Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites of application, this paper posits that reconceptualizing replicability can help bridge the gap. Through a shift from model performance replicability to claim replicability, Machine Learning scientists can be held accountable for producing non-replicable claims that are prone to eliciting harm due to misuse and misinterpretation. In this paper, I make the following contributions. First, I define and distinguish two forms of replicability for ML research that can aid constructive conversations around replicability. Second, I formulate an argument for claim-replicability's advantage over model performance replicability in justifying assigning accountability to Machine Learning scientists for producing non-replicable claims and show how it enacts a sense of responsibility that is actionable. In addition, I characterize the implementation of claim replicability as more of a social project than a technical one by discussing its competing epistemological principles, practical implications on Circulating Reference, Interpretative Labor, and research communication.

Every day, in ways both small and large, more businesses are using AI to change the way they engage with customers and employees. But AI also faces mounting public skepticism. Rapid technological change and opacity surrounding how and why algorithms arrive at their recommendations feed this mistrust. So do well-publicized AI failures, such as the targeting of minority communities in social services fraud investigations or the assignment of different credit limits for men and women of similar financial backgrounds. Companies can address this trust gap in 2023 by adopting responsible AI.

Artificial Intelligence has been around for decades, however, only in the past ten years, it has deeply penetrated the enterprise layer with a wave of new applications. João Marques Lima speaks to Dr. Greg Benson, Professor of Computer Science and Chief Scientist at SnapLogic to find out how AI, and in particular, machine learning is changing businesses worldwide. The global Machine Learning (ML) market was valued at around $1.58 billion in 2017 and is expected to reach approximately $20.83 billion in 2024, growing at a CAGR of 44.06% between 2017 and 2024, according to Zion Market Research. Machine Learning is an application of artificial intelligence that enables software applications for being more precise in predicting results without being definitively programmed. Many of the artificial intelligence experts have projected their idea that by 2050 all the intellectual tasks performed by the humans can be accomplished by the artificial intelligence technology.

The widespread shortage of skilled security operations and threat intelligence resources in security operations centers (SOCs) leaves many organizations open to the increased risk of a security incident. That's because they are unable to effectively investigate all discovered, potentially malicious behaviors in their environment in a thorough and repeatable way. According to ESG, two-thirds of security professionals believe the cybersecurity skills gap has led to an increased workload for existing staff. "Since organizations don't have enough people, they simply pile more work onto those that they have," wrote ESG Senior Principal Analyst Jon Oltsik. "This leads to human error, misalignment of tasks to skills, and employee burnout."

Despite innovations that make it easier for seniors to keep living on their own rather than moving into special facilities, most elderly people eventually need a hand with chores and other everyday activities. Friends and relatives often can't do all the work. Growing evidence indicates it's neither sustainable nor healthy for seniors or their loved ones. Yet demand for professional caregivers already far outstrips supply, and experts say this workforce shortage will only get worse. Just as automation has begun to do jobs previously seen as uniquely suited for humans, like retrieving goods from warehouses, robots will assist your elderly relatives.

Mark Zuckerberg's 2016 personal goal is to build an Iron Man-style AI. Eric Schmidt has been musing about a future with "Eric" and "Not-Eric," in which Eric is himself and Not-Eric is "this digital thing that helps me." Phil Libin has been calling AI bots the most important tech trend of the year. I'm pretty excited about AI too. But it's hard to square this enthusiasm with the present-day performance of machine intelligence.