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Decoding Machine Learning to Simplify Complex Model Interpretation

Image credits: news.mit.edu

Machine-learning models are utilised in the real world to assist radiologists in identifying potential diseases in X-rays; however, these models are intricate and their prediction process remains elusive even to their creators. To address this, researchers employ saliency methods, techniques that seek to offer insights into the model’s behaviour and elucidate its decision-making procedure.

Researchers from the Massachusetts Institute of Technology (MIT) and a multinational technology company have collaboratively developed a tool with a new method to assist users in selecting the most suitable saliency method for their specific requirements. Therefore, they introduced saliency cards, providing standardised documentation summarising how a particular process of saliency operates, including its strengths, weaknesses, and explanations to aid users in correctly interpreting the method’s outputs.

The Co-lead Author, Angie Boggust, a graduate student in electrical engineering and computer science at MIT and a member of the Visualization Group of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), expresses the team’s aspiration that users equipped with this knowledge will be able to consciously select a suitable saliency method based on the specific machine-learning model being employed and the task it aims to accomplish.

Boggust explains that saliency cards are purposefully crafted to provide a concise and easily understandable overview of a saliency method while highlighting the essential attributes most relevant to human users. These cards are intended to be accessible to a wide range of individuals, including machine-learning researchers and even those unfamiliar with the field and seeking guidance in selecting a saliency method for the first time.

Choosing the “wrong” saliency method can have serious consequences. For instance, one saliency method known as integrated gradients compares the importance of features in an image to a meaningless reference point. Features with the highest priority compared to this reference point are considered the most meaningful for the model’s prediction. If an unsuitable saliency method is chosen, it can lead to incorrect or misleading interpretations of the model’s behaviour and predictions. Therefore, selecting a saliency method appropriate for the specific task requirements is crucial to avoid these consequences.

Saliency cards can assist users in avoiding choosing “the wrong method” by reducing the operational details of a saliency method into ten user-centric attributes. The attributes encompass the methodology for calculating saliency, the connection between the saliency method and the model, and how users interpret the outputs generated by the method.

The saliency cards can also serve as a valuable resource for scientists by revealing areas where further research is needed. For instance, the researchers from MIT encountered a challenge in finding a saliency method that was both computationally efficient and applicable to any machine-learning model. This highlights a gap in the research space that warrants further exploration and development.

In the future, the researchers aim to delve into the less-explored attributes of saliency methods and potentially create task-specific saliency techniques. They also seek to enhance their understanding of how individuals perceive saliency method outputs, with the potential for developing improved visualisations. Furthermore, they have made their work accessible through a public repository, inviting feedback from others that will contribute to future advancements.

Boggust is optimistic, envisioning these saliency cards as dynamic documents that will evolve as new saliency methods and evaluations emerge. Ultimately, this marks just the beginning of a broader discussion regarding the attributes of saliency methods and their relevance to different tasks. Boggust believes that in the future, there will be other researchers who will further develop this discovery.

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Qlik’s vision is a data-literate world, where everyone can use data and analytics to improve decision-making and solve their most challenging problems. A private company, Qlik offers real-time data integration and analytics solutions, powered by Qlik Cloud, to close the gaps between data, insights and action. By transforming data into Active Intelligence, businesses can drive better decisions, improve revenue and profitability, and optimize customer relationships. Qlik serves more than 38,000 active customers in over 100 countries.

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CTC Global Singapore, a premier end-to-end IT solutions provider, is a fully owned subsidiary of ITOCHU Techno-Solutions Corporation (CTC) and ITOCHU Corporation.

Since 1972, CTC has established itself as one of the country’s top IT solutions providers. With 50 years of experience, headed by an experienced management team and staffed by over 200 qualified IT professionals, we support organizations with integrated IT solutions expertise in Autonomous IT, Cyber Security, Digital Transformation, Enterprise Cloud Infrastructure, Workplace Modernization and Professional Services.

Well-known for our strengths in system integration and consultation, CTC Global proves to be the preferred IT outsourcing destination for organizations all over Singapore today.

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Planview has one mission: to build the future of connected work. Our solutions enable organizations to connect the business from ideas to impact, empowering companies to accelerate the achievement of what matters most. Planview’s full spectrum of Portfolio Management and Work Management solutions creates an organizational focus on the strategic outcomes that matter and empowers teams to deliver their best work, no matter how they work. The comprehensive Planview platform and enterprise success model enables customers to deliver innovative, competitive products, services, and customer experiences. Headquartered in Austin, Texas, with locations around the world, Planview has more than 1,300 employees supporting 4,500 customers and 2.6 million users worldwide. For more information, visit www.planview.com.

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SIRIM is a premier industrial research and technology organisation in Malaysia, wholly-owned by the Minister​ of Finance Incorporated. With over forty years of experience and expertise, SIRIM is mandated as the machinery for research and technology development, and the national champion of quality. SIRIM has always played a major role in the development of the country’s private sector. By tapping into our expertise and knowledge base, we focus on developing new technologies and improvements in the manufacturing, technology and services sectors. We nurture Small Medium Enterprises (SME) growth with solutions for technology penetration and upgrading, making it an ideal technology partner for SMEs.

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HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. 

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IBM is a leading global hybrid cloud and AI, and business services provider. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. Nearly 3,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and business services deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service.

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