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[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”] Publishes Paper on ‘Explainable Machine Learning’ for NeurIPS 2021

Explainable Machine Learning is a sub-field within Data Science and Artificial Intelligence (AI). It is also referred to as X-ML or XML, and projected to be the next biggest avenue for all AI and machine learning applications in the future., a leading AI startup has made substantial progress in the field of Explainable Machine Learning, which has been published in its latest paper. This paper is all set to appear at Neural Information Processing Systems (NeurIPS) Conference 2021, to be held between 7-10 December later this year.

Explainable Machine Learning or XML is tested based on three key parameters – transparency, interpretability, and explainability. For any plain machine learning model to qualify as an XML algorithm, it should be understood using concepts of human-level intelligence. In recent years, significant developments have been made in this area with an aim to bring AI and Deep Learning models out of the conventional “black box’ domains. As per IBM, machine learning models are often thought to be behaving as black-boxes that are hard to interpret.

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In its latest paper on XML, has released the workflow associated with XAI-BENCH. XAI-BENCH is a battery of synthetic datasets for “benchmarking popular feature attribution algorithms.” The synthetic dataset could be configured and re-engineered to simulate real-world data using popular explainability techniques across several evaluation metrics.

AI is becoming advanced and human brains behind this trend associate the evolution to powerful XML techniques which are entrusted to bring computing out of black-box approaches. The black box legacy within conventional AI ML algorithms is so deeply entrenched that it would require much more than publishing papers on XML. is putting its brain and brawn behind XML models to help scientists and AI engineers understand the various ways they can create an algorithm that humans can understand and evaluate what’s happening inside the ‘black-box’ of the AI ML field.

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Explainable Machine Learning or XML is already influencing the penetration of advanced AI in various industries. Some of the key applications of XML in the modern era have been listed below:

In healthcare and telemedicine: XML is used to optimize image analysis, diagnostics, and decision-making for patient management processes;

In banking and loan approval systems, where XML is used to evaluate credit health and financial fraud risks;

In blockchain and crypto, where XAI  and machine learning algorithms can be used to fully secure and decentralize the “highly sensitive system for storing and processing AI-generated data”, and so much more…

As we continue to trace the next phase of advanced AI growth in the marketplace, it is expected that companies like would emerge as the top contributors of trustworthy AI abilities that break the conventional mold of black-box modeling.

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