There is increasing evidence of the business benefits of responsible AI (RAI), when companies mitigate risks through training and testing data, measuring model bias and accuracy, and model documentation. Companies that adopt responsible AI experience higher returns on their AI investment. Raj Shekhar writes that business leaders globally must coalesce around the imperative to develop rigorous, consistent standards for responsible AI adoption. Much has been spoken and written about the risks to public trust and safety arising from the adoption of artificial intelligence (AI)-based applications across multiple sectors. In finance, the use of AI has led to discriminatory credit decisions.

Artificial intelligence-based tools continue to be used by only a very small percentage of law firms, according to the ABA's 2020 Legal Technology Survey Report this month. Just 7% of respondents to the ABA Legal Technology Resource Center's survey reported that their firms use AI tech tools, a decrease of one percentage point from a year ago. Meanwhile, 23% of respondents said their firms were not interested in purchasing AI-based tools and nearly 34% said they did not know enough about AI to answer the question regarding their firms current or planned usage of such tools. Alexander Paykin, a Legal Technology Resource Center board member, says he thinks the legal industry has been slow to adopt AI-based tools because the available products have yet to demonstrate they can consistently produce the results vendors promise. He points to his experience with the AI-based legal research offerings he has tried out in recent years to back up his point.

A lot of organizations seek to engage closely with ML developers, either to increasing product adoption or crowdsource innovation. But a lot of these efforts fall into the trap of "seen-it-done-it-all" trap, where organizations employ the same strategies to engage them which they have utilized for other developers. Machine Learning developers have unique needs from the ecosystem. They face challenges that developers from another stream are largely insulated from. Firstly, ML is a fast-changing domain.

With all the buzz in the information technology industry around artificial intelligence (AI) and machine learning (ML) you'd think that every organization was using these tools or planning for how they are going to use them. After all, the promise is that AI and ML will help organizations harness the ever-growing volumes of data being generated by automating and augmenting human analytic processes and decision-making.

A new survey provides some insight into how hospital and health system leaders are prioritizing healthcare technology investments for next year with strong indications that healthcare leaders are focused on investing in proven technology solutions that will have an immediate impact, and are proceeding cautiously with emerging technology like artificial intelligence (AI). The survey, conducted by the Pittsburgh-based Center for Connected Medicine (CCM) in partnership with the Health Management Academy, reflects the opinions of healthcare C-suite leaders from 20 major U.S. health systems across the country. CCM is a collaborative health care executive briefing center and is operated by five partners -- GE Healthcare, IBM, Lenovo Health, Nokia, and UPMC. The Alexandra, Va.-based Health Management Academy is a membership organization consisting of executives from the country's top 100 health systems focused on sharing best practices. CCM and the Academy conducted a quantitative survey of IT leaders, specifically CIOs, chief medical information officers (CMIOs) and chief nursing information officers (CNIOs) at about 25 leading health systems, followed by quantitative interviews with health system CIOs, CFOs and CEOs at 20 health systems about health IT trends for 2018 and how these trends fit into the overall strategy and priorities of their health systems.