Machine learning (ML) applications and the organizations that develop them should be accountable. Proposed regulations require impact assessment and there are calls to strengthen enforcement of regulations for ethical business practice regulations.a Responsible organizations should implement a "test-driven ethics" development approach rooted in pragmatist discourse ethics and lessons from test-driven development. This approach extends the popular "principles" approach to ethics seen in industry, government, and the academy.2 Adopting ethical principles will not guarantee ethical actions or outcomes.

This paper introduces Personalized Path Recourse, a novel method that generates recourse paths for an agent. The objective is to achieve desired goals (e.g., better outcomes compared to the agent's original paths of action), while ensuring a high similarity to the agent's original paths and being personalized to the agent. Personalization refers to the extent to which the new path is tailored to the agent's observed behavior patterns from their policy function. We train a personalized recourse agent to generate such personalized paths, which are obtained using reward functions that consider the goal, similarity, and personalization. The proposed method is applicable to both reinforcement learning and supervised learning settings for correcting or improving sequences of actions or sequences of data to achieve a pre-determined goal. The method is evaluated in various settings and demonstrates promising results.

Artificial intelligence could have the capability to pinpoint cancer diagnoses a lot sooner. A new study published in the journal Radiology last week noted that AI helped predict one-third of breast cancer cases up to two years prior to diagnosis. The research surveyed imaging data and screening information from BreastScreen Norway exams performed from January 2004 to December 2019. Women who were later diagnosed with breast cancer based on these exams were given an AI risk score by a "commercially available AI system," according to the study's findings. The scores were ranked 1-7 for low-risk malignancy, 8-9 for intermediate risk and 10 for high-risk malignancy.

Nick Ismail One in four people have mental health issues and in men under 50, suicide is the main cause of death. It is a huge, often misunderstood problem that pervades every society and most families. This was the opening message delivered by Valentin Tablan, an artificial intelligence expert and scientist at Ieso Digital Health, at the Healthcare Summit in London last month. His message was clear: the mental health problem is endemic, but we know the solution in cognitive behavioural therapy. The issue is not everyone has access to this service. There are not enough therapists to deliver the necessary therapy.

The vast majority of today's healthcare data comes from medical scans, and doctors have become stressed and overburdened as they struggle to interpret the images while managing patient care. By using AI and deep-learning technology to analyze patient scans, doctors can obtain results much faster while also improving diagnostic accuracy. Scans are not as easy to decipher as they may appear. Many contain dozens of images that doctors must pore over to arrive at a diagnosis. Pinpointing the exact location and dimensions of fractures, nodules, and other lesions is often difficult.

For Dr. Denis Lacombe, CEO of the European Organisation Research Treatment Cancer (EORTC) to fight cancer you need big data. "Cancer is notoriously complex," said Lacombe. "Not only is it more than 200 separate diseases, but it can express itself differently in each person and at each stage of its progression." Lacombe belives that for patients, big data and machine learning can transform cancer, reducing uncertainty and truly personalising treatment and care, as opposed to a one size fits all approach. "Finding the most tailored treatment for a person – the one that is going to have the best results - requires researchers to be precise and targeted," said Lacombe.

For Dr. Denis Lacombe, CEO of the European Organisation Research Treatment Cancer (EORTC) to fight cancer you need big data. "Cancer is notoriously complex," said Lacombe. "Not only is it more than 200 separate diseases, but it can express itself differently in each person and at each stage of its progression." Lacombe belives that for patients, big data and machine learning can transform cancer, reducing uncertainty and truly personalising treatment and care, as opposed to a one size fits all approach. "Finding the most tailored treatment for a person – the one that is going to have the best results - requires researchers to be precise and targeted," said Lacombe.

Hyperautomation has pushed the boundaries of traditional automation capabilities. Rather than relying on a single technology or task automation, enterprises now can access a bundle of advanced technologies that enable process mining, intelligent business process management (iBPM), artificial intelligence (AI), robotic process automation (RPA), and machine learning (ML). It is about achieving the ideal technology process design to automate and provide a seamless customer experience. According to Coherent Market Insights, the global Hyperautomation market is set to exhibit a CAGR of nearly 18.9% during 2019-2027. This is understandable as organisations are adopting automation technologies to go beyond the routine and be innovative. Let's take a look at how Hyperautomation technologies change the way enterprises work?

Sepsis remains one of the most costly and deadly of medical conditions. Sepsis is not a disease per se, but a syndrome, a collection of signs and symptoms, that indicated the presence of an overwhelming infection. Many, if not all, severely ill patients with COVID-19 had viral sepsis. Bacterial causes are more common, but sepsis in all its microbial forms carries a high mortality. Academics have long tortured clinical hospital data to find some statistical means of identifying sepsis or its incipient signs, because early intervention is associated with better outcomes.

If 2020 was the year that forced the acceleration of digital transformation, this year will be the year that tests which companies can reimagine the new rules of business and find opportunities to innovate. Atop the list of technologies driving this change are artificial intelligence (AI) and automation, which together enable companies to accelerate productivity and augment customer and employee experiences. While most get that AI and automation are keys to future scale and optimization, many don't realize the potential to reimagine the future of work and business through operational and business model innovation. While some companies are using technology to incrementally make existing systems and processes more efficient, others are finding ways to introduce entirely new customer and employee experiences that unlock new business value. With this vision, businesses can alter their trajectory and explore the art of the possible.