Increasingly complex and autonomous systems require machine ethics to maximize the benefits and minimize the risks to society arising from the new technology. It is challenging to decide which type of ethical theory to employ and how to implement it effectively. This survey provides a threefold contribution. Firstly, it introduces a taxonomy to analyze the field of machine ethics from an ethical, implementational, and technical perspective. Secondly, an exhaustive selection and description of relevant works is presented. Thirdly, applying the new taxonomy to the selected works, dominant research patterns and lessons for the field are identified, and future directions for research are suggested.

Lack of understanding of the decisions made by model-based AI systems is an important barrier for their adoption. We examine counterfactual explanations as an alternative for explaining AI decisions. The counterfactual approach defines an explanation as a set of the system's data inputs that causally drives the decision (meaning that removing them changes the decision) and is irreducible (meaning that removing any subset of the inputs in the explanation does not change the decision). We generalize previous work on counterfactual explanations, resulting in a framework that (a) is model-agnostic, (b) can address features with arbitrary data types, (c) is able explain decisions made by complex AI systems that incorporate multiple models, and (d) is scalable to large numbers of features. We also propose a heuristic procedure to find the most useful explanations depending on the context. We contrast counterfactual explanations with another alternative: methods that explain model predictions by weighting features according to their importance (e.g., SHAP, LIME). This paper presents two fundamental reasons why explaining model predictions is not the same as explaining the decisions made using those predictions, suggesting we should carefully consider whether importance-weight explanations are well-suited to explain decisions made by AI systems. Specifically, we show that (1) features that have a large importance weight for a model prediction may not actually affect the corresponding decision, and (2) importance weights are insufficient to communicate whether and how features influence system decisions. We demonstrate this using several examples, including three detailed studies using real-world data that compare the counterfactual approach with SHAP and illustrate various conditions under which counterfactual explanations explain data-driven decisions better than feature importance weights.

Driver turnover has been above 90 percent for more than nine consecutive quarters and shows little sign of slowing down. What's more is that issues related to management and workplace policies and communication have caused 30 percent of drivers to leave their job. Keeping your drivers safe, recognizing them for doing the right thing, and offering a solid feedback loop are key to increasing driver engagement -- and retention. If the people who drive your products from one place to another do not feel like you have taken extra measures to keep them safe, then they will leave for jobs that do. Where does this leave you? The more drivers you lose, the more difficult it will become to transport your products.

BRUSSELS – The EU's proposal for a temporary ban on facial-recognition technology won backing from Alphabet Chief Executive Sundar Pichai on Monday but got a cool response from Microsoft President Brad Smith. While Pichai cited the possibility that the technology could be used for nefarious purposes as a reason for a moratorium, Smith said a ban was akin to using a meat cleaver instead of a scalpel to solve potential problems. "I think it is important that governments and regulations tackle it sooner rather than later and give a framework for it," Pichai told a conference in Brussels organized by think-tank Bruegel. "It can be immediate but maybe there's a waiting period before we really think about how it's being used," he said. "It's up to governments to chart the course" for the use of such technology.

The head of Google and parent company Alphabet has called for artificial intelligence (AI) to be regulated. Writing in the Financial Times, Sundar Pichai said it was "too important not to" impose regulation but argued for "a sensible approach". He said that individual areas of AI development, like self-driving cars and health tech, required tailored rules. Last week it was revealed that the European Commission is considering a five-year ban on facial recognition. Earlier this month, the White House published its own proposed regulatory principles and urged Europe to "avoid heavy-handed innovation-killing models".

Google's chief executive called Monday for a balanced approach to regulating artificial intelligence, telling a European audience that the technology brings benefits but also "negative consequences." Sundar Pichai's comments come as lawmakers and governments seriously consider putting limits on how artificial intelligence is used. "There is no question in my mind that artificial intelligence needs to be regulated. The question is how best to approach this," Pichai said, according to a transcript of his speech at a Brussel-based think tank. He noted that there's an important role for governments to play and that as the European Union and the U.S. start drawing up their own approaches to regulation, "international alignment" of any eventual rules will be critical.

In August of 2018, the Federal Minister of Justice approved the Drager Drug Test 5000 as the Approved Drug Screening Equipment (ADSE) for all Canadian police services. The device itself is costly ($6,000 per device, and $60 per swab) and has to be used under ideal conditions for proper analysis, according to experts. The device tests for commonly used drugs in oral fluids including THC, which is the major psychoactive component in cannabis. Although the device may excel at identifying presence of THC, it does not address the issue of impairment specially when studies do not support a strong correlation between THC levels and impairment. Currently, there's an urgent demand for a device to assist Canadian police officers in their drug impairment investigations which is where PredictMedix is likely to fill an unmet need.

Soft Robotics Inc., a pioneer in robotic grasping, announced today that it has raised $23 million in an oversubscribed Series B funding round. The round was co-led by Calibrate Ventures and Material Impact and included existing investors Honeywell, Hyperplane, Scale, Tekfen Ventures, and Yamaha. FANUC Corp., the world's largest industrial robot manufacturer, joined this round as a new investor in Soft Robotics. Soft Robotics previously announced a strategic partnership with FANUC to integrate Soft Robotics' mGrip adaptable gripper system with any FANUC robot through the deployment of a new controller. The combined product was introduced at IREX in Tokyo in December 2019.

Alphabet and Google CEO, Sundar Pichai, is the latest tech giant kingpin to make a public call for AI to be regulated while simultaneously encouraging lawmakers towards a dilute enabling framework that does not put any hard limits on what can be done with AI technologies. In an op-ed published in today's Financial Times, Pichai makes a headline-grabbing call for artificial intelligence to be regulated. But his pitch injects a suggestive undercurrent that puffs up the risk for humanity of not letting technologists get on with business as usual and apply AI at population-scale -- with the Google chief claiming: "AI has the potential to improve billions of lives, and the biggest risk may be failing to do so" -- thereby seeking to frame'no hard limits' as actually the safest option for humanity. Simultaneously the pitch downplays any negatives that might cloud the greater good that Pichai implies AI will unlock -- presenting "potential negative consequences" as simply the inevitable and necessary price of technological progress. It's all about managing the level of risk, is the leading suggestion, rather than questioning outright whether the use of a hugely risk-laden technology such as facial recognition should actually be viable in a democratic society.

Yes, companies use AI to automate various tasks, while consumers use AI to make their daily routines easier. But governments–and in particular militaries–also have a massive interest in the speed and scale offered by AI. Nation states are already using artificial intelligence to monitor their own citizens, and as the UK's Ministry of Defence (MoD) revealed last week, they'll also be using AI to make decisions related to national security and warfare. The MoD's Defence and Security Accelerator (DASA) has announced the initial injection of £4 million in funding for new projects and startups exploring how to use AI in the context of the British Navy. In particular, the DASA is looking to support AI- and machine learning-based technology that will "revolutionise the way warships make decisions and process thousands of strands of intelligence and data."