As organizations roll out machine learning and AI models into production, they're increasing cognizant of the presence of bias in their systems. Not only does this bias potentially lead to poorer decisions on the part of the AI systems, but it can put the organizations running them in legal jeopardy. However, getting on top of this problem is turning out to be tougher than expected for a lot of organizations. For example, Harvard University and Accenture demonstrated how algorithmic bias can creep into the hiring processes at human resources departments in a report issued last year. In their 2021 joint report "Hidden Workers: Untapped Talent," the two organizations show how the combination of outdated job descriptions and automated hiring systems that leans heavily on algorithmic processes for posting of ads for open job and evaluation of resumes can keep otherwise qualified individuals from landing jobs.
London – May 23, 2018 – DataRobot, a pioneer in automated machine learning, announced today the release of a new version of the DataRobot Cloud platform for the European Union (EU). The DataRobot EU Cloud, available on the Amazon Web Services (AWS) EU (Ireland) Region, is able to support the unique needs of EU customers, whose data is tightly regulated, as well as help enterprises conform to the upcoming General Data Protection Regulation (GDPR) requirements. With growing sensitivities over data privacy and breaches, the need for organisations to keep and process customer data at the point of origin is vital. With the introduction of GDPR and its 25 May 2018 enforcement date, organisations are also required to develop a strategy to prove compliance or face hefty fines. The DataRobot EU Cloud ensures European organisations adhere to enhanced data sovereignty requirements without sacrificing any enterprise-grade capabilities.
Enterprise AI service provider DataRobot has unveiled MLOps, a machine learning operations (MLOps) solution for deploying, monitoring, and managing machine learning models across the enterprise. MLOps combines DataRobot's existing model management and monitoring solution with capabilities from MLOps category leader ParallelM, which DataRobot acquired in June. DataRobot's new MLOps offering provides a centralised hub for deployment, monitoring, and governance of models created from a variety of tools. As a result, organisations will be able to cut the time it takes them to deploy and scale machine learning-based services in production. Despite the investments in data science teams and infrastructure, many companies have not been able to derive measurable value from AI projects.
As artificial intelligence (AI) continues its march into enterprises, many IT pros are beginning to express concern about potential AI bias in the systems they use. A new report from DataRobot finds that nearly half (42%) of AI professionals in the US and UK are "very" to "extremely" concerned about AI bias. The report, conducted last June of more than 350 US- and UK-based CIOs, CTOs, VPs, and IT managers involved in AI and machine learning (ML) purchasing decisions, also found that "compromised brand reputation" and "loss of customer trust" are the most concerning repercussions of AI bias. This prompted 93% of respondents to say they plan to invest more in AI bias prevention initiatives in the next 12 months. SEE: The ethical challenges of AI: A leader's guide (free PDF) (TechRepublic) Despite the fact that many organizations see AI as a game changer, many organizations are still using untrustworthy AI systems, said Ted Kwartler, vice president of trusted AI, at DataRobot.
This article is brought to you thanks to the collaboration of The European Sting with the World Economic Forum. The current conversation around AI, ethics and the benefits for our global community is a heated one. The combination of high stakes and a complex, rapidly-adopted technology has created a very real state of urgency and intensity around this discussion. Promoters of the technology love to position AI as a welcome disruptor that could bring about a global revolution. It's all too easy to get caught up in the hype and create a situation whereby the world does not fully benefit from the development of AI technology.