Miliaku Nwabueze, a senior program manager at Code for Science & Society, had been concerned for some time about the role of technology in state violence. Then, on 7 October of last year, Hamas entered Israel, killing and kidnapping about 1,400 people. Less than a week later, as Israel ordered 1.1 million Palestinians out of northern Gaza in the onset of its deadly retaliation, Nwabueze decided to write a message to her colleagues on the US-based non-profit organization's Slack channel. "Hey y'all … I have been watching multiple genocides around the world," she began, naming Palestine as well as Sudan, the Congo and Artsakh. "All of these have heavy linkages to the tech industry." The 30-year-old went on to assert that CS&S – whose stated mission is to "advance the power of data to improve the social and economic lives of all people" – should say, at the minimum, "we support demands for a ceasefire" in Gaza.

Automated decision-making systems are being increasingly deployed and affect the public in a multitude of positive and negative ways. Governmental and private institutions use these systems to process information according to certain human-devised rules in order to address social problems or organizational challenges. Both research and real-world experience indicate that the public lacks trust in automated decision-making systems and the institutions that deploy them. The recreancy theorem argues that the public is more likely to trust and support decisions made or influenced by automated decision-making systems if the institutions that administer them meet their fiduciary responsibility. However, often the public is never informed of how these systems operate and resultant institutional decisions are made. A ``black box'' effect of automated decision-making systems reduces the public's perceptions of integrity and trustworthiness. The result is that the public loses the capacity to identify, challenge, and rectify unfairness or the costs associated with the loss of public goods or benefits. The current position paper defines and explains the role of fiduciary responsibility within an automated decision-making system. We formulate an automated decision-making system as a data science lifecycle (DSL) and examine the implications of fiduciary responsibility within the context of the DSL. Fiduciary responsibility within DSLs provides a methodology for addressing the public's lack of trust in automated decision-making systems and the institutions that employ them to make decisions affecting the public. We posit that fiduciary responsibility manifests in several contexts of a DSL, each of which requires its own mitigation of sources of mistrust. To instantiate fiduciary responsibility, a Los Angeles Police Department (LAPD) predictive policing case study is examined.

The Bay Area is famed for nurturing speculative investments like flying cars, floating cities, and the notion that a ride hailing service can turn a profit. A new utopian investment opportunity arrived Monday: Shovel dollars into a San Francisco artificial intelligence lab cofounded by Elon Musk and you'll receive a share of the profits when (or if) it figures out how to create machines smarter than humans. That pitch comes from OpenAI, an independent AI research lab cofounded as a nonprofit in 2015 by Musk and Sam Altman, the president of startup incubator YCombinator. Its stated mission was to safely create software as capable as people, which it terms artificial general intelligence or AGI, and share the benefits with the world. The founders argued society shouldn't have to hope that profit-seeking tech giants would do that.

For the first time in many years, US companies clearly face a real change in sentiment and political risk in sending or keeping work offshore. It's clear that the new Trump administration combined with other secular forces – Brexit and the U.S. presidential campaign rhetoric, for example – signals a step back and pause in globalization. I think it's reasonable to believe that, to a larger extent than before, all directors in companies will be pressured to not use labor arbitrage and potentially pressured to repatriate work that has already been moved offshore. The problem this poses for companies is that executives have a fiduciary responsibility to their company. So far, president-elect Trump has gone after high-profile consumer goods companies and manufacturing companies.