Digital technologies have dramatically accelerated the digital transformation in process industries, boosted new industrial applications, upgraded the production system, and enhanced operational efficiency. In contrast, the challenges and gaps between human and artificial intelligence (AI) have become more and more prominent, whereas the digital divide in process safety is aggregating. The study attempts to address the following questions: (i)What is AI in the process safety context? (ii)What is the difference between AI and humans in process safety? (iii)How do AI and humans collaborate in process safety? (iv)What are the challenges and gaps in human-AI collaboration? (v)How to quantify the risk of human-AI collaboration in process safety? Qualitative risk analysis based on brainstorming and literature review, and quantitative risk analysis based on layer of protection analysis (LOPA) and Bayesian network (BN), were applied to explore and model. The importance of human reliability should be stressed in the digital age, not usually to increase the reliability of AI, and human-centered AI design in process safety needs to be propagated.

Are you curious about what could go wrong with AI projects? If you've heard about some of the artificial trends of 2022 and are possibly thinking about incorporating AI into your workflow, you may be cautious of AI project failures. These have left countless companies facing huge losses and a compromised workflow. Unfortunately, it is the case today that the majority of AI initiatives fail. A Pactera study established that 85% of all AI projects end up not meeting objectives.

AI implementations share a number of key characteristics. Rather than simply unleashing the technology first on one goal and then another in a linear fashion, which is the habit for most traditional technology initiatives, a more effective approach is to direct it at three critical capabilities: business transformation, enhanced decision-making and systems and process modernization.

The enterprise is rushing headfirst into AI-driven analytics and processes. However, based on the success rate so far, it appears there will be a steep learning curve before it starts to make noticeable contributions to most data operations. While positive stories are starting to emerge, the fact remains that most AI projects fail. The reasons vary, but in the end, it comes down to a lack of experience with the technology, which will most certainly improve over time. In the meantime, it might help to examine some of the pain points that lead to AI failure to hopefully flatten out the learning curve and shorten its duration.

Artificial intelligence is groundbreaking and, at times, still quite mind blowing. We're constantly peppered with amazing stories of efficiency, automation, and intelligent prognostication. And for every story of success, there's another tale of a mess up or mistake – a situation where something didn't go as planned. While I'm a huge believer in AI and have seen the power of it in my own businesses, sometimes it's nice to see the other side of the coin, have a couple of laughs, and remember that we're all just pushing for bigger and better things. But along that path, there will be friction and interruptions. It's how we respond to these anomalies and shortcomings that ultimately defines where we go from here.

Instances of Artificial Intelligence (AI) systems failing to deliver consistent, satisfactory performance are legion. We investigate why AI failures occur. We address only a narrow subset of the broader field of AI Safety. We focus on AI failures on account of flaws in conceptualization, design and deployment. Other AI Safety issues like trade-offs between privacy and security or convenience, bad actors hacking into AI systems to create mayhem or bad actors deploying AI for purposes harmful to humanity and are out of scope of our discussion. We find that AI systems fail on account of omission and commission errors in the design of the AI system, as well as upon failure to develop an appropriate interpretation of input information. Moreover, even when there is no significant flaw in the AI software, an AI system may fail because the hardware is incapable of robust performance across environments. Finally an AI system is quite likely to fail in situations where, in effect, it is called upon to deliver moral judgments -- a capability AI does not possess. We observe certain trade-offs in measures to mitigate a subset of AI failures and provide some recommendations.

Most major companies, including Google, Amazon, Microsoft, Uber, and Tesla, have had their artificial intelligence (AI) and machine learning (ML) systems tricked, evaded, or unintentially misled. Yet despite these high profile failures, most organizations' leaders are largely unaware of their own risk when creating and using AI and ML technologies. This is not entirely the fault of the businesses. An emerging solution is AI/ML-specific insurance. But who will need it and exactly what it will cover are still open questions.

Becoming data-driven and driving the AI-first strategy is the ultimate objective of most companies today as they are gearing towards a digital transformation journey. While the final results are gratifying, the journey of analytics or AI adoption can be slow. As a result, many analytics projects and startups ultimately fail to scale up or stand the test of time. In the last year, there have been several reports that suggested that a majority of data science projects will face failure. In fact, one report said that 87% of data science projects fail to move past the preliminary stages.

This is the third Synced year-end compilation of "Artificial Intelligence Failures." Despite AI's rapid growth and remarkable achievements, a review of AI failures remains necessary and meaningful. Our aim is not to downplay or mock research and development results, but rather to take a look at what went wrong with the hope we can do better next time. A leading facial-recognition system identified three-time Super Bowl champion Duron Harmon of the New England Patriots, Boston Bruins forward Brad Marchand, and 25 other New England professional athletes as criminals. Amazon's Rekognition software incorrectly matched the athletes to a database of mugshots in a test organized by the Massachusetts chapter of the American Civil Liberties Union (ACLU).

This is the third Synced year-end compilation of "Artificial Intelligence Failures." Despite AI's rapid growth and remarkable achievements, a review of AI failures remains necessary and meaningful. Our aim is not to downplay or mock research and development results, but rather to take a look at what went wrong with the hope we can do better next time. A leading facial-recognition system identified three-time Super Bowl champion Duron Harmon of the New England Patriots, Boston Bruins forward Brad Marchand, and 25 other New England professional athletes as criminals. Amazon's Rekognition software incorrectly matched the athletes to a database of mugshots in a test organized by the Massachusetts chapter of the American Civil Liberties Union (ACLU).