Oct 23, 2020

Select Page

News presented free of algorithm

Post image

The Best of This Week

Gaining an Edge With a Data Science Bridge Strain between business operations and data science teams did not originate with the rise of data science. These tensions demand structural solutions, and the way forward is through a data science bridge — an organizational structure and leadership commitment to develop better communication, processes, and trust among all stakeholders. Position Your Business to Win Tech Talent A new analysis pinpoints seven key “clusters of need” in the tech talent capabilities that will matter most in the next few years, including data management, automation, and DevOps. To close the talent gap in these areas, […]Read full article >
Post image

To Succeed With Data Science, First Build the ‘Bridge’

Much has been written about the high failure rate of data science projects. Data science teams often have difficulty moving their insights and algorithms into business processes. At the same time, business teams often can’t articulate the problems they need solved. And they ignore their cultural resistance to data science. There is a designed-in structural tension between business and data science teams that needs to be recognized and addressed. Structural problems demand structural solutions, and we see a way forward through a data science bridge: an organizational structure and leadership commitment to develop better communication, […]Read full article >
Post image

Putting Responsible AI Into Practice

As awareness grows regarding the risks associated with deploying AI systems that violate legal, ethical, or cultural norms, building responsible AI and machine learning technology has become a paramount concern in organizations across all sectors. Individuals tasked with leading responsible-AI efforts are shifting their focus from establishing high-level principles and guidance to managing the system-level change that is necessary to make responsible AI a reality. Ethics frameworks and principles abound. AlgorithmWatch maintains a repository of more than 150 ethical guidelines. A meta-analysis of a half-dozen prominent guidelines identified […]Read full article >
Post image

History’s Lessons on Competitive Innovation

We might assume that history has little to teach us about navigating our current situation of technological disruption — Industry 4.0, the internet of things, 5G, AI, machine learning, genomics, robotics — as it intersects with societal and economic upheaval. In fact, the past does offer important lessons, albeit from a surprising source. The 20th century was cursed with two world wars, showcasing the worst of people’s brutality, for sure, and also the best of their courage, selflessness, and perseverance. In concentrating on character traits, we risk missing meaningful lessons about how to innovate and invent in periods of stress and […]Read full article >
Post image

Why Your Board Needs a Plan for AI Oversight

We can safely defer the discussion about whether artificial intelligence will eventually take over board functions. We cannot, however, defer the discussion about how boards will oversee AI — a discussion that’s relevant whether organizations are developing AI systems or buying AI-powered software. With the technology in increasingly widespread use, it’s time for every board to develop a proactive approach for overseeing how AI operates within the context of an organization’s overall mission and risk management. According to McKinsey’s 2019 global AI survey, although AI adoption is increasing rapidly, overseeing and mitigating its risks […]Read full article >