AI) and machine learning<\/a> have revolutionized numerous industries, offering automation and efficiency. However, achieving the optimal balance between human input and machine automation in AI model development is crucial but often overlooked. In our recent Beyond Data podcast, hosts Tessa Jones<\/a> and Peter Matson<\/a> were joined for a compelling discussion with the co-founder of Trubrics, Joel Hodgson<\/a>, where the importance of AI explainability, trust, user feedback, and ongoing monitoring were explored.<\/p>\n\n\n\n

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The Challenge of Model Adoption<\/strong><\/h2>\n\n\n\n

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Joel highlighted the challenge of model adoption, a common issue in the data science<\/a> landscape. Organizations invest significant time and resources in developing AI models, only to face skepticism and underutilization from non-technical stakeholders. This hesitation often arises from a lack of trust and understanding. Education and transparency are vital tools to address this challenge.<\/p>\n\n\n\n

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Effective Communication and Collaboration<\/strong><\/h2>\n\n\n\n

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Another significant hurdle is the gap in effective communication between business professionals and data scientists. Bridging this divide is essential to incorporate valuable domain knowledge into the model development process. The solution lies in creating feedback loops that enable collaboration between domain experts, business users, and data scientists throughout the model’s lifecycle. These feedback loops are crucial for gathering user insights, improving model performance, and building trust.<\/p>\n\n\n\n

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User-Centric Monitoring and Model Utility<\/strong><\/h2>\n\n\n\n

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Trubrics’<\/a> approach of “machine learning monitoring from the users’ point of view” shifts the focus from traditional machine learning metrics to user perception. Evaluating AI models based on their impact and utility to users, rather than just accuracy, is essential. Users’ experiences, trust, and satisfaction play a pivotal role in determining the effectiveness of AI models. Monitoring should identify issues impacting the user experience and ensure AI models align with user expectations.<\/p>\n\n\n\n

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Building Trust as the Foundation<\/strong><\/h2>\n\n\n\n

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Trust emerged as a cornerstone in AI adoption. Trust is not limited to data scientists but extends to end-users, employees, and the entire organization. It involves transparent communication, feedback loops, and alignment between different groups. Over time, as individuals become more familiar with AI in the business world, this trust can be built and weaved into organizations\u2019 culture, just as our trust in everyday technology has.<\/p>\n\n\n\n

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Balancing Technical and Business Monitoring<\/strong><\/h2>\n\n\n\n

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Monitoring AI models’ performance is essential. Technical monitoring involves tracking various model characteristics, while business-facing monitoring assesses alignment with expectations and business impact. These two facets of monitoring are crucial in ensuring AI models continue to meet user needs and business objectives and therefore must be aligned when identifying the reasoning and desired outcomes from such models.<\/p>\n\n\n\n

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Measuring ROI and Sustained Value<\/strong><\/h2>\n\n\n\n

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Measuring and evaluating the Return on Investment (ROI) for AI models presents considerable challenges, especially when examining their performance over extended periods. Striking a balance between the continual expenses associated with model maintenance and the value it delivers requires a nuanced approach. Organizations need to account for both the initial and ongoing financial ROI assessment, recognizing that it can become less clear-cut.<\/p>\n\n\n\n

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According to a recent research report<\/a> conducted by Calligo, in collaboration with the Global CIO Institute, “36% of business leaders measure the success of an ML project in financial terms, while 11% either have no way to gauge success or go by gut feeling<\/strong>“. This suggests that determining the ROI for ML and AI initiatives isn’t solely tied to financial gains; it also involves a significant degree of uncertainty when the desired ROI isn’t well-defined at the project’s outset.<\/p>\n\n\n\n

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In conclusion, AI explainability and the balance between human input and machine automation are crucial in AI model development. Education, transparency, effective communication, user-centric monitoring, and trust-building are essential elements in this endeavor. As AI continues to shape our world, achieving these elements will be pivotal to ensure responsible and ethical AI development and its successful integration into our lives. Organizations like Trubrics are at the forefront of this mission, working towards making AI a valuable and trusted tool in our increasingly automated world.<\/p>\n\n\n\n

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Listen on Spotify<\/strong><\/a> or watch below<\/p>\n\n\n\n

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  Artificial Intelligence (AI) and machine learning have revolutionized numerous industries, offering automation and efficiency. However, achieving the optimal balance between human input and machine automation in AI model development is crucial but often overlooked. In our recent Beyond Data podcast, hosts Tessa Jones and Peter Matson were joined for a compelling discussion with the […]<\/p>\n","protected":false},"author":34,"featured_media":4733,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[44,121,38,137],"tags":[98,118,97,100,55,117,63,64],"post_format_type":[39],"class_list":["post-4732","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-beyond-data-podcast","category-glossary","category-machine-learning","category-managed-cloud","tag-ai","tag-ai-interpretability","tag-artificial-intelligence","tag-data-science","tag-digital-transformation","tag-explainability","tag-machine-learning","tag-ml"],"acf":[],"yoast_head":"\nAI Explainability - Balancing the human machine potential | Calligo<\/title>\n<meta name=\"description\" content=\"Achieving the optimal balance between human input and machine automation in AI model development is crucial but often overlooked.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.calligo.io\/insights\/machine-learning\/ai-explainability-balancing-human-machine-collaboration-and-potential\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Explainability - 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